From d9b0e1436b01eb9491d05fe7c3d4b566cd7a7588 Mon Sep 17 00:00:00 2001 From: Ale3e Date: Mon, 3 Mar 2025 10:19:20 +0100 Subject: [PATCH 01/70] First lab --- AI/Academy/1-Intro-To-ML-And-MLOps/lab.md | 258 ++++++++++++++++++ .../src/solution.ipynb | 230 ++++++++++++++++ 2 files changed, 488 insertions(+) create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/lab.md create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md new file mode 100644 index 0000000..357fafb --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md @@ -0,0 +1,258 @@ +# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow + +In questo laboratorio, affrontiamo un percorso step-by-step: + +1. **Caricare e preparare un dataset meteo** (con dati di temperatura e umidità) per prevedere se pioverà o meno. +2. **Addestrare un modello di Machine Learning** con Scikit-learn. +3. **Valutare il modello** e comprendere i risultati. +4. **Estendere il progetto** integrando **MLflow** per tracciare metriche, parametri e versioni del modello. + +La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow. + +--- + +## Parte 1: Dai dati al modello ML (Supervised Learning con Scikit-learn) + +### Obiettivo +- Creare un semplice **modello di classificazione** per predire la pioggia basandoci su dati di temperatura e umidità. + +### 1. Preparazione del Dataset + +Per semplicità, qui useremo un dataset di esempio: +[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv) + +```python +import pandas as pd + +# URL del dataset (sostituiscilo con il tuo link) +url = "https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv" + +# Carica il dataset +df = pd.read_csv(url) + +# Mostra le prime righe +print(df.head()) +``` + +--- + +### 2. Esplorazione e pulizia dei Dati +1. Controlliamo se ci sono valori mancanti +2. Trasformiamo la colonna `Label` in una variabile numerica (0 = NoRain, 1 = Rain) +3. Selezione delle feature + + +```python +# 1. Rimozione dei valori mancanti +df = df.dropna() + +# 2. Conversione della colonna 'RainToday' in valori numerici +df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0) + +# 3. Selezione delle feature +features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am'] + +X = df[features] +y = df['RainToday'] +``` +--- +### 3. Suddivisione del Dataset in Training e Test + +Prima di addestrare il modello, dividiamo il dataset in: +- **X** (features) → Temperatura, Umidità +- **y** (target) → Label (Rain/NoRain) + +```python +from sklearn.model_selection import train_test_split + +# Suddivisione dei dati (80% training, 20% testing) +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +``` + +--- +### 4. Creazione e Addestramento del Modello + +Utilizziamo un classificatore semplice, ad esempio **RandomForestClassifier**. + +```python +from sklearn.ensemble import RandomForestClassifier + +# Creazione e addestramento del modello +model = RandomForestClassifier(n_estimators=100, random_state=42) +model.fit(X_train, y_train) + +``` + +--- + +### 5. Valutazione del Modello + +Calcoliamo l’accuratezza e visualizziamo la matrice di confusione. + +```python +from sklearn.metrics import accuracy_score, classification_report + +# Predizioni sul set di test +y_pred = model.predict(X_test) + +# Calcolo dell'accuratezza +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuratezza del modello: {accuracy:.2f}") + +# Report di classificazione +print(classification_report(y_test, y_pred)) +``` + +--- + +### Conclusione (Parte 1) +In questa prima parte, abbiamo: +1. Caricato un dataset relativo alle condizioni meteo +2. Pulito i dati e trasformato la variabile target +3. Suddiviso il dataset in training e test +4. Creato un modello di classificazione con RandomForest +5. Valutato le prestazioni con accuratezza e matrice di confusione + +--- +## Tech: Installazione e Configurazione di MLflow in Locale con Docker + +### Obiettivo +- Configurare un'istanza MLflow in locale per registrare esperimenti. + +### 1. Avviare MLflow con Docker + +Esegui il seguente comando per avviare un'istanza locale di MLflow: + +```bash +docker pull ghcr.io/mlflow/mlflow + +docker run -p 5001:5000 --name mlflow-server \ + -v $(pwd)/mlruns:/mlruns \ + ghcr.io/mlflow/mlflow:latest server \ + --backend-store-uri sqlite:///mlflow.db \ + --default-artifact-root /mlruns \ + --host 0.0.0.0 +``` + +- **`-p 5001:5000`**: Espone MLflow sulla porta 5001 +- **`-v $(pwd)/mlruns:/mlruns`**: Monta la cartella locale per salvare gli esperimenti +- **`--backend-store-uri sqlite:///mlflow.db`**: Utilizza un database SQLite per il tracking + +Una volta avviato, MLflow sarà disponibile all’indirizzo: + +``` +http://127.0.0.1:5001 +``` + +Ora possiamo passare alla terza parte e integrare MLflow nel nostro codice! + +--- +## Parte 2: Integrazione con MLflow + +Adesso, estendiamo il codice esistente per **tracciare i nostri esperimenti** con **MLflow**. Questo ci permetterà di: +- Loggare i nostri parametri (es. `n_estimators`) +- Salvare l’accuratezza e altre metriche +- Salvare il modello e ricaricarlo facilmente + +### Obiettivo +- Integrare MLflow nel codice esistente per versionare e tracciare le metriche del modello. + +### 1. Installazione e Setup MLflow + +Installa MLflow: + +```bash +pip install mlflow +``` + +```python +import mlflow +import mlflow.sklearn + +# Impostiamo un nome per l'esperimento +mlflow.set_tracking_uri("http://127.0.0.1:5001") +mlflow.set_experiment("weather_classification_experiment") +``` + +--- + +### 2. Logging dei Parametri e Metriche + +Possiamo loggare: +- Parametri → `n_estimators` e altre impostazioni +- Metriche → accuratezza +- Modello → versione del modello addestrato + + +```python +# Usando il modello creato nella Parte 1 +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score + +n_estimators = 100 + +# Creiamo una nuova run MLflow +with mlflow.start_run(): + # Log Param + mlflow.log_param("n_estimators", n_estimators) + + # Creazione del modello + rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42) + rf_model.fit(X_train, y_train) + + # Calcolo metriche + y_pred = rf_model.predict(X_test) + accuracy = accuracy_score(y_test, y_pred) + mlflow.log_metric("accuracy", accuracy) + + # Salviamo il modello + mlflow.sklearn.log_model(rf_model, "random_forest_model") + + print(f"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}") +``` + +--- + +### 3. Visualizzare e Confrontare i Risultati + +MLflow fornisce un’interfaccia web per esplorare tutte le run ed esperimenti. + +```bash +http://127.0.0.1:5001 +``` + +Accedi all’URL indicato nel terminale. Nella sezione **Experiments** potrai vedere: +- Parametri loggati +- Metriche +- Modelli salvati + +### 4. Caricare un Modello Salvato con MLflow + +Possiamo anche recuperare un modello da MLflow per fare previsioni future. + +```python +import mlflow.sklearn +from sklearn.metrics import accuracy_score + +# Inserisci un run_id reale che trovi nell'interfaccia MLflow +RUN_ID = "" + +loaded_model = mlflow.sklearn.load_model(f"runs:/{RUN_ID}/random_forest_model") + +# Verifichiamo l'accuratezza +y_loaded_pred = loaded_model.predict(X_test) +acc_loaded = accuracy_score(y_test, y_loaded_pred) +print(f"Accuratezza del modello caricato: {acc_loaded:.2f}") +``` + +## Conclusioni +In questo laboratorio abbiamo: +1. **Creato un modello di classificazione con Scikit-learn** su un dataset meteo. +2. **Aggiunto MLflow** per tracciare parametri, metriche e gestire le versioni del modello. +3. **Visualizzato i risultati** con l’interfaccia MLflow UI e ricaricato il modello salvato. + + **Prossimi Passi** +- Provare iperparametri diversi (`n_estimators`, `max_depth`, ecc.) e confrontare i risultati su MLflow. +- Integrare un sistema di **continuous integration/continuous deployment (CI/CD)** per distribuire automaticamente il modello. +- Monitorare in produzione il drift del modello (eventuali cali di accuratezza). diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb new file mode 100644 index 0000000..b1584fc --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuratezza del modello: 0.82\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.93 0.89 2649\n", + " 1 0.61 0.42 0.50 721\n", + "\n", + " accuracy 0.82 3370\n", + " macro avg 0.73 0.67 0.69 3370\n", + "weighted avg 0.80 0.82 0.81 3370\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# URL del dataset (sostituiscilo con il tuo link)\n", + "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", + "\n", + "# Carica il dataset\n", + "df = pd.read_csv(url)\n", + "\n", + "\n", + "# 1. Rimozione dei valori mancanti\n", + "df = df.dropna()\n", + "\n", + "# 2. Conversione della colonna 'Rain' in valori numerici\n", + "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", + "\n", + "# 3. Selezione delle caratteristiche e della variabile target\n", + "features = ['MinTemp', 'MaxTemp', 'Humidity9am', 'Humidity3pm']\n", + "X = df[features]\n", + "y = df['RainToday']\n", + "\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Suddivisione dei dati (80% training, 20% testing)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Creazione e addestramento del modello\n", + "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "model.fit(X_train, y_train)\n", + "\n", + "from sklearn.metrics import accuracy_score, classification_report\n", + "\n", + "# Predizioni sul set di test\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Calcolo dell'accuratezza\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", + "\n", + "# Report di classificazione\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "# Calcolare la matrice di confusione\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Creiamo una heatmap con Seaborn\n", + "plt.figure(figsize=(6, 4))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Rain\", \"Rain\"], yticklabels=[\"No Rain\", \"Rain\"])\n", + "\n", + "# Aggiungiamo i titoli\n", + "plt.xlabel(\"Predicted Label\")\n", + "plt.ylabel(\"True Label\")\n", + "plt.title(\"Confusion Matrix\")\n", + "\n", + "# Mostriamo il grafico\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025/02/25 15:17:44 INFO mlflow.tracking.fluent: Experiment with name 'weather_classification_experiment' does not exist. Creating a new experiment.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import mlflow\n", + "import mlflow.sklearn\n", + "\n", + "# Impostiamo un nome per l'esperimento\n", + "mlflow.set_tracking_uri(\"http://127.0.0.1:5001\")\n", + "mlflow.set_experiment(\"weather_classification_experiment\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025/02/25 15:17:52 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n", + "2025/02/25 15:17:52 INFO mlflow.tracking._tracking_service.client: 🏃 View run shivering-yak-384 at: http://127.0.0.1:5001/#/experiments/1/runs/e1a69ed25ddc49609674c7df7375d642.\n", + "2025/02/25 15:17:52 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5001/#/experiments/1.\n" + ] + }, + { + "ename": "OSError", + "evalue": "[Errno 30] Read-only file system: '/mlruns'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 24\u001b[0m\n\u001b[1;32m 21\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m, accuracy)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Salviamo il modello\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msklearn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrf_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrandom_forest_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEsperimento concluso. Accuratezza registrata: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maccuracy\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/sklearn/__init__.py:413\u001b[0m, in \u001b[0;36mlog_model\u001b[0;34m(sk_model, artifact_path, conda_env, code_paths, serialization_format, registered_model_name, signature, input_example, await_registration_for, pip_requirements, extra_pip_requirements, pyfunc_predict_fn, metadata)\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;129m@format_docstring\u001b[39m(LOG_MODEL_PARAM_DOCS\u001b[38;5;241m.\u001b[39mformat(package_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscikit-learn\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_model\u001b[39m(\n\u001b[1;32m 336\u001b[0m sk_model,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 348\u001b[0m metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 349\u001b[0m ):\n\u001b[1;32m 350\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 351\u001b[0m \u001b[38;5;124;03m Log a scikit-learn model as an MLflow artifact for the current run. Produces an MLflow Model\u001b[39;00m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;124;03m containing the following flavors:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 411\u001b[0m \n\u001b[1;32m 412\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43martifact_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[43mflavor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msklearn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43msk_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msk_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mconda_env\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconda_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mcode_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcode_paths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43mserialization_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserialization_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m \u001b[49m\u001b[43mregistered_model_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mregistered_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 421\u001b[0m \u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 422\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_example\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_example\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[43m \u001b[49m\u001b[43mawait_registration_for\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mawait_registration_for\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 424\u001b[0m \u001b[43m \u001b[49m\u001b[43mpip_requirements\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpip_requirements\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 425\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_pip_requirements\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_pip_requirements\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 426\u001b[0m \u001b[43m \u001b[49m\u001b[43mpyfunc_predict_fn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpyfunc_predict_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 427\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 428\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/models/model.py:744\u001b[0m, in \u001b[0;36mModel.log\u001b[0;34m(cls, artifact_path, flavor, registered_model_name, await_registration_for, metadata, run_id, resources, **kwargs)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m tracking_uri \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabricks\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m get_uri_scheme(tracking_uri) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabricks\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 743\u001b[0m _logger\u001b[38;5;241m.\u001b[39mwarning(_LOG_MODEL_MISSING_SIGNATURE_WARNING)\n\u001b[0;32m--> 744\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtracking\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfluent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlflow_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43martifact_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 746\u001b[0m \u001b[38;5;66;03m# if the model_config kwarg is passed in, then log the model config as an params\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_config \u001b[38;5;241m:=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_config\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/fluent.py:1170\u001b[0m, in \u001b[0;36mlog_artifacts\u001b[0;34m(local_dir, artifact_path, run_id)\u001b[0m\n\u001b[1;32m 1136\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;124;03mLog all the contents of a local directory as artifacts of the run. If no run is active,\u001b[39;00m\n\u001b[1;32m 1138\u001b[0m \u001b[38;5;124;03mthis method will create a new active run.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1167\u001b[0m \u001b[38;5;124;03m mlflow.log_artifacts(tmp_dir, artifact_path=\"states\")\u001b[39;00m\n\u001b[1;32m 1168\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1169\u001b[0m run_id \u001b[38;5;241m=\u001b[39m run_id \u001b[38;5;129;01mor\u001b[39;00m _get_or_start_run()\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mrun_id\n\u001b[0;32m-> 1170\u001b[0m \u001b[43mMlflowClient\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/client.py:1977\u001b[0m, in \u001b[0;36mMlflowClient.log_artifacts\u001b[0;34m(self, run_id, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 1930\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_artifacts\u001b[39m(\n\u001b[1;32m 1931\u001b[0m \u001b[38;5;28mself\u001b[39m, run_id: \u001b[38;5;28mstr\u001b[39m, local_dir: \u001b[38;5;28mstr\u001b[39m, artifact_path: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1932\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Write a directory of files to the remote ``artifact_uri``.\u001b[39;00m\n\u001b[1;32m 1934\u001b[0m \n\u001b[1;32m 1935\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1975\u001b[0m \n\u001b[1;32m 1976\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1977\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tracking_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/_tracking_service/client.py:874\u001b[0m, in \u001b[0;36mTrackingServiceClient.log_artifacts\u001b[0;34m(self, run_id, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_artifacts\u001b[39m(\u001b[38;5;28mself\u001b[39m, run_id, local_dir, artifact_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Write a directory of files to the remote ``artifact_uri``.\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \n\u001b[1;32m 868\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 872\u001b[0m \n\u001b[1;32m 873\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 874\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_artifact_repo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/store/artifact/local_artifact_repo.py:66\u001b[0m, in \u001b[0;36mLocalArtifactRepository.log_artifacts\u001b[0;34m(self, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 62\u001b[0m artifact_dir \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 63\u001b[0m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39martifact_dir, artifact_path) \u001b[38;5;28;01mif\u001b[39;00m artifact_path \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39martifact_dir\n\u001b[1;32m 64\u001b[0m )\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(artifact_dir):\n\u001b[0;32m---> 66\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43martifact_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m shutil\u001b[38;5;241m.\u001b[39mcopytree(src\u001b[38;5;241m=\u001b[39mlocal_dir, dst\u001b[38;5;241m=\u001b[39martifact_dir, dirs_exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/utils/file_utils.py:211\u001b[0m, in \u001b[0;36mmkdir\u001b[0;34m(root, name)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m!=\u001b[39m errno\u001b[38;5;241m.\u001b[39mEEXIST \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(target):\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m target\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/utils/file_utils.py:208\u001b[0m, in \u001b[0;36mmkdir\u001b[0;34m(root, name)\u001b[0m\n\u001b[1;32m 206\u001b[0m target \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(root, name) \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m root\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m!=\u001b[39m errno\u001b[38;5;241m.\u001b[39mEEXIST \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(target):\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + " \u001b[0;31m[... skipping similar frames: makedirs at line 213 (1 times)]\u001b[0m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:223\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\n", + "\u001b[0;31mOSError\u001b[0m: [Errno 30] Read-only file system: '/mlruns'" + ] + } + ], + "source": [ + "# Usando il modello creato nella Parte 1\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "n_estimators = 100\n", + "\n", + "# Creiamo una nuova run MLflow\n", + "with mlflow.start_run():\n", + " # Log Param\n", + " mlflow.log_param(\"n_estimators\", n_estimators)\n", + "\n", + " # Creazione del modello\n", + " rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n", + " rf_model.fit(X_train, y_train)\n", + "\n", + " # Calcolo metriche\n", + " y_pred = rf_model.predict(X_test)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + " \n", + " # Salviamo il modello\n", + " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", + "\n", + " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlflow_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 5587afd0a2505e2c020d8082e5bff4e30bc903f1 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Mar 2025 14:59:27 +0100 Subject: [PATCH 02/70] add lab python and refactor solution --- AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore | 178 ++++++ AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 530 ++++++++++++++++++ AI/Academy/1-Intro-To-ML-And-MLOps/lab.md | 258 --------- .../src/requirements.txt | 153 +++++ .../src/solution.ipynb | 152 ++--- 5 files changed, 899 insertions(+), 372 deletions(-) create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb delete mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/lab.md create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore b/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore new file mode 100644 index 0000000..5dd41ee --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore @@ -0,0 +1,178 @@ +# local mlflow +mlartifacts/* +mlruns/* + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc \ No newline at end of file diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb new file mode 100644 index 0000000..65cae9f --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -0,0 +1,530 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow\n", + "\n", + "In questo laboratorio, affrontiamo un percorso step-by-step:\n", + "\n", + "1. **Caricare e preparare un dataset meteo** (con dati di temperatura e umidità) per prevedere se pioverà o meno.\n", + "2. **Addestrare un modello di Machine Learning** con Scikit-learn.\n", + "3. **Valutare il modello** e comprendere i risultati.\n", + "4. **Estendere il progetto** integrando **MLflow** per tracciare metriche, parametri e versioni del modello.\n", + "\n", + "La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parte 0: Setup dell'Environment per Python\n", + "\n", + "#### **Requisiti**\n", + "\n", + "Questo laboratorio assume che Python sia già installato, la repository [kiratech/labs](https://github.com/kiratech/labs.git) sia accessibile e che Git sia configurato correttamente sul computer locale.\n", + "\n", + "### 1. Clonare la Repository\n", + "Per iniziare, clona la repository del laboratorio eseguendo il seguente comando:\n", + "\n", + "```sh\n", + "git clone https://github.com/kiratech/labs.git\n", + "```\n", + "\n", + "### 2. Fare il Checkout del Branch Lab\n", + "\n", + "Dopo aver clonato la repository, entra nella cartella del progetto:\n", + "\n", + "```sh\n", + "cd labs\n", + "```\n", + "\n", + "Quindi, fai il checkout del branch `lab`:\n", + "\n", + "```sh\n", + "git checkout academy-ai\n", + "```\n", + "In questa cartella sono presenti le risorse relative ai laboratori con tema AI.\n", + "\n", + "### 3. Spostarsi nella Cartella 1-Intro-To-ML-And-MLOps\n", + "\n", + "Naviga nella cartella del primo laboratorio:\n", + "\n", + "```sh\n", + "cd Academy/1-Intro-To-ML-And-MLOps\n", + "```\n", + "\n", + "### 4. Creare un Virtual Environment\n", + "\n", + "Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema.\n", + "\n", + "Crea un ambiente virtuale:\n", + "\n", + "```sh\n", + "python -m venv venv\n", + "```\n", + "\n", + "Attiva l'Ambiente Virtuale\n", + "\n", + "- **Su macOS e Linux:**\n", + " \n", + " ```sh\n", + " source venv/bin/activate\n", + " ```\n", + " \n", + "- **Su Windows:**\n", + " \n", + " ```sh\n", + " venv\\Scripts\\activate\n", + " ```\n", + "\n", + "Dovresti vedere il prefisso `(venv)` nel terminale, indicando che l'ambiente virtuale è attivo.\n", + "\n", + "### 5. Installare i pacchetti da `requirements.txt`\n", + "\n", + "Tramite il file `requirements.txt`, possiamo installare tutte le dipendenze di questo laboratorio, in particolare **scikit-learn** e **MLflow**, eseguendo il comando:\n", + "\n", + "```sh\n", + "pip install -r src/requirements.txt\n", + "```\n", + "\n", + "Verifica che i pacchetti siano stati installati correttamente con:\n", + "\n", + "```sh\n", + "pip list\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parte 1: Dai dati al modello ML (Supervised Learning con Scikit-learn)\n", + "\n", + "**Obiettivo**\n", + "- Creare un semplice **modello di classificazione** per predire la pioggia basandoci su dati di temperatura e umidità.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Preparazione del Dataset\n", + "\n", + "Per semplicità, qui useremo un dataset di esempio:\n", + "[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# URL del dataset (sostituiscilo con il tuo link)\n", + "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", + "\n", + "# Carica il dataset\n", + "df = pd.read_csv(url)\n", + "\n", + "# Mostra le prime righe\n", + "df.head(5)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Esplorazione e pulizia dei Dati\n", + "1. Controlliamo se ci sono valori mancanti\n", + "2. Trasformiamo la colonna `Label` in una variabile binaria (0 = NoRain, 1 = Rain)\n", + "3. Selezione delle feature " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Rimozione dei valori mancanti\n", + "df = df.dropna()\n", + "\n", + "# 2. Conversione della colonna 'RainToday' in valori numerici\n", + "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", + "\n", + "# 3. Selezione delle feature \n", + "features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am']\n", + "\n", + "X = df[features]\n", + "y = df['RainToday']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Suddivisione del Dataset in Training e Test\n", + "\n", + "Prima di addestrare il modello, dividiamo il dataset in:\n", + "- **X** (features) → Temperatura, Umidità\n", + "- **y** (target) → Label (Rain/NoRain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Suddivisione dei dati (80% training, 20% testing)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "print(f\"Dimensioni del dataset di training: {len(X_train)}\")\n", + "print(f\"Dimensioni del dataset di test: {len(X_test)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Creazione e Addestramento del Modello\n", + "\n", + "Utilizziamo un classificatore semplice, ad esempio [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Creazione e addestramento del modello\n", + "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Valutazione del Modello\n", + "\n", + "Calcoliamo l’accuratezza del modello e altre metriche. Inoltre, visualizziamo la matrice di confusione ovvero un metodo visivo per valutare le prestazioni di un modello." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score, classification_report\n", + "\n", + "# Predizioni sul set di test\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Calcolo dell'accuratezza\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", + "\n", + "# Report di classificazione\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "# Creiamo una heatmap con Seaborn\n", + "target_names = [\"No Rain\", \"Rain\"]\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(6, 4))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", + "\n", + "# Aggiungiamo i titoli\n", + "plt.xlabel(\"Predicted Label\")\n", + "plt.ylabel(\"True Label\")\n", + "plt.title(\"Confusion Matrix\")\n", + "\n", + "# Mostriamo il grafico\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusione (Parte 1)\n", + "In questa prima parte, abbiamo:\n", + "1. Caricato un dataset relativo alle condizioni meteo\n", + "2. Pulito i dati e trasformato la variabile target\n", + "3. Suddiviso il dataset in training e test\n", + "4. Creato un modello di classificazione con RandomForest\n", + "5. Valutato le prestazioni con accuratezza e matrice di confusione" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tech: Installazione e Configurazione di MLflow\n", + "### Obiettivo\n", + "- Configurare un'istanza MLflow in locale per registrare esperimenti.\n", + "### 1. Avviare MLflow con Docker\n", + "\n", + "Esegui il seguente comando per avviare un'istanza locale di MLflow:\n", + "```bash\n", + "mlflow ui\n", + "```\n", + "In questo modo MLflow espone l'interfaccia grafica di MLflow all'indirizzo:\n", + "```\n", + "http://127.0.0.1:5000\n", + "```\n", + "Questa configurazione salva artefatti ed esperimenti in locale. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MLflow in Produzione\n", + "Nel contesto produttivo e presso i clienti, non si esegue MLflow in locale. \n", + "Generalmente si scelgono soluzioni che permettono un'architettura più robusta e scalabile, evitando problemi di gestione manuale degli esperimenti e della persistenza dei dati.\n", + "Le soluzioni comuni includono:\n", + "- **Docker Compose**: Un file docker-compose.yml viene utilizzato per avviare MLflow con un database backend e uno storage remoto. \n", + "Una soluzione basata su questo metodo può essere trovata nella repository interna [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main).\n", + "- **Kubernetes**: MLflow viene distribuito su un cluster Kubernetes, garantendo scalabilità e gestione centralizzata. \n", + "\n", + "Entrambe le opzioni si basano su una soluzione multi-container dove sono presenti un database persistente e un storage S3, in genere MinIO o soluzioni cloud. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parte 2: Integrazione con MLflow\n", + "\n", + "Adesso, estendiamo il codice esistente per **tracciare i nostri esperimenti** con **MLflow**. Questo ci permetterà di:\n", + "- Loggare i nostri parametri (es. `n_estimators`)\n", + "- Salvare l’accuratezza e altre metriche\n", + "- Salvare il modello e ricaricarlo facilmente\n", + "\n", + "### Obiettivo\n", + "- Integrare MLflow nel codice esistente per versionare e tracciare le metriche del modello.\n", + "\n", + "### 1. Setup MLflow\n", + "Impostiamo le variabili di MLflow per questo esperimento:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow\n", + "import mlflow.sklearn\n", + "\n", + "# Impostiamo un nome per l'esperimento\n", + "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n", + "mlflow.set_experiment(\"weather_classification_experiment\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Verifichiamo che dalla nostra interfaccia grafica il nuovo esperimento sia visibile." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Logging dei Parametri e Metriche\n", + "\n", + "Possiamo loggare:\n", + "- Parametri → `n_estimators` e altre impostazioni\n", + "- Metriche → accuratezza\n", + "- Modello → versione del modello addestrato\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Usando il modello creato nella Parte 1\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "n_estimators = 100\n", + "\n", + "# Creiamo una nuova run MLflow\n", + "with mlflow.start_run():\n", + " # Log Param\n", + " mlflow.log_param(\"n_estimators\", n_estimators)\n", + "\n", + " # Creazione del modello\n", + " rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n", + " rf_model.fit(X_train, y_train)\n", + "\n", + " # Calcolo metriche\n", + " y_pred = rf_model.predict(X_test)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + "\n", + " # Salviamo il modello\n", + " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", + "\n", + " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Visualizzare e Confrontare i Risultati\n", + "\n", + "MLflow fornisce un’interfaccia web per esplorare tutte le run ed esperimenti.\n", + "\n", + "```bash\n", + "http://127.0.0.1:5000\n", + "```\n", + "\n", + "Accedi all’URL indicato nel terminale. Nella sezione **Experiments** potrai vedere:\n", + "- Parametri loggati\n", + "- Metriche\n", + "- Modelli salvati" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Caricare un Modello Salvato con MLflow\n", + "\n", + "Copiando l'id della run dell'esperimento eseguito, possiamo recuperare un modello da MLflow per fare previsioni future." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow.sklearn\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Inserisci un run_id reale che trovi nell'interfaccia MLflow\n", + "RUN_ID = \"\"\n", + "\n", + "loaded_model = mlflow.sklearn.load_model(f\"runs:/{RUN_ID}/random_forest_model\")\n", + "\n", + "# Verifichiamo l'accuratezza\n", + "y_loaded_pred = loaded_model.predict(X_test)\n", + "acc_loaded = accuracy_score(y_test, y_loaded_pred)\n", + "print(f\"Accuratezza del modello caricato: {acc_loaded:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "È possibile notare che con lo stesso dataset di test, il modello ha precisamente lo stessa accuratezza." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusioni\n", + "In questo laboratorio abbiamo:\n", + "1. **Creato un modello di classificazione con Scikit-learn** su un dataset meteo.\n", + "2. **Aggiunto MLflow** per tracciare parametri, metriche e gestire le versioni del modello.\n", + "3. **Visualizzato i risultati** con l’interfaccia MLflow UI e ricaricato il modello salvato.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prossimi Passi\n", + "- Provare iperparametri diversi (`n_estimators`, `max_depth`, ecc.) e confrontare i risultati su MLflow.\n", + "- Integrare un sistema di **continuous integration/continuous deployment (CI/CD)** per distribuire automaticamente il modello.\n", + "- Monitorare in produzione il drift del modello (eventuali cali di accuratezza)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.10 ('venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a8e57e261110579b348b220fe1c71aea6cd1a8cd238281550a2a6c068185ef06" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md deleted file mode 100644 index 357fafb..0000000 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md +++ /dev/null @@ -1,258 +0,0 @@ -# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow - -In questo laboratorio, affrontiamo un percorso step-by-step: - -1. **Caricare e preparare un dataset meteo** (con dati di temperatura e umidità) per prevedere se pioverà o meno. -2. **Addestrare un modello di Machine Learning** con Scikit-learn. -3. **Valutare il modello** e comprendere i risultati. -4. **Estendere il progetto** integrando **MLflow** per tracciare metriche, parametri e versioni del modello. - -La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow. - ---- - -## Parte 1: Dai dati al modello ML (Supervised Learning con Scikit-learn) - -### Obiettivo -- Creare un semplice **modello di classificazione** per predire la pioggia basandoci su dati di temperatura e umidità. - -### 1. Preparazione del Dataset - -Per semplicità, qui useremo un dataset di esempio: -[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv) - -```python -import pandas as pd - -# URL del dataset (sostituiscilo con il tuo link) -url = "https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv" - -# Carica il dataset -df = pd.read_csv(url) - -# Mostra le prime righe -print(df.head()) -``` - ---- - -### 2. Esplorazione e pulizia dei Dati -1. Controlliamo se ci sono valori mancanti -2. Trasformiamo la colonna `Label` in una variabile numerica (0 = NoRain, 1 = Rain) -3. Selezione delle feature - - -```python -# 1. Rimozione dei valori mancanti -df = df.dropna() - -# 2. Conversione della colonna 'RainToday' in valori numerici -df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0) - -# 3. Selezione delle feature -features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am'] - -X = df[features] -y = df['RainToday'] -``` ---- -### 3. Suddivisione del Dataset in Training e Test - -Prima di addestrare il modello, dividiamo il dataset in: -- **X** (features) → Temperatura, Umidità -- **y** (target) → Label (Rain/NoRain) - -```python -from sklearn.model_selection import train_test_split - -# Suddivisione dei dati (80% training, 20% testing) -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - -``` - ---- -### 4. Creazione e Addestramento del Modello - -Utilizziamo un classificatore semplice, ad esempio **RandomForestClassifier**. - -```python -from sklearn.ensemble import RandomForestClassifier - -# Creazione e addestramento del modello -model = RandomForestClassifier(n_estimators=100, random_state=42) -model.fit(X_train, y_train) - -``` - ---- - -### 5. Valutazione del Modello - -Calcoliamo l’accuratezza e visualizziamo la matrice di confusione. - -```python -from sklearn.metrics import accuracy_score, classification_report - -# Predizioni sul set di test -y_pred = model.predict(X_test) - -# Calcolo dell'accuratezza -accuracy = accuracy_score(y_test, y_pred) -print(f"Accuratezza del modello: {accuracy:.2f}") - -# Report di classificazione -print(classification_report(y_test, y_pred)) -``` - ---- - -### Conclusione (Parte 1) -In questa prima parte, abbiamo: -1. Caricato un dataset relativo alle condizioni meteo -2. Pulito i dati e trasformato la variabile target -3. Suddiviso il dataset in training e test -4. Creato un modello di classificazione con RandomForest -5. Valutato le prestazioni con accuratezza e matrice di confusione - ---- -## Tech: Installazione e Configurazione di MLflow in Locale con Docker - -### Obiettivo -- Configurare un'istanza MLflow in locale per registrare esperimenti. - -### 1. Avviare MLflow con Docker - -Esegui il seguente comando per avviare un'istanza locale di MLflow: - -```bash -docker pull ghcr.io/mlflow/mlflow - -docker run -p 5001:5000 --name mlflow-server \ - -v $(pwd)/mlruns:/mlruns \ - ghcr.io/mlflow/mlflow:latest server \ - --backend-store-uri sqlite:///mlflow.db \ - --default-artifact-root /mlruns \ - --host 0.0.0.0 -``` - -- **`-p 5001:5000`**: Espone MLflow sulla porta 5001 -- **`-v $(pwd)/mlruns:/mlruns`**: Monta la cartella locale per salvare gli esperimenti -- **`--backend-store-uri sqlite:///mlflow.db`**: Utilizza un database SQLite per il tracking - -Una volta avviato, MLflow sarà disponibile all’indirizzo: - -``` -http://127.0.0.1:5001 -``` - -Ora possiamo passare alla terza parte e integrare MLflow nel nostro codice! - ---- -## Parte 2: Integrazione con MLflow - -Adesso, estendiamo il codice esistente per **tracciare i nostri esperimenti** con **MLflow**. Questo ci permetterà di: -- Loggare i nostri parametri (es. `n_estimators`) -- Salvare l’accuratezza e altre metriche -- Salvare il modello e ricaricarlo facilmente - -### Obiettivo -- Integrare MLflow nel codice esistente per versionare e tracciare le metriche del modello. - -### 1. Installazione e Setup MLflow - -Installa MLflow: - -```bash -pip install mlflow -``` - -```python -import mlflow -import mlflow.sklearn - -# Impostiamo un nome per l'esperimento -mlflow.set_tracking_uri("http://127.0.0.1:5001") -mlflow.set_experiment("weather_classification_experiment") -``` - ---- - -### 2. Logging dei Parametri e Metriche - -Possiamo loggare: -- Parametri → `n_estimators` e altre impostazioni -- Metriche → accuratezza -- Modello → versione del modello addestrato - - -```python -# Usando il modello creato nella Parte 1 -from sklearn.ensemble import RandomForestClassifier -from sklearn.metrics import accuracy_score - -n_estimators = 100 - -# Creiamo una nuova run MLflow -with mlflow.start_run(): - # Log Param - mlflow.log_param("n_estimators", n_estimators) - - # Creazione del modello - rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42) - rf_model.fit(X_train, y_train) - - # Calcolo metriche - y_pred = rf_model.predict(X_test) - accuracy = accuracy_score(y_test, y_pred) - mlflow.log_metric("accuracy", accuracy) - - # Salviamo il modello - mlflow.sklearn.log_model(rf_model, "random_forest_model") - - print(f"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}") -``` - ---- - -### 3. Visualizzare e Confrontare i Risultati - -MLflow fornisce un’interfaccia web per esplorare tutte le run ed esperimenti. - -```bash -http://127.0.0.1:5001 -``` - -Accedi all’URL indicato nel terminale. Nella sezione **Experiments** potrai vedere: -- Parametri loggati -- Metriche -- Modelli salvati - -### 4. Caricare un Modello Salvato con MLflow - -Possiamo anche recuperare un modello da MLflow per fare previsioni future. - -```python -import mlflow.sklearn -from sklearn.metrics import accuracy_score - -# Inserisci un run_id reale che trovi nell'interfaccia MLflow -RUN_ID = "" - -loaded_model = mlflow.sklearn.load_model(f"runs:/{RUN_ID}/random_forest_model") - -# Verifichiamo l'accuratezza -y_loaded_pred = loaded_model.predict(X_test) -acc_loaded = accuracy_score(y_test, y_loaded_pred) -print(f"Accuratezza del modello caricato: {acc_loaded:.2f}") -``` - -## Conclusioni -In questo laboratorio abbiamo: -1. **Creato un modello di classificazione con Scikit-learn** su un dataset meteo. -2. **Aggiunto MLflow** per tracciare parametri, metriche e gestire le versioni del modello. -3. **Visualizzato i risultati** con l’interfaccia MLflow UI e ricaricato il modello salvato. - - **Prossimi Passi** -- Provare iperparametri diversi (`n_estimators`, `max_depth`, ecc.) e confrontare i risultati su MLflow. -- Integrare un sistema di **continuous integration/continuous deployment (CI/CD)** per distribuire automaticamente il modello. -- Monitorare in produzione il drift del modello (eventuali cali di accuratezza). diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt b/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt new file mode 100644 index 0000000..2580f85 --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt @@ -0,0 +1,153 @@ +alembic==1.14.1 +annotated-types==0.7.0 +anyio==4.8.0 +appnope==0.1.4 +argon2-cffi==23.1.0 +argon2-cffi-bindings==21.2.0 +arrow==1.3.0 +asttokens==3.0.0 +async-lru==2.0.4 +attrs==25.1.0 +babel==2.17.0 +beautifulsoup4==4.13.3 +bleach==6.2.0 +blinker==1.9.0 +cachetools==5.5.2 +certifi==2025.1.31 +cffi==1.17.1 +charset-normalizer==3.4.1 +click==8.1.8 +cloudpickle==3.1.1 +comm==0.2.2 +contourpy==1.3.1 +cycler==0.12.1 +databricks-sdk==0.44.1 +debugpy==1.8.12 +decorator==5.2.1 +defusedxml==0.7.1 +Deprecated==1.2.18 +docker==7.1.0 +exceptiongroup==1.2.2 +executing==2.2.0 +fastjsonschema==2.21.1 +Flask==3.1.0 +fonttools==4.56.0 +fqdn==1.5.1 +gitdb==4.0.12 +GitPython==3.1.44 +google-auth==2.38.0 +graphene==3.4.3 +graphql-core==3.2.6 +graphql-relay==3.2.0 +gunicorn==23.0.0 +h11==0.14.0 +httpcore==1.0.7 +httpx==0.28.1 +idna==3.10 +importlib_metadata==8.5.0 +ipykernel==6.29.5 +ipython==8.33.0 +ipywidgets==8.1.5 +isoduration==20.11.0 +itsdangerous==2.2.0 +jedi==0.19.2 +Jinja2==3.1.5 +joblib==1.4.2 +json5==0.10.0 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2024.10.1 +jupyter==1.1.1 +jupyter-console==6.6.3 +jupyter-events==0.12.0 +jupyter-lsp==2.2.5 +jupyter_client==8.6.3 +jupyter_core==5.7.2 +jupyter_server==2.15.0 +jupyter_server_terminals==0.5.3 +jupyterlab==4.3.5 +jupyterlab_pygments==0.3.0 +jupyterlab_server==2.27.3 +jupyterlab_widgets==3.0.13 +kiwisolver==1.4.8 +Mako==1.3.9 +Markdown==3.7 +MarkupSafe==3.0.2 +matplotlib==3.10.1 +matplotlib-inline==0.1.7 +mistune==3.1.2 +mlflow==2.20.3 +mlflow-skinny==2.20.3 +nbclient==0.10.2 +nbconvert==7.16.6 +nbformat==5.10.4 +nest-asyncio==1.6.0 +notebook==7.3.2 +notebook_shim==0.2.4 +numpy==2.2.3 +opentelemetry-api==1.30.0 +opentelemetry-sdk==1.30.0 +opentelemetry-semantic-conventions==0.51b0 +overrides==7.7.0 +packaging==24.2 +pandas==2.2.3 +pandocfilters==1.5.1 +parso==0.8.4 +pexpect==4.9.0 +pillow==11.1.0 +platformdirs==4.3.6 +prometheus_client==0.21.1 +prompt_toolkit==3.0.50 +protobuf==5.29.3 +psutil==7.0.0 +ptyprocess==0.7.0 +pure_eval==0.2.3 +pyarrow==19.0.1 +pyasn1==0.6.1 +pyasn1_modules==0.4.1 +pycparser==2.22 +pydantic==2.10.6 +pydantic_core==2.27.2 +Pygments==2.19.1 +pyparsing==3.2.1 +python-dateutil==2.9.0.post0 +python-json-logger==3.2.1 +pytz==2025.1 +PyYAML==6.0.2 +pyzmq==26.2.1 +referencing==0.36.2 +requests==2.32.3 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rpds-py==0.23.1 +rsa==4.9 +scikit-learn==1.6.1 +scipy==1.15.2 +seaborn==0.13.2 +Send2Trash==1.8.3 +six==1.17.0 +smmap==5.0.2 +sniffio==1.3.1 +soupsieve==2.6 +SQLAlchemy==2.0.38 +sqlparse==0.5.3 +stack-data==0.6.3 +terminado==0.18.1 +threadpoolctl==3.5.0 +tinycss2==1.4.0 +tomli==2.2.1 +tornado==6.4.2 +traitlets==5.14.3 +types-python-dateutil==2.9.0.20241206 +typing_extensions==4.12.2 +tzdata==2025.1 +uri-template==1.3.0 +urllib3==2.3.0 +wcwidth==0.2.13 +webcolors==24.11.1 +webencodings==0.5.1 +websocket-client==1.8.0 +Werkzeug==3.1.3 +widgetsnbextension==4.0.13 +wrapt==1.17.2 +zipp==3.21.0 diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb index b1584fc..848696a 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb @@ -2,173 +2,81 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuratezza del modello: 0.82\n", - " precision recall f1-score support\n", - "\n", - " 0 0.85 0.93 0.89 2649\n", - " 1 0.61 0.42 0.50 721\n", - "\n", - " accuracy 0.82 3370\n", - " macro avg 0.73 0.67 0.69 3370\n", - "weighted avg 0.80 0.82 0.81 3370\n", - "\n" - ] - }, - { - "data": { - "image/png": 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TpiAnJ0ejzZEjR9CiRQuo1Wo4OjoiLCxMq1r1Hgysra3Rp08ftG/fHlWqVIGVlZXGQkRE9Lo7evQofHx8cPLkSYSHhyM7OxudO3fG06dPxTZ+fn7Yt28fdu7ciaNHj+L+/fsaf0HOzc1F9+7dkZWVhaioKGzcuBFhYWGYPXu22Ob27dvo3r07OnbsiJiYGEyaNAkfffQRDh48WORaFUIF7MM3ae6r7xKIdO7xmS/0XQKRzhnreCZctVE7ir1v0rcDir3vgwcPUK1aNRw9ehTu7u5ISUlB1apVsXXrVnzwwQcAgGvXrqFhw4aIjo5GmzZt8PPPP6NHjx64f/8+qlevDgBYu3Ytpk2bhgcPHkClUmHatGk4cOAALl++LJ5r0KBBSE5Oxi+//FKk2vTeY0BERKQvJRlKyMzMRGpqqsaSmZlZpPOmpKQAAGxsbAAA586dQ3Z2Njw8PMQ2b775JmrVqoXo6GgAQHR0NFxcXMRQAACenp5ITU3FlStXxDb/PUZ+m/xjFIVe7kpo0aIFIiIiUKlSJTRv3vylYzW///57GVZGRERyUpJJhMHBwZg7d67Gujlz5iAwMPCl++Xl5WHSpElwc3ND48aNAQAJCQlQqVSwtrbWaFu9enUkJCSIbf4bCvK35297WZvU1FQ8e/YMJiYmr7wuvQSDXr16Qa1WAwB69+6tjxKIiIhKFAxmzJgBf39/jXX5v9texsfHB5cvX8bx48eLfW5d0kswmDNnToE/ExERlaWSBAO1Wl2kIPBfvr6+2L9/P44dO4YaNWqI621tbZGVlYXk5GSNXoPExETY2tqKbU6fPq1xvPy7Fv7b5sU7GRITE2FpaVmk3gKAcwyIiIh0ThAE+Pr6Ys+ePYiMjESdOnU0trds2RJGRkaIiIgQ18XGxiIuLg6urq4AAFdXV1y6dAlJSUlim/DwcFhaWsLZ2Vls899j5LfJP0ZR6P3Jh7m5uQgJCcGOHTsQFxeHrKwsje2PHj3SU2VERFThldFzinx8fLB161b8+OOPsLCwEOcEWFlZwcTEBFZWVhg1ahT8/f1hY2MDS0tLTJgwAa6urmjTpg0AoHPnznB2dsaHH36IxYsXIyEhAZ999hl8fHzEnotx48bhiy++wNSpUzFy5EhERkZix44dOHDgQJFr1XuPwdy5c7F8+XIMHDgQKSkp8Pf3R9++faFUKl85gYOIiKgkyuoBR2vWrEFKSgo6dOgAOzs7cdm+fbvYJiQkBD169EC/fv3g7u4OW1tb7N69W9xuYGCA/fv3w8DAAK6urhg2bBi8vLwQFBQktqlTpw4OHDiA8PBwNG3aFMuWLcM333wDT0/Pon8n+n6OQb169bBy5Up0794dFhYWiImJEdedPHkSW7du1fqYfI4ByQGfY0ByoOvnGNT4eG+x9727unep1VGe6L3HICEhAS4uLgAAc3Nz8d7OHj16aNX1QUREpC2+K0FK78GgRo0aiI+PB/C89+DQoUMAgDNnzmg925OIiIhKRu/BoE+fPuIMygkTJmDWrFmoX78+vLy8MHLkSD1XR0REFZqiBEsFpfe7EhYtWiT+PHDgQDg4OCAqKgr169dHz5499ViZvASM7Ize7zZFg9rV8SwzG6cu/ImZoT/ixl9JBbbf+8V4eLo1wgC/ddh35KK4/tl56bi31/QN2HnwHADAtoolFvn3RQvnWqhXswpWf38UU5bu0s1FERXBubNnELb+W1z94zIePHiAkJVf4t1O/z5S9uE//2DF8qWIjjqOJ0+eoEXLVpg+cxYcHGqLbf558ADLly3GyagoPE1/itq162D0mHHw6Fz0CV+kHxV5SKC49B4MXtSmTRvx1oyzZ8+iVatWeq5IHtq1cMTa7cdw7spfMDQ0wFzfnti/xhfN+85HeobmLaQThnbEy6asjp69GeFRf4ifk588E39WGRnin8dPsOibXzBhaMdSvw4ibT17lg4nJyf07tsP/hM1Jy4LgoBJn/jA0NAQK1athrm5OTZtDMPYUSOw+38HYGpqCgCY+ek0PElNRegXa1CpUiX8dGAfpkyehK07dqFhQ2d9XBYVEYOBlN6HEtLS0vDs2TONdTExMejZsydat26tp6rkp5fvany37xSu/pmAS9fvYcyc71DLzgbNnWtqtGvS4A1M/PBdjAv8rtBjpTx5hsSHT8QlM+vfd4XHxT9CwJJd2Lr/NFLTMnR2PURF9U679vCd6IdOHu9Jtv311x1cvBCDmbMD0dilCWrXqYvPZgciIzMDv/z07+ToC+fPY/DQYXBp0gQ1atbEmHEfw8LCElf//8U2VH5x8qGU3oLB33//DVdXV1hZWcHKygr+/v5IT0+Hl5cXWrduDTMzM0RFRemrPNmzNDcGADxOSRfXmRgbISx4OCYt2oHEh08K3XfFjAH4O3IRftscAK9ebXReK5GuZP//A9fUqn8nQiuVSqhUKpz//Zy4rmnz5jj4y89ISU5GXl4efv7pADKzMtHqrbfLvGbSDoOBlN6GEqZMmYKMjAyEhoZi9+7dCA0NxW+//YbWrVvj1q1bGs+QprKlUCiwJOADRJ2/hT9uxYvrF0/uh5MXbmP/kUuF7jt39X4cPX0d6RlZ8HB9E6EzBsLcVI3V3x8ti9KJSlXtOnVhZ2ePlSuWYdacIJiYmGDzpjAkJiTgwYMHYrsly1Zg6mQ/uLu1hqGhIYyNjRES+gVqOTjosXqi4tFbMDh27Bh2796NNm3aYMCAAbC1tcXQoUMxadIkrY6TmZkpef+1kJcLhdKgFKuVlxUzBqCRox06jQgR13Vv74IObzdAm0GLXrInsOjrX8SfL8TehamJGn5eHgwG9FoyMjLC8tBVCJw1E+3avg0DAwO0buOKd9q547/PhvtyVSiePEnFum/DYG1dCYcjf8XUyZOwYdMW1G/gpMcroFequH/xLza9BYPExETxJRLVqlWDqakpunbtqvVxCnoftkH1t2Bkxy684giZ1h/d2jWGx6gVuJeULK7v8FYD1K1RBQnHlmi0/37pRzhx/hY8R4cWeLwzl+7g0zFdoTIyRFZ2ToFtiMoz50aNsWP3j3jy5Amys7NhY2ODoYP6o1GjxgCAv+PisG3rd9j14344OtYHADi9+SZ+P3cW277fgllzgl52eNKzijwkUFx6vStBqVRq/KxSqbQ+RkHvw67WblqJa5OjkGn98f67TdF5dCj+uv9QY9vSDYewYY/mnI9zP8zE1GW7cODo5UKP2cSpBh6lPGUooNeehYUFgOcTEv+4chk+EyYCADIynk+eVio0p2wplQYQ8vT6xHkqAgYDKb0FA0EQ0KBBA/FfSlpaGpo3b64RFoBXv12xoPdhcxhBeytmDMDArq3Q328d0p5moHrl5/8TTEnLQEZmtniHwYv+jn8shohu7o1RrbIFTl+8g4ysbHRq8yamjuqMFZs0XwHapMEbAAAzUzWqVDJHkwZvICsnF9f+TNDxVRJJpT99iri4OPHzvbt3ce3qVVhZWcHO3h6HDv6MSpVsYGdnjxs3YrE4eCE6vuuBtm7vAHg+D6FWLQfMmzsb/gHTYG1tjcjIX3Ey+gRWrf5KX5dFRcRcIKW3YLBhwwZ9nZoKMHaAOwAg/JtJGutHz96M7/adKtIxsnNyMXaAOxZP7geFQoFbfz/AtGW7sX63Zk/Dqe0zxJ9bOtfCoG5v4a/7D/Fm9zkluwiiYrhy5TI+GuElfl66OBgA8H6vPpi3cBEePHiApYsX4eE/D1G1alX0eL8Xxo77WGxvZGSEL9auQ+jyZfjEdxzS09NRq2YtzFu4CO3c25f59ZB22GMgpfe3K+oC365IcsC3K5Ic6PrtivWn/PLqRoW4saRLKVZSfpS7Jx8SERGVFXYYSDEYEBGRbHEoQYrBgIiIZIu5QIrBgIiIZEupZDJ4UbkKBvnzINm1Q0REZYG/bqT0/nZFANi0aRNcXFxgYmICExMTNGnSBJs3b9Z3WURERLKj9x6D5cuXY9asWfD19YWbmxsA4Pjx4xg3bhz++ecf+Pn56blCIiKqqNhDLaX3YLBq1SqsWbMGXl7/PmDk/fffR6NGjRAYGMhgQEREOsNcIKX3YBAfH4+2bdtK1rdt2xbx8fEF7EFERFQ62GMgpfc5Bo6OjtixY4dk/fbt21G/fn09VERERHKhUCiKvVRUeu8xmDt3LgYOHIhjx46JcwxOnDiBiIiIAgMDERFRaanAv9+LTe89Bv369cOpU6dQpUoV7N27F3v37kWVKlVw+vRp9OnTR9/lERERyYreewwAoGXLlvjuu+/0XQYREclMRR4SKK5yEQyIiIj0gblASm/BQKlUvjKpKRQK5OTklFFFREQkN+wxkNJbMNizZ0+h26Kjo7Fy5Urk5eWVYUVERCQ3zAVSegsGvXr1kqyLjY3F9OnTsW/fPgwdOhRBQUF6qIyIiOSCPQZSer8rAQDu37+P0aNHw8XFBTk5OYiJicHGjRvh4OCg79KIiIhkRa/BICUlBdOmTYOjoyOuXLmCiIgI7Nu3D40bN9ZnWUREJBMKRfGXikpvQwmLFy/G559/DltbW3z//fcFDi0QERHpEocSpPQWDKZPnw4TExM4Ojpi48aN2LhxY4Htdu/eXcaVERGRXDAXSOktGHh5eTGpERGRXvH3kJTegkFYWJi+Tk1ERASAPQYFKRd3JRAREVH5wEciExGRbHEoQYrBgIiIZIu5QIrBgIiIZIs9BlIMBkREJFsMBlIMBkREJFvMBVK8K4GIiIhE7DEgIiLZ4lCCFIMBERHJFnOBFIMBERHJFnsMpBgMiIhItpgLpBgMiIhItpRMBhK8K4GIiIhE7DEgIiLZYoeBFIMBERHJFicfShUpGFy8eLHIB2zSpEmxiyEiIipLSuYCiSIFg2bNmkGhUEAQhAK3529TKBTIzc0t1QKJiIh0hT0GUkUKBrdv39Z1HURERGWOuUCqSMHAwcFB13UQERFROVCs2xU3b94MNzc32Nvb46+//gIArFixAj/++GOpFkdERKRLihL8U1FpHQzWrFkDf39/dOvWDcnJyeKcAmtra6xYsaK06yMiItIZpaL4S0WldTBYtWoVvv76a8ycORMGBgbi+latWuHSpUulWhwREZEuKRSKYi/aOHbsGHr27Al7e3soFArs3btXY/vw4cMlx+/SpYtGm0ePHmHo0KGwtLSEtbU1Ro0ahbS0NI02Fy9eRLt27WBsbIyaNWti8eLFWn8nWgeD27dvo3nz5pL1arUaT58+1boAIiIifVEoir9o4+nTp2jatCm+/PLLQtt06dIF8fHx4vL9999rbB86dCiuXLmC8PBw7N+/H8eOHcOYMWPE7ampqejcuTMcHBxw7tw5LFmyBIGBgVi3bp1WtWr9gKM6deogJiZGMiHxl19+QcOGDbU9HBERkd6U1bsSunbtiq5du760jVqthq2tbYHbrl69il9++QVnzpxBq1atADzvwe/WrRuWLl0Ke3t7bNmyBVlZWVi/fj1UKhUaNWqEmJgYLF++XCNAvIrWPQb+/v7w8fHB9u3bIQgCTp8+jQULFmDGjBmYOnWqtocjIiJ6LWVmZiI1NVVjyczMLPbxjhw5gmrVqsHJyQnjx4/Hw4cPxW3R0dGwtrYWQwEAeHh4QKlU4tSpU2Ibd3d3qFQqsY2npydiY2Px+PHjItehdTD46KOP8Pnnn+Ozzz5Deno6hgwZgjVr1iA0NBSDBg3S9nBERER6U5KhhODgYFhZWWkswcHBxaqjS5cu2LRpEyIiIvD555/j6NGj6Nq1qzjBPyEhAdWqVdPYx9DQEDY2NkhISBDbVK9eXaNN/uf8NkVRrHclDB06FEOHDkV6ejrS0tIkxRIREb0OSvLkwxkzZsDf319jnVqtLtax/vsXaxcXFzRp0gT16tXDkSNH0KlTp2LXWBzFfolSUlISYmNjATz/YqtWrVpqRREREZWFkkwxUKvVxQ4Cr1K3bl1UqVIFN2/eRKdOnWBra4ukpCSNNjk5OXj06JE4L8HW1haJiYkabfI/FzZ3oSBaDyU8efIEH374Iezt7dG+fXu0b98e9vb2GDZsGFJSUrQ9HBERkd4oFYpiL7p09+5dPHz4EHZ2dgAAV1dXJCcn49y5c2KbyMhI5OXloXXr1mKbY8eOITs7W2wTHh4OJycnVKpUqcjnLtYcg1OnTuHAgQNITk5GcnIy9u/fj7Nnz2Ls2LHaHo6IiEhvFCVYtJGWloaYmBjExMQAeH7rf0xMDOLi4pCWloYpU6bg5MmTuHPnDiIiItCrVy84OjrC09MTANCwYUN06dIFo0ePxunTp3HixAn4+vpi0KBBsLe3BwAMGTIEKpUKo0aNwpUrV7B9+3aEhoZKhjte+Z0Ihb0ysRBmZmY4ePAg3nnnHY31v/32G7p06VIunmVg0txX3yUQ6dzjM1/ouwQinTMu9oB30QzaeL7Y+27zlj7TpzBHjhxBx44dJeu9vb2xZs0a9O7dG+fPn0dycjLs7e3RuXNnzJs3T2My4aNHj+Dr64t9+/ZBqVSiX79+WLlyJczNzcU2Fy9ehI+PD86cOYMqVapgwoQJmDZtmlbXpfVXXrlyZVhZWUnWW1lZadVVQUREpG9l9drlDh064GV/Dz948OArj2FjY4OtW7e+tE2TJk3w22+/aV3ff2k9lPDZZ5/B399f49aHhIQETJkyBbNmzSpRMURERGWJ70qQKlKPQfPmzTVS1Y0bN1CrVi3UqlULABAXFwe1Wo0HDx5wngEREb02yqrH4HVSpGDQu3dvHZdBRERU9pgLpIoUDObMmaPrOoiIiMocewyktJ5jQERERBWX1ncl5ObmIiQkBDt27EBcXByysrI0tj969KjUiiMiItKlijyJsLi07jGYO3culi9fjoEDByIlJQX+/v7o27cvlEolAgMDdVAiERGRbigUimIvFZXWwWDLli34+uuvMXnyZBgaGmLw4MH45ptvMHv2bJw8eVIXNRIREelEWT358HWidTBISEiAi4sLAMDc3Fx8P0KPHj1w4MCB0q2OiIhIh8rruxL0SetgUKNGDcTHxwMA6tWrh0OHDgEAzpw5o7O3TBEREVHZ0DoY9OnTBxEREQCACRMmYNasWahfvz68vLwwcuTIUi+QiIhIVxSK4i8VldZ3JSxatEj8eeDAgXBwcEBUVBTq16+Pnj17lmpxREREulSRJxEWV4mfY9CmTRv4+/ujdevWWLhwYWnUREREVCbYYyBVag84io+P50uUiIjotcLJh1I6ftM1ERFR+VWBf78XGx+JTERERCL2GBARkWxx8qFUkYOBv7//S7c/ePCgxMWUlpuHl+u7BCKde5aVq+8SiHTO2NBAp8dnt7lUkYPB+fPnX9nG3d29RMUQERGVJfYYSBU5GBw+fFiXdRAREZU5vl1RinMMiIhIthgMpDi8QkRERCL2GBARkWxxjoEUgwEREckWhxKkGAyIiEi22GEgVaw5Br/99huGDRsGV1dX3Lt3DwCwefNmHD9+vFSLIyIi0iW+K0FK62Cwa9cueHp6wsTEBOfPn0dmZiYAICUlhW9XJCKi14qyBEtFpfW1zZ8/H2vXrsXXX38NIyMjcb2bmxt+//33Ui2OiIiIypbWcwxiY2MLfMKhlZUVkpOTS6MmIiKiMlGBRwSKTeseA1tbW9y8eVOy/vjx46hbt26pFEVERFQWOMdASutgMHr0aEycOBGnTp2CQqHA/fv3sWXLFgQEBGD8+PG6qJGIiEgnFIriLxWV1kMJ06dPR15eHjp16oT09HS4u7tDrVYjICAAEyZM0EWNREREOsHnGEgpBEEQirNjVlYWbt68ibS0NDg7O8Pc3Ly0ayu2e8lZ+i6BSOdMVbp9HS1ReVDJVLd/zoPCpUPjRTX7PcdSrKT8KPYDjlQqFZydnUuzFiIiItIzrYNBx44dX/ps6cjIyBIVREREVFYq8lyB4tI6GDRr1kzjc3Z2NmJiYnD58mV4e3uXVl1EREQ6xzkGUloHg5CQkALXBwYGIi0trcQFERERlRUFmAxeVGpPdRw2bBjWr19fWocjIiLSOaWi+EtFVWpvV4yOjoaxsXFpHY6IiEjnKvIv+OLSOhj07dtX47MgCIiPj8fZs2cxa9asUiuMiIiIyp7WwcDKykrjs1KphJOTE4KCgtC5c+dSK4yIiEjXXnaXnVxpFQxyc3MxYsQIuLi4oFKlSrqqiYiIqExwKEFKq8mHBgYG6Ny5M9+iSEREFQLflSCl9V0JjRs3xp9//qmLWoiIiMoU364opXUwmD9/PgICArB//37Ex8cjNTVVYyEiInpd8HZFqSLPMQgKCsLkyZPRrVs3AMD777+vMWlDEAQoFArk5uaWfpVERERUJoocDObOnYtx48bh8OHDuqyHiIiozFTgEYFiK3IwyH87c/v27XVWDBERUVlS8pHIElrdrsj7PYmIqCLhrzUprYJBgwYNXhkOHj16VKKCiIiIykpFnkRYXFoFg7lz50qefEhERPS6qsi3HRaXVsFg0KBBqFatmq5qISIiIj0rcjDg/AIiIqpo+KtNSuu7EoiIiCoKDiVIFTkY5OXl6bIOIiKiMsdcIKX1a5eJiIgqCq3fCyADDAZERCRbnD8nxbBERESkY8eOHUPPnj1hb28PhUKBvXv3amwXBAGzZ8+GnZ0dTExM4OHhgRs3bmi0efToEYYOHQpLS0tYW1tj1KhRSEtL02hz8eJFtGvXDsbGxqhZsyYWL16sda0MBkREJFuKEizaePr0KZo2bYovv/yywO2LFy/GypUrsXbtWpw6dQpmZmbw9PRERkaG2Gbo0KG4cuUKwsPDsX//fhw7dgxjxowRt6empqJz585wcHDAuXPnsGTJEgQGBmLdunVa1aoQKuDtBveSs/RdApHOmaoM9F0Ckc5VMtXtn/Pvzt0t9r7DWtYo1n4KhQJ79uxB7969ATzvLbC3t8fkyZMREBAAAEhJSUH16tURFhaGQYMG4erVq3B2dsaZM2fQqlUrAMAvv/yCbt264e7du7C3t8eaNWswc+ZMJCQkQKVSAQCmT5+OvXv34tq1a0Wujz0GREQkWyXpMcjMzERqaqrGkpmZqXUNt2/fRkJCAjw8PMR1VlZWaN26NaKjowEA0dHRsLa2FkMBAHh4eECpVOLUqVNiG3d3dzEUAICnpydiY2Px+PHjItfDYEBERLKlUBR/CQ4OhpWVlcYSHBysdQ0JCQkAgOrVq2usr169urgtISFB8uRhQ0ND2NjYaLQp6Bj/PUdR8K4EIiKSrZLclTBjxgz4+/trrFOr1SUtSe8YDIiIiIpBrVaXShCwtbUFACQmJsLOzk5cn5iYiGbNmoltkpKSNPbLycnBo0ePxP1tbW2RmJio0Sb/c36bouBQAhERyZayBEtpqVOnDmxtbRERESGuS01NxalTp+Dq6goAcHV1RXJyMs6dOye2iYyMRF5eHlq3bi22OXbsGLKzs8U24eHhcHJyQqVKlYpcD4MBERHJlkKhKPaijbS0NMTExCAmJgbA8wmHMTExiIuLg0KhwKRJkzB//nz873//w6VLl+Dl5QV7e3vxzoWGDRuiS5cuGD16NE6fPo0TJ07A19cXgwYNgr29PQBgyJAhUKlUGDVqFK5cuYLt27cjNDRUMtzxKhxKICIi2Sqr5x6ePXsWHTt2FD/n/7L29vZGWFgYpk6diqdPn2LMmDFITk7GO++8g19++QXGxsbiPlu2bIGvry86deoEpVKJfv36YeXKleJ2KysrHDp0CD4+PmjZsiWqVKmC2bNnazzroCj4HAOi1xSfY0ByoOvnGPxwIb7Y+37Q1O7VjV5D7DEgIiLZ4ni6FL8TIiIiErHHgIiIZItvV5RiMCAiItliLJBiMCAiItlih4EUgwEREcmWkn0GEgwGREQkW+wxkOJdCURERCRijwEREcmWgkMJEgwGREQkWxxKkGIwICIi2eLkQykGAyIiki32GEgxGBARkWwxGEjxrgQiIiISsceAiIhki3clSJWbYHDjxg0cPnwYSUlJyMvL09g2e/ZsPVVFREQVmZK5QKJcBIOvv/4a48ePR5UqVWBra6vxtiuFQsFgQEREOsEeA6lyEQzmz5+PBQsWYNq0afouhYiIZISTD6XKxeTDx48fo3///voug4iISPbKRTDo378/Dh06pO8yiIhIZhQl+KeiKhdDCY6Ojpg1axZOnjwJFxcXGBkZaWz/5JNP9FQZ5du68Rt8szoUfQcOg6//8yGf/Xt2IuLQT7hx7SrS05/if7+egLmFpWTfk8ePYdP6tfjz5nWoVCo0bd4K85asLOtLIJLYtWMbdv+wDfH37wEA6tZ1xMgx49H2HXcAQGZmJlYuX4zwgz8hOysLrV3fwZRPZ6Fy5SriMdo0d5Ycd17wUrzXpVvZXASVCCcfSpWLYLBu3TqYm5vj6NGjOHr0qMY2hULBYKBn1/64jP17fkBdxwYa6zMyMvBWGze81cYN36wOLXDfY5HhWBYciFHjJ6J5q7eRm5OLO3/eKIuyiV6pWvXq8Jnghxq1HAAAB/btxVQ/X2zatgt169XHiqWLEHX8KBYuDoG5uQWWLpqP6ZMn4uuwLRrH+WzuAri2fUf8XFBApvKpIv/Nv7jKRTC4ffu2vkugQjxLT8fC2dMx+dM5+G7DOo1tHwz+EAAQc+5Mgfvm5uTgi+WLMHbCZHR7v6+4vnbderormEgL7dp31Pg83ncS9uzchssXL6JaNVvs27sLQQuXoNXbbQA8DwCD+vbA5YsX0LhJU3E/CwsLVK5StUxrp9LByYdS5WKOAZVfoUsWoLVbO7R821Xrfa/HXsU/D5KgUCgw5sP++KBbR0yfNA63b7HHgMqf3NxchP/yE549ewaXJk1x7eoV5OTk4K02//7Zr12nLmxt7XDpYozGvkuD58OzY1uMHDYQ+/bugiAIZVw9FZeiBEtFpbceA39/f8ybNw9mZmbw9/d/advly5eXUVX0X5GHfsaN2D+wZsO2Yu0ff+8uAGDjN2vw8cQpsLWzx46tG+E3fiQ27dwPSyur0iyXqFhu3riO0d6DkZWVBRMTU3y+bCXq1HPE9evXYGRkBIsXhgVsKlfBw4f/iJ/HjJ+Alm+3hrGxMU5FR2FJ8Dykp6dj4JAPy/pSiEqF3oLB+fPnkZ2dLf5cGMUr+nkyMzORmZn5wjoF1Gp1yYuUsaTEBHy5fBEWr1oHVTG/S0F4/gTLYcNHw/3d9wAAU2fNx8CeHjgacRA9+w4otXqJisuhdm1s2rYbT9PSEPnrQQTN/hRrvtlY5P1Hjhkv/uz0pjMynj3Dlk0bGAxeE0qOJUjoLRgcPny4wJ+1FRwcjLlz52qs85v2GSZPn1XsYxJw/doVPH78CGO9B4rr8nJzcfH8Oez94Xsc/O0cDAwMXnoMm8rPx1wd6vw7p0ClUsHujRpISkzQTeFEWjIyUqHm/08+fNO5Ef64chnbv98Mj85dkZ2djSdPUjV6DR49/EfjroQXNXJpgvVfr0FWVhZUKpXO66eSYSyQKheTD0tixowZkqGIf57xX3VJtWjVBt9u3a2xbvG8WajpUAeDvUa+MhQAQIM3nWGkUuHvuDtwadYCAJCTk43E+/dQ3dZOJ3UTlZQgCMjKysabDRvB0NAQZ06dxLsenQEAf925jYSEeLg0aVbo/tdjr8LS0pKh4HXBXxcS5SYYnD17Fjt27EBcXByysrI0tu3evbuQvQC1Wi0ZNniSl1VIayoqUzMz1KlXX2OdsYkJLK2sxfWPHv6DRw//wb27cQCAP2/egKmZGapVt4OllRXMzM3Rs88AhK37ElWr2aK6nR12fBcGAGjfqXOZXg9RQVavXA5XN3dUt7ND+tOnOPTzfvx+9jRWrP4a5hYW6Nm7H1Yu+xxWVlYwMzPHss8XwKVJM/GOhN+OHsajhw/RuElTqFQqnD4ZjY3ffo2hXsP1e2FUZLxdUapcBINt27bBy8sLnp6eOHToEDp37ozr168jMTERffr00Xd5VIj/7d6BTd+sET9PGjccADB11jx06dEbADDuE38YGBhgUeAMZGZmomFjFyxd/S0sLDnxkPTv8aNHmDtrOh7+8wDm5haoV78BVqz+Gq3btAUATAqYDqVSiRkBE5GVlY3Wbd0wdca/w5SGhobYtWMrQpctgiAIqFGzFiZOnopeffmI99cFpxhIKYRycF9NkyZNMHbsWPj4+MDCwgIXLlxAnTp1MHbsWNjZ2UnmELzKvWT2GFDFZ6p69XAO0euukqlu/5yf/jOl2Pu+Xbdi/gWnXDzH4NatW+jevTuA55PTnj59CoVCAT8/P6xbt+4VexMRERUPn2MgVS6CQaVKlfDkyRMAwBtvvIHLly8DAJKTk5Genq7P0oiIqCJjMpAoF3MM3N3dER4eDhcXF/Tv3x8TJ05EZGQkwsPD8e677+q7PCIiqqA4+VCqXASDL774AhkZGQCAmTNnwsjICFFRUejXrx8CAgL0XB0REVVUnHwoVS6CgY2NjfizUqnE9OnTkZGRgS+//BLNmzdHQgIfhkNERKWPuUBKr3MMMjMzMWPGDLRq1Qpt27bF3r17AQAbNmxAvXr1EBoaCj8/P32WSEREJCt67TGYPXs2vvrqK3h4eCAqKgr9+/fHiBEjcPLkSSxbtgz9+/cv0hP2iIiIioVdBhJ6DQY7d+7Epk2b8P777+Py5cto0qQJcnJycOHChVe+PImIiKikOPlQSq/B4O7du2jZsiUAoHHjxlCr1fDz82MoICKiMsFfN1J6DQa5ubkaLxoxNDSEubm5HisiIiI5YS6Q0mswEAQBw4cPF1+ClJGRgXHjxsHMzEyj3cteokRERFRsTAYSeg0G3t7eGp+HDRump0qIiIgIKCcvUSptfIkSyQFfokRyoOuXKF38O63Y+zapWTGHvsvFA46IiIj0gZMPpRgMiIhItpgLpBgMiIhIvpgMJBgMiIhItviAIym9viuBiIiIyhf2GBARkWxx8qEUgwEREckWc4EUgwEREckXk4EEgwEREckWJx9KMRgQEZFscY6BFO9KICIiIhF7DIiISLbYYSDFYEBERPLFZCDBYEBERLLFyYdSnGNARESypVAUf9FGYGAgFAqFxvLmm2+K2zMyMuDj44PKlSvD3Nwc/fr1Q2JiosYx4uLi0L17d5iamqJatWqYMmUKcnJySuNr0MAeAyIikq2y7C9o1KgRfv31V/GzoeG/v4L9/Pxw4MAB7Ny5E1ZWVvD19UXfvn1x4sQJAEBubi66d+8OW1tbREVFIT4+Hl5eXjAyMsLChQtLtU6FIAhCqR6xHLiXnKXvEoh0zlRloO8SiHSukqlu/5zfSnpW7H1rWCmRmZmpsU6tVkOtVkvaBgYGYu/evYiJiZFsS0lJQdWqVbF161Z88MEHAIBr166hYcOGiI6ORps2bfDzzz+jR48euH//PqpXrw4AWLt2LaZNm4YHDx5ApVIV+zpexKEEIiKSL0Xxl+DgYFhZWWkswcHBhZ7qxo0bsLe3R926dTF06FDExcUBAM6dO4fs7Gx4eHiIbd98803UqlUL0dHRAIDo6Gi4uLiIoQAAPD09kZqaiitXrpTa1wFwKIGIiGSsJJMPZ8yYAX9/f411BfUWAEDr1q0RFhYGJycnxMfHY+7cuWjXrh0uX76MhIQEqFQqWFtba+xTvXp1JCQkAAASEhI0QkH+9vxtpYnBgIiIZKskTz4sbNigIF27dhV/btKkCVq3bg0HBwfs2LEDJiYmxS9CBziUQEREslWCkYQSsba2RoMGDXDz5k3Y2toiKysLycnJGm0SExNha2sLALC1tZXcpZD/Ob9NaWEwICIi+dJTMkhLS8OtW7dgZ2eHli1bwsjICBEREeL22NhYxMXFwdXVFQDg6uqKS5cuISkpSWwTHh4OS0tLODs7l6yYF3AogYiISMcCAgLQs2dPODg44P79+5gzZw4MDAwwePBgWFlZYdSoUfD394eNjQ0sLS0xYcIEuLq6ok2bNgCAzp07w9nZGR9++CEWL16MhIQEfPbZZ/Dx8SnycEZRMRgQEZFsldWTD+/evYvBgwfj4cOHqFq1Kt555x2cPHkSVatWBQCEhIRAqVSiX79+yMzMhKenJ1avXi3ub2BggP3792P8+PFwdXWFmZkZvL29ERQUVOq18jkGRK8pPseA5EDXzzGIe5T56kaFqGVTun9TLy/YY0BERLLFNyVIMRgQEZFsleR2xYqKwYCIiGSMyeBFvF2RiIiIROwxICIi2eJQghSDARERyRZzgRSDARERyRZ7DKQYDIiISLbK6gFHrxMGAyIiki/mAgnelUBEREQi9hgQEZFsscNAisGAiIhki5MPpRgMiIhItjj5UIrBgIiI5Iu5QILBgIiIZIu5QIp3JRAREZGIPQZERCRbnHwoxWBARESyxcmHUgwGREQkW+wxkOIcAyIiIhKxx4CIiGSLPQZS7DEgIiIiEXsMiIhItjj5UIrBgIiIZItDCVIMBkREJFvMBVIMBkREJF9MBhKcfEhEREQi9hgQEZFscfKhFIMBERHJFicfSjEYEBGRbDEXSDEYEBGRfDEZSDAYEBGRbHGOgRTvSiAiIiIRewyIiEi2OPlQSiEIgqDvIuj1lpmZieDgYMyYMQNqtVrf5RDpBP+ck1wwGFCJpaamwsrKCikpKbC0tNR3OUQ6wT/nJBecY0BEREQiBgMiIiISMRgQERGRiMGASkytVmPOnDmckEUVGv+ck1xw8iERERGJ2GNAREREIgYDIiIiEjEYEBERkYjBgPQuMDAQzZo103cZRFrp0KEDJk2apO8yiEodg0EFN3z4cCgUCixatEhj/d69e6Eo4UPCw8LCoFAooFAooFQqYWdnh4EDByIuLk6r4wQEBCAiIqJEtRBpI/+/C4VCASMjI9SpUwdTp05FRkZGkY+xe/duzJs3T4dVEukHg4EMGBsb4/PPP8fjx49L/diWlpaIj4/HvXv3sGvXLsTGxqJ///5aHcPc3ByVK1cu9dqIXqZLly6Ij4/Hn3/+iZCQEHz11VeYM2dOkfe3sbGBhYWFDisk0g8GAxnw8PCAra0tgoODX9pu165daNSoEdRqNWrXro1ly5a98tgKhQK2traws7ND27ZtMWrUKJw+fRqpqalim2nTpqFBgwYwNTVF3bp1MWvWLGRnZ4vbXxxKGD58OHr37o2lS5fCzs4OlStXho+Pj8Y+RCWlVqtha2uLmjVronfv3vDw8EB4eDgA4OHDhxg8eDDeeOMNmJqawsXFBd9//73G/i8OJdSuXRsLFy7EyJEjYWFhgVq1amHdunVleUlEpYLBQAYMDAywcOFCrFq1Cnfv3i2wzblz5zBgwAAMGjQIly5dQmBgIGbNmoWwsLAinycpKQl79uyBgYEBDAwMxPUWFhYICwvDH3/8gdDQUHz99dcICQl56bEOHz6MW7du4fDhw9i4cSPCwsK0qoVIG5cvX0ZUVBRUKhUAICMjAy1btsSBAwdw+fJljBkzBh9++CFOnz790uMsW7YMrVq1wvnz5/Hxxx9j/PjxiI2NLYtLICo9AlVo3t7eQq9evQRBEIQ2bdoII0eOFARBEPbs2SP891//kCFDhPfee09j3ylTpgjOzs6FHnvDhg0CAMHMzEwwNTUVAAgAhE8++eSlNS1ZskRo2bKl+HnOnDlC06ZNNWp2cHAQcnJyxHX9+/cXBg4c+MrrJSoKb29vwcDAQDAzMxPUarUAQFAqlcIPP/xQ6D7du3cXJk+eLH5u3769MHHiRPGzg4ODMGzYMPFzXl6eUK1aNWHNmjU6uQYiXTHUayqhMvX555/j3XffRUBAgGTb1atX0atXL411bm5uWLFiBXJzczV6AP7LwsICv//+O7Kzs/Hzzz9jy5YtWLBggUab7du3Y+XKlbh16xbS0tKQk5PzytfWNmrUSOOcdnZ2uHTpUlEvleiVOnbsiDVr1uDp06cICQmBoaEh+vXrBwDIzc3FwoULsWPHDty7dw9ZWVnIzMyEqanpS4/ZpEkT8ef8YbakpCSdXgdRaeNQgoy4u7vD09MTM2bMKLVjKpVKODo6omHDhvD390ebNm0wfvx4cXt0dDSGDh2Kbt26Yf/+/Th//jxmzpyJrKyslx7XyMhI47NCoUBeXl6p1U1kZmYGR0dHNG3aFOvXr8epU6fw7bffAgCWLFmC0NBQTJs2DYcPH0ZMTAw8PT3555ZkgT0GMrNo0SI0a9YMTk5OGusbNmyIEydOaKw7ceIEGjRoUGhvQUGmT5+OevXqwc/PDy1atEBUVBQcHBwwc+ZMsc1ff/1VsosgKmVKpRKffvop/P39MWTIEJw4cQK9evXCsGHDAAB5eXm4fv06nJ2d9Vwpke6xx0BmXFxcMHToUKxcuVJj/eTJkxEREYF58+bh+vXr2LhxI7744osChx1epmbNmujTpw9mz54NAKhfvz7i4uKwbds23Lp1CytXrsSePXtK7XqISkv//v1hYGCAL7/8EvXr10d4eDiioqJw9epVjB07FomJifoukahMMBjIUFBQkKR7s0WLFtixYwe2bduGxo0bY/bs2QgKCsLw4cO1Pr6fnx8OHDiA06dP4/3334efnx98fX3RrFkzREVFYdasWaV0JUSlx9DQEL6+vli8eDEmT56MFi1awNPTEx06dICtrS169+6t7xKJygRfu0xEREQi9hgQERGRiMGAiIiIRAwGREREJGIwICIiIhGDAREREYkYDIiIiEjEYEBEREQiBgMiIiISMRgQ6cDw4cM1npTXoUMHTJo0qczrOHLkCBQKBZKTk3V2jhevtTjKok4iKhoGA5KN4cOHQ6FQQKFQQKVSwdHREUFBQcjJydH5uXfv3o158+YVqW1Z/5KsXbs2VqxYUSbnIqLyj29XJFnp0qULNmzYgMzMTPz000/w8fGBkZFRga+izsrKgkqlKpXz2tjYlMpxiIh0jT0GJCtqtRq2trZwcHDA+PHj4eHhgf/9738A/u0SX7BgAezt7cVXU//9998YMGAArK2tYWNjg169euHOnTviMXNzc+Hv7w9ra2tUrlwZU6dOxYuvIHlxKCEzMxPTpk1DzZo1oVar4ejoiG+//RZ37txBx44dAQCVKlWCQqEQX2SVl5eH4OBg1KlTByYmJmjatCl++OEHjfP89NNPaNCgAUxMTNCxY0eNOosjNzcXo0aNEs/p5OSE0NDQAtvOnTsXVatWhaWlJcaNG4esrCxxW1FqJ6LygT0GJGsmJiZ4+PCh+DkiIgKWlpYIDw8HAGRnZ8PT0xOurq747bffYGhoiPnz56NLly64ePEiVCoVli1bhrCwMKxfvx4NGzbEsmXLsGfPHrz77ruFntfLywvR0dFYuXIlmjZtitu3b+Off/5BzZo1sWvXLvTr1w+xsbGwtLSEiYkJACA4OBjfffcd1q5di/r16+PYsWMYNmwYqlativbt2+Pvv/9G37594ePjgzFjxuDs2bOYPHlyib6fvLw81KhRAzt37kTlypURFRWFMWPGwM7ODgMGDND43oyNjXHkyBHcuXMHI0aMQOXKlbFgwYIi1U5E5YhAJBPe3t5Cr169BEEQhLy8PCE8PFxQq9VCQECAuL169epCZmamuM/mzZsFJycnIS8vT1yXmZkpmJiYCAcPHhQEQRDs7OyExYsXi9uzs7OFGjVqiOcSBEFo3769MHHiREEQBCE2NlYAIISHhxdY5+HDhwUAwuPHj8V1GRkZgqmpqRAVFaXRdtSoUcLgwYMFQRCEGTNmCM7Ozhrbp02bJjnWixwcHISQkJBCt7/Ix8dH6Nevn/jZ29tbsLGxEZ4+fSquW7NmjWBubi7k5uYWqfaCrpmI9IM9BiQr+/fvh7m5ObKzs5GXl4chQ4YgMDBQ3O7i4qIxr+DChQu4efMmLCwsNI6TkZGBW7duISUlBfHx8WjdurW4zdDQEK1atZIMJ+SLiYmBgYGBVn9TvnnzJtLT0/Hee+9prM/KykLz5s0BAFevXtWoAwBcXV2LfI7CfPnll1i/fj3i4uLw7NkzZGVloVmzZhptmjZtClNTU43zpqWl4e+//0ZaWtorayei8oPBgGSlY8eOWLNmDVQqFezt7WFoqPmfgJmZmcbntLQ0tGzZElu2bJEcq2rVqsWqIX9oQBtpaWkAgAMHDuCNN97Q2KZWq4tVR1Fs27YNAQEBWLZsGVxdXWFhYYElS5bg1KlTRT6GvmonouJhMCBZMTMzg6OjY5Hbt2jRAtu3b0e1atVgaWlZYBs7OzucOnUK7u7uAICcnBycO3cOLVq0KLC9i4sL8vLycPToUXh4eEi25/dY5ObmiuucnZ2hVqsRFxdXaE9Dw4YNxYmU+U6ePPnqi3yJEydOoG3btvj444/Fdbdu3ZK0u3DhAp49eyaGnpMnT8Lc3Bw1a9aEjY3NK2snovKDdyUQvcTQoUNRpUoV9OrVC7/99htu376NI0eO4JNPPsHdu3cBABMnTsSiRYuwd+9eXLt2DR9//PFLn0FQu3ZteHt7Y+TIkdi7d694zB07dgAAHBwcoFAosH//fjx48ABpaWmwsLBAQEAA/Pz8sHHjRty6dQu///47Vq1ahY0bNwIAxo0bhxs3bmDKlCmIjY3F1q1bERYWVqTrvHfvHmJiYjSWx48fo379+jh79iwOHjyI69evY9asWThz5oxk/6ysLIwaNQp//PEHfvrpJ8yZMwe+vr5QKpVFqp2IyhF9T3IgKiv/nXyozfb4+HjBy8tLqFKliqBWq4W6desKo0ePFlJSUgRBeD7ZcOLEiYKlpaVgbW0t+Pv7C15eXoVOPhQEQXj27Jng5+cn2NnZCSqVSnB0dBTWr18vbg8KChJsbW0FhUIheHt7C4LwfMLkihUrBCcnJ8HIyEioWrWq4OnpKRw9elTcb9++fYKjo6OgVquFdu3aCevXry/S5EMAkmXz5s1CRkaGMHz4cMHKykqwtrYWxo8fL0yfPl1o2rSp5HubPXu2ULlyZcHc3FwYPXq0kJGRIbZ5Ve2cfEhUfigEoZAZUkRERCQ7HEogIiIiEYMBERERiRgMiIiISMRgQERERCIGAyIiIhIxGBAREZGIwYCIiIhEDAZEREQkYjAgIiIiEYMBERERiRgMiIiISPR/GxESj7BOOBYAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", "# URL del dataset (sostituiscilo con il tuo link)\n", "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", - "\n", "# Carica il dataset\n", "df = pd.read_csv(url)\n", "\n", - "\n", "# 1. Rimozione dei valori mancanti\n", "df = df.dropna()\n", - "\n", - "# 2. Conversione della colonna 'Rain' in valori numerici\n", + "# 2. Conversione della colonna 'RainToday' in valori numerici\n", "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", - "\n", - "# 3. Selezione delle caratteristiche e della variabile target\n", - "features = ['MinTemp', 'MaxTemp', 'Humidity9am', 'Humidity3pm']\n", + "# 3. Selezione delle feature \n", + "features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am']\n", "X = df[features]\n", "y = df['RainToday']\n", "\n", - "\n", "from sklearn.model_selection import train_test_split\n", - "\n", "# Suddivisione dei dati (80% training, 20% testing)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "print(f\"Dimensioni del dataset di training: {len(X_train)}\")\n", + "print(f\"Dimensioni del dataset di test: {len(X_test)}\")\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", - "\n", "# Creazione e addestramento del modello\n", "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", "model.fit(X_train, y_train)\n", "\n", "from sklearn.metrics import accuracy_score, classification_report\n", - "\n", "# Predizioni sul set di test\n", "y_pred = model.predict(X_test)\n", - "\n", "# Calcolo dell'accuratezza\n", "accuracy = accuracy_score(y_test, y_pred)\n", "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", - "\n", "# Report di classificazione\n", "print(classification_report(y_test, y_pred))\n", "\n", - "from sklearn.metrics import confusion_matrix\n", - "\n", - "# Calcolare la matrice di confusione\n", - "cm = confusion_matrix(y_test, y_pred)\n", - "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", - "\n", + "from sklearn.metrics import confusion_matrix\n", "# Creiamo una heatmap con Seaborn\n", + "target_names = [\"No Rain\", \"Rain\"]\n", + "cm = confusion_matrix(y_test, y_pred)\n", "plt.figure(figsize=(6, 4))\n", - "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Rain\", \"Rain\"], yticklabels=[\"No Rain\", \"Rain\"])\n", - "\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", "# Aggiungiamo i titoli\n", "plt.xlabel(\"Predicted Label\")\n", "plt.ylabel(\"True Label\")\n", "plt.title(\"Confusion Matrix\")\n", - "\n", "# Mostriamo il grafico\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025/02/25 15:17:44 INFO mlflow.tracking.fluent: Experiment with name 'weather_classification_experiment' does not exist. Creating a new experiment.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import mlflow\n", "import mlflow.sklearn\n", "\n", "# Impostiamo un nome per l'esperimento\n", - "mlflow.set_tracking_uri(\"http://127.0.0.1:5001\")\n", + "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n", "mlflow.set_experiment(\"weather_classification_experiment\")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025/02/25 15:17:52 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n", - "2025/02/25 15:17:52 INFO mlflow.tracking._tracking_service.client: 🏃 View run shivering-yak-384 at: http://127.0.0.1:5001/#/experiments/1/runs/e1a69ed25ddc49609674c7df7375d642.\n", - "2025/02/25 15:17:52 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5001/#/experiments/1.\n" - ] - }, - { - "ename": "OSError", - "evalue": "[Errno 30] Read-only file system: '/mlruns'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 24\u001b[0m\n\u001b[1;32m 21\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m, accuracy)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Salviamo il modello\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msklearn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrf_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrandom_forest_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEsperimento concluso. Accuratezza registrata: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maccuracy\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/sklearn/__init__.py:413\u001b[0m, in \u001b[0;36mlog_model\u001b[0;34m(sk_model, artifact_path, conda_env, code_paths, serialization_format, registered_model_name, signature, input_example, await_registration_for, pip_requirements, extra_pip_requirements, pyfunc_predict_fn, metadata)\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;129m@format_docstring\u001b[39m(LOG_MODEL_PARAM_DOCS\u001b[38;5;241m.\u001b[39mformat(package_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscikit-learn\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_model\u001b[39m(\n\u001b[1;32m 336\u001b[0m sk_model,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 348\u001b[0m metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 349\u001b[0m ):\n\u001b[1;32m 350\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 351\u001b[0m \u001b[38;5;124;03m Log a scikit-learn model as an MLflow artifact for the current run. Produces an MLflow Model\u001b[39;00m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;124;03m containing the following flavors:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 411\u001b[0m \n\u001b[1;32m 412\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43martifact_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[43mflavor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msklearn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43msk_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msk_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mconda_env\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconda_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mcode_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcode_paths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43mserialization_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserialization_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m \u001b[49m\u001b[43mregistered_model_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mregistered_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 421\u001b[0m \u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 422\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_example\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_example\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[43m \u001b[49m\u001b[43mawait_registration_for\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mawait_registration_for\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 424\u001b[0m \u001b[43m \u001b[49m\u001b[43mpip_requirements\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpip_requirements\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 425\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_pip_requirements\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_pip_requirements\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 426\u001b[0m \u001b[43m \u001b[49m\u001b[43mpyfunc_predict_fn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpyfunc_predict_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 427\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 428\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/models/model.py:744\u001b[0m, in \u001b[0;36mModel.log\u001b[0;34m(cls, artifact_path, flavor, registered_model_name, await_registration_for, metadata, run_id, resources, **kwargs)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m tracking_uri \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabricks\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m get_uri_scheme(tracking_uri) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabricks\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 743\u001b[0m _logger\u001b[38;5;241m.\u001b[39mwarning(_LOG_MODEL_MISSING_SIGNATURE_WARNING)\n\u001b[0;32m--> 744\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtracking\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfluent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlflow_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43martifact_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 746\u001b[0m \u001b[38;5;66;03m# if the model_config kwarg is passed in, then log the model config as an params\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_config \u001b[38;5;241m:=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_config\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/fluent.py:1170\u001b[0m, in \u001b[0;36mlog_artifacts\u001b[0;34m(local_dir, artifact_path, run_id)\u001b[0m\n\u001b[1;32m 1136\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;124;03mLog all the contents of a local directory as artifacts of the run. If no run is active,\u001b[39;00m\n\u001b[1;32m 1138\u001b[0m \u001b[38;5;124;03mthis method will create a new active run.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1167\u001b[0m \u001b[38;5;124;03m mlflow.log_artifacts(tmp_dir, artifact_path=\"states\")\u001b[39;00m\n\u001b[1;32m 1168\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1169\u001b[0m run_id \u001b[38;5;241m=\u001b[39m run_id \u001b[38;5;129;01mor\u001b[39;00m _get_or_start_run()\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mrun_id\n\u001b[0;32m-> 1170\u001b[0m \u001b[43mMlflowClient\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/client.py:1977\u001b[0m, in \u001b[0;36mMlflowClient.log_artifacts\u001b[0;34m(self, run_id, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 1930\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_artifacts\u001b[39m(\n\u001b[1;32m 1931\u001b[0m \u001b[38;5;28mself\u001b[39m, run_id: \u001b[38;5;28mstr\u001b[39m, local_dir: \u001b[38;5;28mstr\u001b[39m, artifact_path: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1932\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Write a directory of files to the remote ``artifact_uri``.\u001b[39;00m\n\u001b[1;32m 1934\u001b[0m \n\u001b[1;32m 1935\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1975\u001b[0m \n\u001b[1;32m 1976\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1977\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tracking_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/_tracking_service/client.py:874\u001b[0m, in \u001b[0;36mTrackingServiceClient.log_artifacts\u001b[0;34m(self, run_id, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_artifacts\u001b[39m(\u001b[38;5;28mself\u001b[39m, run_id, local_dir, artifact_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Write a directory of files to the remote ``artifact_uri``.\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \n\u001b[1;32m 868\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 872\u001b[0m \n\u001b[1;32m 873\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 874\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_artifact_repo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/store/artifact/local_artifact_repo.py:66\u001b[0m, in \u001b[0;36mLocalArtifactRepository.log_artifacts\u001b[0;34m(self, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 62\u001b[0m artifact_dir \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 63\u001b[0m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39martifact_dir, artifact_path) \u001b[38;5;28;01mif\u001b[39;00m artifact_path \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39martifact_dir\n\u001b[1;32m 64\u001b[0m )\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(artifact_dir):\n\u001b[0;32m---> 66\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43martifact_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m shutil\u001b[38;5;241m.\u001b[39mcopytree(src\u001b[38;5;241m=\u001b[39mlocal_dir, dst\u001b[38;5;241m=\u001b[39martifact_dir, dirs_exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/utils/file_utils.py:211\u001b[0m, in \u001b[0;36mmkdir\u001b[0;34m(root, name)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m!=\u001b[39m errno\u001b[38;5;241m.\u001b[39mEEXIST \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(target):\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m target\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/utils/file_utils.py:208\u001b[0m, in \u001b[0;36mmkdir\u001b[0;34m(root, name)\u001b[0m\n\u001b[1;32m 206\u001b[0m target \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(root, name) \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m root\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m!=\u001b[39m errno\u001b[38;5;241m.\u001b[39mEEXIST \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(target):\n", - "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - " \u001b[0;31m[... skipping similar frames: makedirs at line 213 (1 times)]\u001b[0m\n", - "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:223\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\n", - "\u001b[0;31mOSError\u001b[0m: [Errno 30] Read-only file system: '/mlruns'" - ] - } - ], + "outputs": [], "source": [ "# Usando il modello creato nella Parte 1\n", "from sklearn.ensemble import RandomForestClassifier\n", @@ -189,9 +97,7 @@ " y_pred = rf_model.predict(X_test)\n", " accuracy = accuracy_score(y_test, y_pred)\n", " mlflow.log_metric(\"accuracy\", accuracy)\n", - " mlflow.log_metric(\"accuracy\", accuracy)\n", - " mlflow.log_metric(\"accuracy\", accuracy)\n", - " \n", + "\n", " # Salviamo il modello\n", " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", "\n", @@ -203,12 +109,25 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import mlflow.sklearn\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Inserisci un run_id reale che trovi nell'interfaccia MLflow\n", + "RUN_ID = \"\"\n", + "\n", + "loaded_model = mlflow.sklearn.load_model(f\"runs:/{RUN_ID}/random_forest_model\")\n", + "\n", + "# Verifichiamo l'accuratezza\n", + "y_loaded_pred = loaded_model.predict(X_test)\n", + "acc_loaded = accuracy_score(y_test, y_loaded_pred)\n", + "print(f\"Accuratezza del modello caricato: {acc_loaded:.2f}\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "mlflow_env", + "display_name": "Python 3.10.10 ('venv': venv)", "language": "python", "name": "python3" }, @@ -222,7 +141,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.10.10" + }, + "vscode": { + "interpreter": { + "hash": "a8e57e261110579b348b220fe1c71aea6cd1a8cd238281550a2a6c068185ef06" + } } }, "nbformat": 4, From bdd0107b2d30178d155d7e7e620c750ac9e0dadc Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Mar 2025 18:29:44 +0100 Subject: [PATCH 03/70] refactor lab --- AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index 65cae9f..43647cd 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -60,14 +60,21 @@ "Naviga nella cartella del primo laboratorio:\n", "\n", "```sh\n", - "cd Academy/1-Intro-To-ML-And-MLOps\n", + "cd AI/Academy/1-Intro-To-ML-And-MLOps\n", "```\n", "\n", - "### 4. Creare un Virtual Environment\n", + "### 4. Apri il progetto in VSCode\n", + "\n", + "A questo punto apri VSCode dall'explorer oppure lanciando il comando:\n", + "```sh\n", + "code .\n", + "```\n", + "\n", + "### 5. Creare un Virtual Environment\n", "\n", "Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema.\n", "\n", - "Crea un ambiente virtuale:\n", + "Tramite il terminale di VSCode, crea un ambiente virtuale:\n", "\n", "```sh\n", "python -m venv venv\n", @@ -89,7 +96,7 @@ "\n", "Dovresti vedere il prefisso `(venv)` nel terminale, indicando che l'ambiente virtuale è attivo.\n", "\n", - "### 5. Installare i pacchetti da `requirements.txt`\n", + "### 6. Installare i pacchetti da `requirements.txt`\n", "\n", "Tramite il file `requirements.txt`, possiamo installare tutte le dipendenze di questo laboratorio, in particolare **scikit-learn** e **MLflow**, eseguendo il comando:\n", "\n", @@ -101,7 +108,8 @@ "\n", "```sh\n", "pip list\n", - "```" + "```\n", + "A questo punto si può continuare sul file `lab.ipynb`." ] }, { From 148eb65080916135751c04d7f77de323c60dc06d Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Mar 2025 18:40:15 +0100 Subject: [PATCH 04/70] refactor lab --- AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 701 ++++++++++++++++++- 1 file changed, 687 insertions(+), 14 deletions(-) diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index 43647cd..3edcfb2 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -141,9 +141,216 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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row IDLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9am...WindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainToday
0Row0Albury12.925.70.0NaNNaNWSW46.0W...26.038.030.01007.61008.7NaN2.021.023.2No
1Row1Albury9.228.00.0NaNNaNNE24.0SE...9.045.016.01017.61012.8NaNNaN18.126.5No
2Row2Albury14.325.00.0NaNNaNW50.0SW...24.049.019.01009.61008.21.0NaN18.124.6No
3Row3Albury9.731.90.0NaNNaNNNW80.0SE...28.042.09.01008.91003.6NaNNaN18.330.2No
4Row4Albury15.918.615.6NaNNaNW61.0NNW...28.076.093.0994.3993.08.08.017.415.8Yes
\n", + "

5 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " row ID Location MinTemp MaxTemp Rainfall Evaporation Sunshine \\\n", + "0 Row0 Albury 12.9 25.7 0.0 NaN NaN \n", + "1 Row1 Albury 9.2 28.0 0.0 NaN NaN \n", + "2 Row2 Albury 14.3 25.0 0.0 NaN NaN \n", + "3 Row3 Albury 9.7 31.9 0.0 NaN NaN \n", + "4 Row4 Albury 15.9 18.6 15.6 NaN NaN \n", + "\n", + " WindGustDir WindGustSpeed WindDir9am ... WindSpeed3pm Humidity9am \\\n", + "0 WSW 46.0 W ... 26.0 38.0 \n", + "1 NE 24.0 SE ... 9.0 45.0 \n", + "2 W 50.0 SW ... 24.0 49.0 \n", + "3 NNW 80.0 SE ... 28.0 42.0 \n", + "4 W 61.0 NNW ... 28.0 76.0 \n", + "\n", + " Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am \\\n", + "0 30.0 1007.6 1008.7 NaN 2.0 21.0 \n", + "1 16.0 1017.6 1012.8 NaN NaN 18.1 \n", + "2 19.0 1009.6 1008.2 1.0 NaN 18.1 \n", + "3 9.0 1008.9 1003.6 NaN NaN 18.3 \n", + "4 93.0 994.3 993.0 8.0 8.0 17.4 \n", + "\n", + " Temp3pm RainToday \n", + "0 23.2 No \n", + "1 26.5 No \n", + "2 24.6 No \n", + "3 30.2 No \n", + "4 15.8 Yes \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd\n", "\n", @@ -170,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -200,9 +407,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensioni del dataset di training: 13476\n", + "Dimensioni del dataset di test: 3370\n" + ] + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -223,9 +439,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
RandomForestClassifier(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "RandomForestClassifier(random_state=42)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", @@ -245,9 +890,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuratezza del modello: 0.82\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.93 0.89 2649\n", + " 1 0.61 0.42 0.50 721\n", + "\n", + " accuracy 0.82 3370\n", + " macro avg 0.73 0.67 0.69 3370\n", + "weighted avg 0.80 0.82 0.81 3370\n", + "\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score, classification_report\n", "\n", @@ -264,9 +926,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", @@ -314,7 +987,7 @@ "## Tech: Installazione e Configurazione di MLflow\n", "### Obiettivo\n", "- Configurare un'istanza MLflow in locale per registrare esperimenti.\n", - "### 1. Avviare MLflow con Docker\n", + "### 1. Avviare MLflow\n", "\n", "Esegui il seguente comando per avviare un'istanza locale di MLflow:\n", "```bash\n", @@ -339,7 +1012,7 @@ "Una soluzione basata su questo metodo può essere trovata nella repository interna [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main).\n", "- **Kubernetes**: MLflow viene distribuito su un cluster Kubernetes, garantendo scalabilità e gestione centralizzata. \n", "\n", - "Entrambe le opzioni si basano su una soluzione multi-container dove sono presenti un database persistente e un storage S3, in genere MinIO o soluzioni cloud. \n" + "Entrambe le opzioni si basano su una soluzione multi-container dove sono presenti un database persistente e un storage S3, in genere soluzioni cloud o MinIO. \n" ] }, { @@ -483,7 +1156,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "È possibile notare che con lo stesso dataset di test, il modello ha precisamente lo stessa accuratezza." + "È possibile notare che con lo stesso dataset di test, il modello ha precisamente la stessa accuratezza." ] }, { From c41311e3f24cfd2e3ddcb97d116dc9d9a96fdfa3 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Mar 2025 18:40:31 +0100 Subject: [PATCH 05/70] refactor lab --- AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 695 +------------------ 1 file changed, 11 insertions(+), 684 deletions(-) diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index 3edcfb2..aac03a9 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -141,216 +141,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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row IDLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9am...WindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainToday
0Row0Albury12.925.70.0NaNNaNWSW46.0W...26.038.030.01007.61008.7NaN2.021.023.2No
1Row1Albury9.228.00.0NaNNaNNE24.0SE...9.045.016.01017.61012.8NaNNaN18.126.5No
2Row2Albury14.325.00.0NaNNaNW50.0SW...24.049.019.01009.61008.21.0NaN18.124.6No
3Row3Albury9.731.90.0NaNNaNNNW80.0SE...28.042.09.01008.91003.6NaNNaN18.330.2No
4Row4Albury15.918.615.6NaNNaNW61.0NNW...28.076.093.0994.3993.08.08.017.415.8Yes
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5 rows × 22 columns

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" - ], - "text/plain": [ - " row ID Location MinTemp MaxTemp Rainfall Evaporation Sunshine \\\n", - "0 Row0 Albury 12.9 25.7 0.0 NaN NaN \n", - "1 Row1 Albury 9.2 28.0 0.0 NaN NaN \n", - "2 Row2 Albury 14.3 25.0 0.0 NaN NaN \n", - "3 Row3 Albury 9.7 31.9 0.0 NaN NaN \n", - "4 Row4 Albury 15.9 18.6 15.6 NaN NaN \n", - "\n", - " WindGustDir WindGustSpeed WindDir9am ... WindSpeed3pm Humidity9am \\\n", - "0 WSW 46.0 W ... 26.0 38.0 \n", - "1 NE 24.0 SE ... 9.0 45.0 \n", - "2 W 50.0 SW ... 24.0 49.0 \n", - "3 NNW 80.0 SE ... 28.0 42.0 \n", - "4 W 61.0 NNW ... 28.0 76.0 \n", - "\n", - " Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am \\\n", - "0 30.0 1007.6 1008.7 NaN 2.0 21.0 \n", - "1 16.0 1017.6 1012.8 NaN NaN 18.1 \n", - "2 19.0 1009.6 1008.2 1.0 NaN 18.1 \n", - "3 9.0 1008.9 1003.6 NaN NaN 18.3 \n", - "4 93.0 994.3 993.0 8.0 8.0 17.4 \n", - "\n", - " Temp3pm RainToday \n", - "0 23.2 No \n", - "1 26.5 No \n", - "2 24.6 No \n", - "3 30.2 No \n", - "4 15.8 Yes \n", - "\n", - "[5 rows x 22 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -377,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -407,18 +200,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dimensioni del dataset di training: 13476\n", - "Dimensioni del dataset di test: 3370\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -439,438 +223,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
RandomForestClassifier(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "RandomForestClassifier(random_state=42)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", @@ -890,26 +245,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuratezza del modello: 0.82\n", - " precision recall f1-score support\n", - "\n", - " 0 0.85 0.93 0.89 2649\n", - " 1 0.61 0.42 0.50 721\n", - "\n", - " accuracy 0.82 3370\n", - " macro avg 0.73 0.67 0.69 3370\n", - "weighted avg 0.80 0.82 0.81 3370\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.metrics import accuracy_score, classification_report\n", "\n", @@ -926,20 +264,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", From 1fec616c4a3c38f472526afafac1ed68c9cfa8d6 Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 6 Mar 2025 10:20:50 +0100 Subject: [PATCH 06/70] refactor lab and repo --- AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore | 1 + AI/Academy/1-Intro-To-ML-And-MLOps/README.md | 98 ++++ AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 435 +++++++++--------- 3 files changed, 329 insertions(+), 205 deletions(-) create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/README.md diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore b/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore index 5dd41ee..1c5d00a 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore @@ -1,6 +1,7 @@ # local mlflow mlartifacts/* mlruns/* +dev/* # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md new file mode 100644 index 0000000..e921163 --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md @@ -0,0 +1,98 @@ +# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow + +Benvenuto in questo laboratorio! Qui imparerai a: + +1. **Caricare e preparare un dataset meteo**, con dati di temperatura e umidità. +2. **Addestrare un modello di Machine Learning** con Scikit-learn per prevedere la pioggia. +3. **Valutare il modello** e comprendere i risultati. +4. **Integrare MLflow** per tracciare metriche, parametri e versioni del modello. + +Seguiremo un approccio guidato, con spiegazioni dettagliate ad ogni passaggio. +La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow. + +--- + +## Parte 0: Setup dell'Environment per Python + +Prima di iniziare, dobbiamo assicurarci di avere tutto il necessario per eseguire correttamente il laboratorio. + +#### **Requisiti** + +Questo laboratorio assume che **Python** e **miniconda** siano già installati, la repository [kiratech/labs](https://github.com/kiratech/labs.git) sia accessibile e che Git sia configurato correttamente sul computer locale. + +### 1. Clonare la Repository +Per iniziare, clona la repository del laboratorio eseguendo il seguente comando nel terminale: +```sh + git clone https://github.com/kiratech/labs.git +``` + +### 2. Fare il Checkout del Branch Lab + +Dopo aver clonato la repository, entra nella cartella del progetto: + +```sh + cd labs +``` + +Quindi, fai il checkout del branch `lab`: + +```sh + git checkout academy-ai +``` +In questa cartella sono presenti le risorse relative ai laboratori con tema AI. + +### 3. Spostarsi nella Cartella 1-Intro-To-ML-And-MLOps + +Naviga nella cartella del primo laboratorio: + +```sh + cd AI/Academy/1-Intro-To-ML-And-MLOps +``` + +### 4. Apri il progetto in VSCode + +A questo punto apri VSCode dall'explorer oppure lanciando il comando: +```sh + code . +``` + +### 5. Creare un Virtual Environment + +Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema. + +Tramite il terminale di **VSCode**, crea un ambiente virtuale: + +```sh + conda create --name lab_env python=3.12 pip -y +``` + +Attiva l'Ambiente Virtuale +```sh + conda activate lab_env +``` + +Dovresti vedere il prefisso `(lab_env)` nel terminale, indicando che l'ambiente virtuale è attivo. + +### 6. Installare i Pacchetti Necessari + +Oltre ai pacchetti presenti di default nell'environment, è possibile che servano librerie aggiuntive per il laboratorio. +Prima di installarle, è sempre buona pratica aggiornare `pip` per evitare problemi di compatibilità: +```sh + pip install --upgrade pip +``` + +Ora installiamo alcuni pacchetti fondamentali per l'analisi dei dati e il machine learning: +```sh + pip install scikit-learn pandas seaborn mlflow ipykernel +``` +- [**scikit-learn**](https://scikit-learn.org/stable/index.html): Libreria per il machine learning con strumenti di modellazione e valutazione. +- [**pandas**](https://pandas.pydata.org/): Framework per la manipolazione e l'analisi dei dati in Python. +- [**seaborn**](https://seaborn.pydata.org/): Libreria per la visualizzazione di dati basata su Matplotlib. +- [**mlflow**](https://mlflow.org/): Strumento per il tracciamento e la gestione di esperimenti di machine learning. + +Verifica che i pacchetti siano stati installati correttamente con: +```sh + conda list +``` + +A questo punto si può continuare sul file `lab.ipynb`. \ No newline at end of file diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index aac03a9..b5cfc00 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -6,13 +6,14 @@ "source": [ "# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow\n", "\n", - "In questo laboratorio, affrontiamo un percorso step-by-step:\n", + "Benvenuto in questo laboratorio! Qui imparerai a:\n", "\n", - "1. **Caricare e preparare un dataset meteo** (con dati di temperatura e umidità) per prevedere se pioverà o meno.\n", - "2. **Addestrare un modello di Machine Learning** con Scikit-learn.\n", + "1. **Caricare e preparare un dataset meteo**, con dati di temperatura e umidità.\n", + "2. **Addestrare un modello di Machine Learning** con Scikit-learn per prevedere la pioggia.\n", "3. **Valutare il modello** e comprendere i risultati.\n", - "4. **Estendere il progetto** integrando **MLflow** per tracciare metriche, parametri e versioni del modello.\n", + "4. **Integrare MLflow** per tracciare metriche, parametri e versioni del modello.\n", "\n", + "Seguiremo un approccio guidato, con spiegazioni dettagliate ad ogni passaggio. \n", "La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow." ] }, @@ -27,116 +28,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Parte 0: Setup dell'Environment per Python\n", + "## Parte 1: Dai Dati al Modello di Machine Learning (Supervised Learning con Scikit-learn) \n", "\n", - "#### **Requisiti**\n", - "\n", - "Questo laboratorio assume che Python sia già installato, la repository [kiratech/labs](https://github.com/kiratech/labs.git) sia accessibile e che Git sia configurato correttamente sul computer locale.\n", - "\n", - "### 1. Clonare la Repository\n", - "Per iniziare, clona la repository del laboratorio eseguendo il seguente comando:\n", - "\n", - "```sh\n", - "git clone https://github.com/kiratech/labs.git\n", - "```\n", - "\n", - "### 2. Fare il Checkout del Branch Lab\n", - "\n", - "Dopo aver clonato la repository, entra nella cartella del progetto:\n", - "\n", - "```sh\n", - "cd labs\n", - "```\n", - "\n", - "Quindi, fai il checkout del branch `lab`:\n", - "\n", - "```sh\n", - "git checkout academy-ai\n", - "```\n", - "In questa cartella sono presenti le risorse relative ai laboratori con tema AI.\n", - "\n", - "### 3. Spostarsi nella Cartella 1-Intro-To-ML-And-MLOps\n", - "\n", - "Naviga nella cartella del primo laboratorio:\n", - "\n", - "```sh\n", - "cd AI/Academy/1-Intro-To-ML-And-MLOps\n", - "```\n", - "\n", - "### 4. Apri il progetto in VSCode\n", - "\n", - "A questo punto apri VSCode dall'explorer oppure lanciando il comando:\n", - "```sh\n", - "code .\n", - "```\n", - "\n", - "### 5. Creare un Virtual Environment\n", - "\n", - "Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema.\n", - "\n", - "Tramite il terminale di VSCode, crea un ambiente virtuale:\n", - "\n", - "```sh\n", - "python -m venv venv\n", - "```\n", - "\n", - "Attiva l'Ambiente Virtuale\n", - "\n", - "- **Su macOS e Linux:**\n", - " \n", - " ```sh\n", - " source venv/bin/activate\n", - " ```\n", - " \n", - "- **Su Windows:**\n", - " \n", - " ```sh\n", - " venv\\Scripts\\activate\n", - " ```\n", - "\n", - "Dovresti vedere il prefisso `(venv)` nel terminale, indicando che l'ambiente virtuale è attivo.\n", - "\n", - "### 6. Installare i pacchetti da `requirements.txt`\n", - "\n", - "Tramite il file `requirements.txt`, possiamo installare tutte le dipendenze di questo laboratorio, in particolare **scikit-learn** e **MLflow**, eseguendo il comando:\n", - "\n", - "```sh\n", - "pip install -r src/requirements.txt\n", - "```\n", - "\n", - "Verifica che i pacchetti siano stati installati correttamente con:\n", - "\n", - "```sh\n", - "pip list\n", - "```\n", - "A questo punto si può continuare sul file `lab.ipynb`." + "### Obiettivo \n", + "Costruire un **modello di classificazione** che possa prevedere se pioverà, utilizzando come input i dati di **temperatura** e **umidità**. Il modello verrà addestrato con **Scikit-learn**, uno strumento potente per il Machine Learning in Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parte 1: Dai dati al modello ML (Supervised Learning con Scikit-learn)\n", + "### 1. Preparazione del Dataset \n", "\n", - "**Obiettivo**\n", - "- Creare un semplice **modello di classificazione** per predire la pioggia basandoci su dati di temperatura e umidità.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Preparazione del Dataset\n", + "Prima di addestrare un modello di Machine Learning, è fondamentale pulire i dati, perché valori mancanti o errati possono compromettere le previsioni. Un dataset ben preparato permette al modello di imparare meglio e fornire risultati più accurati. \n", + "\n", + "Per questo laboratorio utilizzeremo un dataset di esempio: \n", + "[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv) \n", + "\n", + "Il dataset **Weather Test Data** contiene informazioni meteorologiche raccolte in diversi momenti. Ogni riga rappresenta un'osservazione con parametri come **temperatura**, **umidità**, **pressione atmosferica** e altre variabili meteo. \n", "\n", - "Per semplicità, qui useremo un dataset di esempio:\n", - "[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv)" + "L'obiettivo di questo dataset è analizzare i pattern climatici e utilizzarli per addestrare un modello di Machine Learning in grado di prevedere condizioni future, come la probabilità di pioggia o variazioni di temperatura." ] }, { @@ -147,10 +58,10 @@ "source": [ "import pandas as pd\n", "\n", - "# URL del dataset (sostituiscilo con il tuo link)\n", + "# URL del dataset \n", "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", "\n", - "# Carica il dataset\n", + "# Caricamento del dataset in un dataframe Pandas\n", "df = pd.read_csv(url)\n", "\n", "# Mostra le prime righe\n", @@ -162,10 +73,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Esplorazione e pulizia dei Dati\n", - "1. Controlliamo se ci sono valori mancanti\n", - "2. Trasformiamo la colonna `Label` in una variabile binaria (0 = NoRain, 1 = Rain)\n", - "3. Selezione delle feature " + "### 2. Esplorazione e Pulizia dei Dati \n", + "\n", + "Per garantire che il nostro modello funzioni correttamente, dobbiamo prima esaminare e preparare il dataset. Ecco i passaggi fondamentali: \n", + "\n", + "1. **Controllo dei dati mancanti**: verifichiamo se ci sono valori assenti, poiché potrebbero compromettere l'addestramento del modello. Se necessario, possiamo eliminarli o sostituirli con valori appropriati. \n", + "2. **Conversione della colonna `Label`**: trasformiamo le categorie testuali (*NoRain* e *Rain*) in valori numerici (0 per *NoRain*, 1 per *Rain*), in modo che il modello possa interpretarli correttamente. \n", + "3. **Selezione delle feature principali**: scegliamo solo le colonne più rilevanti (es. temperatura e umidità) per semplificare il modello e migliorare le sue prestazioni." ] }, { @@ -191,11 +105,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Suddivisione del Dataset in Training e Test\n", + "### 3. Suddivisione del Dataset in Training e Test \n", + "\n", + "Per addestrare e valutare correttamente il modello, dividiamo il dataset in due parti: \n", "\n", - "Prima di addestrare il modello, dividiamo il dataset in:\n", - "- **X** (features) → Temperatura, Umidità\n", - "- **y** (target) → Label (Rain/NoRain)" + "- **X (feature)**: contiene le informazioni che useremo per fare previsioni, come **temperatura** e **umidità**. \n", + "- **y (target)**: rappresenta la variabile che vogliamo prevedere, ovvero se pioverà (*Rain*) o meno (*NoRain*). \n", + "\n", + "Dividiamo i dati in **training set** (80%) e **test set** (20%) per i seguenti motivi: \n", + "\n", + "1. **Addestramento del modello** \n", + " - L'80% dei dati viene usato per insegnare al modello a riconoscere i pattern tra le feature e il target. \n", + "\n", + "2. **Valutazione del modello** \n", + " - Il restante 20% dei dati non viene usato nell'addestramento, ma serve per testare il modello su dati mai visti prima. \n", + " - Questo ci permette di capire se il modello è davvero in grado di fare previsioni accurate su nuovi dati. \n", + "\n", + "3. **Evitare overfitting** \n", + " - Se testassimo il modello sugli stessi dati con cui è stato addestrato, potremmo ottenere risultati ingannevolmente buoni, perché il modello li avrebbe semplicemente memorizzati. \n", + " - Usare dati di test separati aiuta a verificare se il modello può generalizzare le sue previsioni a nuovi dati reali. \n", + "\n", + "Questa suddivisione è un passaggio fondamentale per costruire un modello affidabile e capace di fare previsioni corrette su dati che non ha mai visto prima." ] }, { @@ -216,9 +146,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Creazione e Addestramento del Modello\n", + "### 4. Creazione e Addestramento del Modello \n", + "\n", + "Ora che abbiamo preparato i dati, possiamo costruire e addestrare un modello di Machine Learning. Per questo utilizzeremo un classificatore chiamato [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), una delle tecniche più utilizzate per problemi di classificazione. \n", + "\n", + "#### Perché usiamo **Random Forest**? \n", + "- È un modello basato su **alberi decisionali**, che suddivide i dati in più passaggi per prendere decisioni accurate. \n", + "- È **robusto** e gestisce bene sia dati numerici che categoriali. \n", + "- È meno sensibile ai dati rumorosi rispetto a un singolo albero decisionale, perché combina più alberi per migliorare la precisione. \n", "\n", - "Utilizziamo un classificatore semplice, ad esempio [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)." + "Il modello verrà addestrato utilizzando i dati di **temperatura** e **umidità** per prevedere se ci sarà **pioggia** o meno. Dopo l'addestramento, lo testeremo su dati nuovi per valutarne l'accuratezza." ] }, { @@ -238,9 +175,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 5. Valutazione del Modello\n", + "### 5. Valutazione del Modello \n", "\n", - "Calcoliamo l’accuratezza del modello e altre metriche. Inoltre, visualizziamo la matrice di confusione ovvero un metodo visivo per valutare le prestazioni di un modello." + "Dopo aver addestrato il modello, dobbiamo verificare quanto è accurato nelle previsioni. Per farlo, calcoliamo **l'accuratezza** e altre metriche di valutazione. \n", + "\n", + "#### Perché è importante valutare il modello? \n", + "Un modello di Machine Learning non è utile se non sappiamo quanto sia affidabile. La valutazione ci aiuta a capire: \n", + "- **Se il modello sta imparando correttamente dai dati** o se sta solo memorizzando le risposte (overfitting). \n", + "- **Se può essere utilizzato su dati nuovi** e fare previsioni realistiche. \n", + "\n", + "#### Matrice di Confusione \n", + "Oltre all'accuratezza, utilizzeremo la **matrice di confusione**, un metodo visivo che mostra dove il modello fa previsioni corrette e dove sbaglia. \n", + "- Ci aiuta a individuare **falsi positivi** e **falsi negativi**, che sono errori critici in molti scenari reali. \n", + "- È utile per migliorare il modello, ad esempio regolando soglie di decisione o bilanciando i dati di input. \n", + "\n", + "Con queste analisi possiamo capire se il nostro modello è pronto per essere utilizzato o se necessita di miglioramenti." ] }, { @@ -249,14 +198,16 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.metrics import accuracy_score, classification_report\n", + "from sklearn.metrics import accuracy_score, f1_score, classification_report\n", "\n", "# Predizioni sul set di test\n", "y_pred = model.predict(X_test)\n", "\n", - "# Calcolo dell'accuratezza\n", + "# Calcolo dell'accuratezza ed f1-score\n", "accuracy = accuracy_score(y_test, y_pred)\n", + "f1 = f1_score(y_test, y_pred, average='weighted')\n", "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", + "print(f\"F1-score del modello: {f1:.2f}\")\n", "\n", "# Report di classificazione\n", "print(classification_report(y_test, y_pred))" @@ -291,13 +242,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Conclusione (Parte 1)\n", - "In questa prima parte, abbiamo:\n", - "1. Caricato un dataset relativo alle condizioni meteo\n", - "2. Pulito i dati e trasformato la variabile target\n", - "3. Suddiviso il dataset in training e test\n", - "4. Creato un modello di classificazione con RandomForest\n", - "5. Valutato le prestazioni con accuratezza e matrice di confusione" + "### Conclusione (Parte 1) \n", + "\n", + "In questa prima parte, abbiamo seguito un processo step-by-step per costruire un modello di Machine Learning in grado di prevedere la pioggia. Ecco cosa abbiamo fatto: \n", + "\n", + "1. **Caricato il dataset meteo** per analizzare temperatura, umidità e altre variabili. \n", + "2. **Pulito e preparato i dati**, gestendo eventuali valori mancanti e trasformando la variabile target in un formato comprensibile al modello. \n", + "3. **Suddiviso il dataset** in dati di training (80%) e di test (20%) per addestrare e valutare il modello correttamente. \n", + "4. **Creato un modello di classificazione** utilizzando **RandomForestClassifier**, un algoritmo potente e robusto. \n", + "5. **Valutato le prestazioni del modello** calcolando l’accuratezza e analizzando la matrice di confusione per identificare eventuali errori. \n", + "\n", + "Ora che abbiamo costruito il modello base, nella prossima parte esploreremo come integrare **MLflow** per tracciare gli esperimenti e migliorare ulteriormente le prestazioni." ] }, { @@ -311,35 +266,55 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tech: Installazione e Configurazione di MLflow\n", - "### Obiettivo\n", - "- Configurare un'istanza MLflow in locale per registrare esperimenti.\n", - "### 1. Avviare MLflow\n", + "## Tech: Installazione e Configurazione di MLflow \n", + "\n", + "### Obiettivo \n", + "Configurare un'istanza locale di **MLflow** per registrare esperimenti, monitorare le metriche e gestire i modelli di Machine Learning in modo organizzato. \n", + "\n", + "### 1. Avviare MLflow \n", + "\n", + "Per avviare MLflow in locale, esegui il seguente comando nel terminale: \n", "\n", - "Esegui il seguente comando per avviare un'istanza locale di MLflow:\n", "```bash\n", - "mlflow ui\n", + " mlflow ui\n", "```\n", - "In questo modo MLflow espone l'interfaccia grafica di MLflow all'indirizzo:\n", + "\n", + "Dopo averlo avviato, l'interfaccia grafica sarà accessibile all'indirizzo: \n", + "\n", "```\n", - "http://127.0.0.1:5000\n", + " http://127.0.0.1:5000\n", "```\n", - "Questa configurazione salva artefatti ed esperimenti in locale. " + "\n", + "Questa configurazione permette di **salvare esperimenti e modelli localmente**, consentendo di tracciare le diverse versioni dei modelli, confrontare le metriche di valutazione e ottimizzare i processi di addestramento. \n", + "\n", + "Nelle prossime sezioni, vedremo come registrare parametri, metriche e modelli direttamente all'interno di MLflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### MLflow in Produzione\n", - "Nel contesto produttivo e presso i clienti, non si esegue MLflow in locale. \n", - "Generalmente si scelgono soluzioni che permettono un'architettura più robusta e scalabile, evitando problemi di gestione manuale degli esperimenti e della persistenza dei dati.\n", - "Le soluzioni comuni includono:\n", - "- **Docker Compose**: Un file docker-compose.yml viene utilizzato per avviare MLflow con un database backend e uno storage remoto. \n", - "Una soluzione basata su questo metodo può essere trovata nella repository interna [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main).\n", - "- **Kubernetes**: MLflow viene distribuito su un cluster Kubernetes, garantendo scalabilità e gestione centralizzata. \n", - "\n", - "Entrambe le opzioni si basano su una soluzione multi-container dove sono presenti un database persistente e un storage S3, in genere soluzioni cloud o MinIO. \n" + "### MLflow in Produzione \n", + "\n", + "In ambienti di produzione e presso i clienti, **MLflow non viene eseguito in locale**, ma viene integrato in un'infrastruttura più solida e scalabile. Questo evita problemi legati alla gestione manuale degli esperimenti e alla persistenza dei dati. \n", + "\n", + "Le soluzioni più comuni includono: \n", + "\n", + "- **Docker Compose** \n", + " - MLflow viene avviato utilizzando un file `docker-compose.yml`, che configura un database backend e uno storage remoto per salvare gli esperimenti. \n", + " - Questo approccio è utile per ambienti controllati, dove è necessario un setup rapido e replicabile. \n", + " - Un esempio di implementazione è disponibile nella repository interna: \n", + " [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main). \n", + "\n", + "- **Kubernetes (K8s)** \n", + " - MLflow viene distribuito su un **cluster Kubernetes**, consentendo una gestione scalabile e centralizzata degli esperimenti. \n", + " - Questo approccio è ideale per ambienti enterprise, dove sono richiesti elevati livelli di affidabilità, sicurezza e scalabilità. \n", + "\n", + "Entrambe le soluzioni si basano su un'**architettura multi-container**, che include: \n", + "- **Un database persistente** (es. PostgreSQL o MySQL) per memorizzare i metadati degli esperimenti. \n", + "- **Uno storage S3 o MinIO** per salvare i modelli e gli artefatti, garantendo una gestione sicura e scalabile dei dati. \n", + "\n", + "Questi approcci assicurano che MLflow possa essere utilizzato in modo affidabile in produzione, integrandosi con infrastrutture cloud o on-premise per una gestione efficace dei modelli di Machine Learning." ] }, { @@ -353,18 +328,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Parte 2: Integrazione con MLflow\n", + "## Parte 2: Integrazione con MLflow \n", "\n", - "Adesso, estendiamo il codice esistente per **tracciare i nostri esperimenti** con **MLflow**. Questo ci permetterà di:\n", - "- Loggare i nostri parametri (es. `n_estimators`)\n", - "- Salvare l’accuratezza e altre metriche\n", - "- Salvare il modello e ricaricarlo facilmente\n", + "Ora estenderemo il codice esistente per **tracciare i nostri esperimenti** utilizzando **MLflow**. Questo ci permetterà di monitorare il processo di addestramento del modello, confrontare le diverse configurazioni e gestire le versioni del modello in modo strutturato. \n", "\n", - "### Obiettivo\n", - "- Integrare MLflow nel codice esistente per versionare e tracciare le metriche del modello.\n", + "### Perché integrare MLflow? \n", + "Con MLflow possiamo: \n", + "- **Registrare i parametri di addestramento** (es. `n_estimators` per Random Forest) per confrontare diverse configurazioni. \n", + "- **Salvare le metriche di valutazione** (es. accuratezza, F1-score) per monitorare le prestazioni del modello. \n", + "- **Archiviare il modello addestrato** per poterlo ricaricare facilmente in futuro e riutilizzarlo senza doverlo riaddestrare. \n", "\n", - "### 1. Setup MLflow\n", - "Impostiamo le variabili di MLflow per questo esperimento:" + "### Obiettivo \n", + "Integrare MLflow nel codice esistente per **tracciare e versionare i modelli**, registrando parametri, metriche e artefatti in modo strutturato. \n", + "\n", + "### 1. Configurazione di MLflow \n", + "Prima di iniziare a tracciare gli esperimenti, impostiamo le variabili necessarie per utilizzare MLflow in questo progetto." ] }, { @@ -385,19 +363,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Verifichiamo che dalla nostra interfaccia grafica il nuovo esperimento sia visibile." + "Verifichiamo che dalla [nostra interfaccia grafica](http://127.0.0.1:5000) il nuovo esperimento sia visibile." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Logging dei Parametri e Metriche\n", + "### 2. Registrazione di Parametri, Metriche e Modello \n", + "\n", + "Con **MLflow**, possiamo salvare e tracciare automaticamente diverse informazioni durante l'addestramento del modello. Questo ci aiuta a confrontare le performance tra diverse configurazioni e a recuperare facilmente i modelli migliori. \n", + "\n", + "Ecco cosa possiamo registrare: \n", + "\n", + "- **Parametri** → Valori utilizzati per configurare il modello, come `n_estimators` (numero di alberi in Random Forest) e altri iperparametri. \n", + "- **Metriche** → Indicatori delle prestazioni del modello, come **accuratezza**, **F1-score**, precisione e recall. \n", + "- **Modello** → La versione del modello addestrato, che potrà essere ricaricata e riutilizzata senza bisogno di riaddestramento. \n", "\n", - "Possiamo loggare:\n", - "- Parametri → `n_estimators` e altre impostazioni\n", - "- Metriche → accuratezza\n", - "- Modello → versione del modello addestrato\n" + "Registrando questi elementi, possiamo analizzare e confrontare le diverse versioni del modello in modo strutturato e riproducibile." ] }, { @@ -407,56 +390,96 @@ "outputs": [], "source": [ "# Usando il modello creato nella Parte 1\n", + "import os\n", "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import accuracy_score, f1_score\n", + "from mlflow.models.signature import infer_signature\n", + "\n", + "# Eseguiremo 4 esperimenti per addestrare più modelli \n", + "n_estimators = [1, 10, 100, 500]\n", + "\n", + "for n_e in n_estimators:\n", + " # Creiamo una nuova run MLflow\n", + " with mlflow.start_run():\n", + " # Log Param\n", + " mlflow.log_param(\"n_estimators\", n_e)\n", "\n", - "n_estimators = 100\n", + " # Creazione del modello\n", + " rf_model = RandomForestClassifier(n_estimators=n_e, random_state=42)\n", + " rf_model.fit(X_train, y_train)\n", "\n", - "# Creiamo una nuova run MLflow\n", - "with mlflow.start_run():\n", - " # Log Param\n", - " mlflow.log_param(\"n_estimators\", n_estimators)\n", + " # Calcolo metriche\n", + " y_pred = rf_model.predict(X_test)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " f1 = f1_score(y_test, y_pred, average='weighted')\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + " mlflow.log_metric(\"f1\", f1)\n", "\n", - " # Creazione del modello\n", - " rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n", - " rf_model.fit(X_train, y_train)\n", + " # Creiamo una heatmap con Seaborn\n", + " target_names = [\"No Rain\", \"Rain\"]\n", + " cm = confusion_matrix(y_test, y_pred)\n", + " plt.figure(figsize=(6, 4))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", "\n", - " # Calcolo metriche\n", - " y_pred = rf_model.predict(X_test)\n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " mlflow.log_metric(\"accuracy\", accuracy)\n", + " # Aggiungiamo i titoli\n", + " plt.xlabel(\"Predicted Label\")\n", + " plt.ylabel(\"True Label\")\n", + " plt.title(\"Confusion Matrix\")\n", "\n", - " # Salviamo il modello\n", - " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", + " # Salviamo l'immagine come PNG\n", + " if not os.path.exists(\"dev/\"):\n", + " os.makedirs(\"dev/\")\n", + " plt.savefig(\"dev/confusion_matrix.png\")\n", + " plt.close()\n", + " # Salviamo la confusion matrix su MLFlow come artifatto\n", + " mlflow.log_artifact(\"dev/confusion_matrix.png\")\n", "\n", - " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" + " # Salviamo il modello\n", + " example_dict = {'MinTemp': 1.1, 'MaxTemp': 1.1, 'Humidity3pm': 1.1, 'Humidity9am': 1.1}\n", + " signature = infer_signature(model_input=example_dict)\n", + " mlflow.sklearn.log_model(rf_model, \"random_forest_model\", signature=signature)\n", + "\n", + " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Visualizzare e Confrontare i Risultati\n", + "### 3. Visualizzazione e Confronto dei Risultati \n", + "\n", + "Dopo aver registrato i parametri, le metriche e i modelli, possiamo utilizzare **MLflow** per esplorare e confrontare i diversi esperimenti. \n", "\n", - "MLflow fornisce un’interfaccia web per esplorare tutte le run ed esperimenti.\n", + "MLflow fornisce un’interfaccia web accessibile all’indirizzo: \n", "\n", "```bash\n", - "http://127.0.0.1:5000\n", + " http://127.0.0.1:5000\n", "```\n", "\n", - "Accedi all’URL indicato nel terminale. Nella sezione **Experiments** potrai vedere:\n", - "- Parametri loggati\n", - "- Metriche\n", - "- Modelli salvati" + "Accedendo a questa interfaccia, nella sezione **Experiments**, sarà possibile: \n", + "- **Esaminare i parametri** utilizzati in ogni esperimento. \n", + "- **Confrontare le metriche** tra diverse configurazioni di modello. \n", + "- **Visualizzare e scaricare i modelli salvati**, facilitando il riutilizzo e il deployment. \n", + "\n", + "Questa funzionalità consente di monitorare l’evoluzione delle performance del modello e di identificare rapidamente le configurazioni migliori." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Caricare un Modello Salvato con MLflow\n", + "### 4. Caricare un Modello Salvato con MLflow \n", + "\n", + "MLflow consente di salvare e ricaricare facilmente i modelli addestrati, evitando la necessità di riaddestrarli ogni volta. \n", + "\n", + "Per recuperare un modello salvato in MLflow, è necessario copiare **l'ID della run** dell'esperimento eseguito. Questo ID identifica in modo univoco ogni esperimento registrato e permette di caricare il modello corrispondente per effettuare previsioni future. \n", + "\n", + "Questa funzionalità è particolarmente utile per: \n", + "- **Riutilizzare un modello addestrato** senza dover ripetere il training. \n", + "- **Confrontare diverse versioni** di un modello per scegliere quella più performante. \n", + "- **Integrare il modello in applicazioni o API**, senza doverlo ricostruire da zero. \n", "\n", - "Copiando l'id della run dell'esperimento eseguito, possiamo recuperare un modello da MLflow per fare previsioni future." + "Nelle prossime sezioni vedremo come eseguire questo processo in pratica." ] }, { @@ -483,34 +506,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "È possibile notare che con lo stesso dataset di test, il modello ha precisamente la stessa accuratezza." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusioni\n", - "In questo laboratorio abbiamo:\n", - "1. **Creato un modello di classificazione con Scikit-learn** su un dataset meteo.\n", - "2. **Aggiunto MLflow** per tracciare parametri, metriche e gestire le versioni del modello.\n", - "3. **Visualizzato i risultati** con l’interfaccia MLflow UI e ricaricato il modello salvato.\n" + "## Conclusioni \n", + "\n", + "In questo laboratorio, abbiamo seguito un processo completo per costruire e monitorare un modello di Machine Learning applicato ai dati meteorologici. In particolare, abbiamo: \n", + "\n", + "1. **Creato un modello di classificazione** utilizzando **Scikit-learn**, sfruttando temperatura e umidità per prevedere la pioggia. \n", + "2. **Integrato MLflow** per tracciare i parametri di addestramento, registrare le metriche di valutazione e gestire le versioni del modello in modo strutturato. \n", + "3. **Esplorato e confrontato i risultati** attraverso l’interfaccia di MLflow UI, verificando le diverse configurazioni e caricando un modello salvato per future previsioni. \n", + "\n", + "Questo approccio ci permette di migliorare il processo di sviluppo dei modelli di Machine Learning, rendendolo più organizzato, riproducibile e scalabile." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Prossimi Passi\n", - "- Provare iperparametri diversi (`n_estimators`, `max_depth`, ecc.) e confrontare i risultati su MLflow.\n", - "- Integrare un sistema di **continuous integration/continuous deployment (CI/CD)** per distribuire automaticamente il modello.\n", - "- Monitorare in produzione il drift del modello (eventuali cali di accuratezza)." + "## Prossimi Passi \n", + "\n", + "Ora che abbiamo costruito e tracciato il nostro modello, possiamo esplorare ulteriori miglioramenti e integrare il lavoro in un flusso più avanzato. \n", + "\n", + "- **Ottimizzazione degli iperparametri**: testare diverse configurazioni di `n_estimators`, `max_depth` e altri parametri del modello, registrando i risultati in **MLflow** per identificare la combinazione migliore. \n", + "- **Automazione con CI/CD**: integrare un sistema di **Continuous Integration/Continuous Deployment (CI/CD)** per addestrare e distribuire automaticamente nuove versioni del modello, riducendo il rischio di errori manuali. \n", + "- **Monitoraggio del modello in produzione**: implementare un sistema di **monitoraggio del drift del modello**, per rilevare eventuali cali di accuratezza nel tempo e decidere quando è necessario riaddestrarlo con nuovi dati. \n", + "\n", + "Questi passaggi aiutano a trasformare il modello sviluppato in un sistema robusto e affidabile, pronto per essere utilizzato in scenari reali." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.10 ('venv': venv)", + "display_name": "Python 3.12.9 ('lab_env')", "language": "python", "name": "python3" }, @@ -524,12 +549,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.12.9" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "a8e57e261110579b348b220fe1c71aea6cd1a8cd238281550a2a6c068185ef06" + "hash": "fbd6de99d3dee89886aa6f30475b6b26f6fe2ef531b8e041d1262739f0fd6851" } } }, From 458bf5053cfdefb3b5660ca27c1b26a8fe2e3df4 Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 12 Mar 2025 13:24:11 +0100 Subject: [PATCH 07/70] fix accordingly to review --- AI/Academy/1-Intro-To-ML-And-MLOps/README.md | 75 ++-- AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 326 +++++++++--------- .../src/requirements.txt | 153 -------- .../src/solution.ipynb | 154 --------- 4 files changed, 201 insertions(+), 507 deletions(-) delete mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt delete mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md index e921163..76da717 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md @@ -1,98 +1,99 @@ -# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow +# Lab: Basic ML Model with Weather Dataset + MLflow Integration -Benvenuto in questo laboratorio! Qui imparerai a: +Welcome to this lab! Here you will learn how to: -1. **Caricare e preparare un dataset meteo**, con dati di temperatura e umidità. -2. **Addestrare un modello di Machine Learning** con Scikit-learn per prevedere la pioggia. -3. **Valutare il modello** e comprendere i risultati. -4. **Integrare MLflow** per tracciare metriche, parametri e versioni del modello. +1. **Load and prepare a weather dataset**, with temperature and humidity data. +2. **Train a Machine Learning model** using Scikit-learn, a powerful tool for Machine Learning in Python, to predict rain. +3. **Evaluate the model** computing metrics to determine how well it makes predictions on new data. +4. **Integrate MLflow**, one of the most used tool to track metrics, parameters, and model versions. -Seguiremo un approccio guidato, con spiegazioni dettagliate ad ogni passaggio. -La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow. +We will follow a guided approach with detailed explanations at each step. +The first part focuses on Scikit-learn and the weather dataset. The second part extends the existing code with MLflow. --- -## Parte 0: Setup dell'Environment per Python +## Part 0: Setting Up the Python Environment -Prima di iniziare, dobbiamo assicurarci di avere tutto il necessario per eseguire correttamente il laboratorio. +Before we begin, we need to ensure we have everything required to run the lab properly. -#### **Requisiti** +#### **Requirements** -Questo laboratorio assume che **Python** e **miniconda** siano già installati, la repository [kiratech/labs](https://github.com/kiratech/labs.git) sia accessibile e che Git sia configurato correttamente sul computer locale. +This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well. +In order to execute this laboratory, you will be asked to install a set of tools common in Machine Learning field: +- [**scikit-learn**](https://scikit-learn.org/stable/index.html): Machine learning library with modeling and evaluation tools. +- [**pandas**](https://pandas.pydata.org/): Framework for data manipulation and analysis in Python. +- [**seaborn**](https://seaborn.pydata.org/): Data visualization library based on Matplotlib. +- [**mlflow**](https://mlflow.org/): Tool for tracking and managing machine learning experiments. -### 1. Clonare la Repository -Per iniziare, clona la repository del laboratorio eseguendo il seguente comando nel terminale: +### 1. Clone the Repository +To start, clone the lab repository by running the following command in the terminal: ```sh git clone https://github.com/kiratech/labs.git ``` -### 2. Fare il Checkout del Branch Lab +### 2. Checkout the Lab Branch -Dopo aver clonato la repository, entra nella cartella del progetto: +After cloning the repository, navigate to the project folder: ```sh cd labs ``` -Quindi, fai il checkout del branch `lab`: +Then, checkout the `lab` branch: ```sh git checkout academy-ai ``` -In questa cartella sono presenti le risorse relative ai laboratori con tema AI. +This folder contains resources related to AI-themed labs. -### 3. Spostarsi nella Cartella 1-Intro-To-ML-And-MLOps +### 3. Navigate to the 1-Intro-To-ML-And-MLOps Folder -Naviga nella cartella del primo laboratorio: +Go to the folder of the first lab: ```sh cd AI/Academy/1-Intro-To-ML-And-MLOps ``` -### 4. Apri il progetto in VSCode +### 4. Open the Project in VSCode -A questo punto apri VSCode dall'explorer oppure lanciando il comando: +At this point, open VSCode from the file explorer or by running the command: ```sh code . ``` -### 5. Creare un Virtual Environment +### 5. Create a Virtual Environment -Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema. +A virtual environment allows you to isolate the project's dependencies from the system-wide ones. -Tramite il terminale di **VSCode**, crea un ambiente virtuale: +Using the **VSCode** terminal, create a virtual environment: ```sh conda create --name lab_env python=3.12 pip -y ``` -Attiva l'Ambiente Virtuale +Activate the Virtual Environment: ```sh conda activate lab_env ``` -Dovresti vedere il prefisso `(lab_env)` nel terminale, indicando che l'ambiente virtuale è attivo. +You should see the `(lab_env)` prefix in the terminal, indicating that the virtual environment is active. -### 6. Installare i Pacchetti Necessari +### 6. Install Required Packages -Oltre ai pacchetti presenti di default nell'environment, è possibile che servano librerie aggiuntive per il laboratorio. -Prima di installarle, è sempre buona pratica aggiornare `pip` per evitare problemi di compatibilità: +Besides the default packages in the environment, additional libraries may be needed for the lab. +Before installing them, it's always a good practice to update `pip` to avoid compatibility issues: ```sh pip install --upgrade pip ``` -Ora installiamo alcuni pacchetti fondamentali per l'analisi dei dati e il machine learning: +Now, install some essential packages for data analysis and machine learning: ```sh pip install scikit-learn pandas seaborn mlflow ipykernel ``` -- [**scikit-learn**](https://scikit-learn.org/stable/index.html): Libreria per il machine learning con strumenti di modellazione e valutazione. -- [**pandas**](https://pandas.pydata.org/): Framework per la manipolazione e l'analisi dei dati in Python. -- [**seaborn**](https://seaborn.pydata.org/): Libreria per la visualizzazione di dati basata su Matplotlib. -- [**mlflow**](https://mlflow.org/): Strumento per il tracciamento e la gestione di esperimenti di machine learning. -Verifica che i pacchetti siano stati installati correttamente con: +Verify that the packages were installed correctly with: ```sh conda list ``` -A questo punto si può continuare sul file `lab.ipynb`. \ No newline at end of file +At this point, you can proceed with the `lab.ipynb` file. \ No newline at end of file diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index b5cfc00..e2352e5 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -4,50 +4,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow\n", + "# Lab: Basic ML Model with Weather Dataset + MLflow Integration\n", "\n", - "Benvenuto in questo laboratorio! Qui imparerai a:\n", + "Welcome to this lab! Here you will learn how to:\n", "\n", - "1. **Caricare e preparare un dataset meteo**, con dati di temperatura e umidità.\n", - "2. **Addestrare un modello di Machine Learning** con Scikit-learn per prevedere la pioggia.\n", - "3. **Valutare il modello** e comprendere i risultati.\n", - "4. **Integrare MLflow** per tracciare metriche, parametri e versioni del modello.\n", + "1. **Load and prepare a weather dataset**, with temperature and humidity data.\n", + "2. **Train a Machine Learning model** using Scikit-learn, a powerful tool for Machine Learning in Python, to predict rain.\n", + "3. **Evaluate the model** computing metrics to determine how well it makes predictions on new data.\n", + "4. **Integrate MLflow**, one of the most used tool to track metrics, parameters, and model versions.\n", "\n", - "Seguiremo un approccio guidato, con spiegazioni dettagliate ad ogni passaggio. \n", - "La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow." + "We will follow a guided approach with detailed explanations at each step. \n", + "The first part focuses on Scikit-learn and the weather dataset. The second part extends the existing code with MLflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---\n" + "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Parte 1: Dai Dati al Modello di Machine Learning (Supervised Learning con Scikit-learn) \n", + "## Part 1: From Data to Machine Learning Model (Supervised Learning with Scikit-learn) \n", "\n", - "### Obiettivo \n", - "Costruire un **modello di classificazione** che possa prevedere se pioverà, utilizzando come input i dati di **temperatura** e **umidità**. Il modello verrà addestrato con **Scikit-learn**, uno strumento potente per il Machine Learning in Python." + "### Objective \n", + "Build a **classification model** that can predict whether it will rain, using **temperature** and **humidity** as input data. The model will be trained using **Scikit-learn**, a powerful tool for Machine Learning in Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1. Preparazione del Dataset \n", + "### 1. Preparing the Dataset \n", "\n", - "Prima di addestrare un modello di Machine Learning, è fondamentale pulire i dati, perché valori mancanti o errati possono compromettere le previsioni. Un dataset ben preparato permette al modello di imparare meglio e fornire risultati più accurati. \n", + "Before training a Machine Learning model, it is essential to clean the data, as missing or incorrect values can compromise predictions. A well-prepared dataset allows the model to learn better and provide more accurate results. \n", "\n", - "Per questo laboratorio utilizzeremo un dataset di esempio: \n", + "For this lab, we will use an example dataset: \n", "[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv) \n", "\n", - "Il dataset **Weather Test Data** contiene informazioni meteorologiche raccolte in diversi momenti. Ogni riga rappresenta un'osservazione con parametri come **temperatura**, **umidità**, **pressione atmosferica** e altre variabili meteo. \n", + "The **Weather Test Data** dataset contains meteorological information collected at different times. Each row represents an observation with parameters such as **temperature**, **humidity**, **atmospheric pressure**, and other weather variables. \n", "\n", - "L'obiettivo di questo dataset è analizzare i pattern climatici e utilizzarli per addestrare un modello di Machine Learning in grado di prevedere condizioni future, come la probabilità di pioggia o variazioni di temperatura." + "The goal of this dataset is to analyze weather patterns and use them to train a Machine Learning model capable of predicting future conditions, such as the probability of rain or temperature variations." ] }, { @@ -58,13 +58,13 @@ "source": [ "import pandas as pd\n", "\n", - "# URL del dataset \n", + "# Dataset URL \n", "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", "\n", - "# Caricamento del dataset in un dataframe Pandas\n", + "# Load the dataset in a Pandas dataframe\n", "df = pd.read_csv(url)\n", "\n", - "# Mostra le prime righe\n", + "# Show first 5 rows\n", "df.head(5)\n", "\n" ] @@ -73,13 +73,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Esplorazione e Pulizia dei Dati \n", + "### 2. Data Exploration and Cleaning \n", "\n", - "Per garantire che il nostro modello funzioni correttamente, dobbiamo prima esaminare e preparare il dataset. Ecco i passaggi fondamentali: \n", + "To ensure our model works correctly, we first need to examine and prepare the dataset. Here are the key steps: \n", "\n", - "1. **Controllo dei dati mancanti**: verifichiamo se ci sono valori assenti, poiché potrebbero compromettere l'addestramento del modello. Se necessario, possiamo eliminarli o sostituirli con valori appropriati. \n", - "2. **Conversione della colonna `Label`**: trasformiamo le categorie testuali (*NoRain* e *Rain*) in valori numerici (0 per *NoRain*, 1 per *Rain*), in modo che il modello possa interpretarli correttamente. \n", - "3. **Selezione delle feature principali**: scegliamo solo le colonne più rilevanti (es. temperatura e umidità) per semplificare il modello e migliorare le sue prestazioni." + "1. **Check for missing data**: Identify if there are any missing values, as they could compromise model training. If necessary, we can remove them or replace them with appropriate values. \n", + "2. **Convert the `Label` column**: Transform categorical values (*NoRain* and *Rain*) into numerical values (0 for *NoRain*, 1 for *Rain*), so the model can interpret them correctly. \n", + "3. **Select key features**: Choose only the most relevant columns (e.g., temperature and humidity) to simplify the model and improve its performance.\n" ] }, { @@ -88,13 +88,13 @@ "metadata": {}, "outputs": [], "source": [ - "# 1. Rimozione dei valori mancanti\n", + "# 1. Remove missing values\n", "df = df.dropna()\n", "\n", - "# 2. Conversione della colonna 'RainToday' in valori numerici\n", + "# 2. Transform column 'RainToday' into numerical values\n", "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", "\n", - "# 3. Selezione delle feature \n", + "# 3. Feature selection \n", "features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am']\n", "\n", "X = df[features]\n", @@ -105,27 +105,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Suddivisione del Dataset in Training e Test \n", + "### 3. Splitting the Dataset into Training and Test Sets \n", "\n", - "Per addestrare e valutare correttamente il modello, dividiamo il dataset in due parti: \n", + "To properly train and evaluate the model, we split the dataset into two parts: \n", "\n", - "- **X (feature)**: contiene le informazioni che useremo per fare previsioni, come **temperatura** e **umidità**. \n", - "- **y (target)**: rappresenta la variabile che vogliamo prevedere, ovvero se pioverà (*Rain*) o meno (*NoRain*). \n", + "- **X (features)**: Contains the information we will use for predictions, such as **temperature** and **humidity**. \n", + "- **y (target)**: Represents the variable we want to predict, i.e., whether it will rain (*Rain*) or not (*NoRain*). \n", "\n", - "Dividiamo i dati in **training set** (80%) e **test set** (20%) per i seguenti motivi: \n", + "We split the data into **80% training set** and **20% test set** for the following reasons: \n", "\n", - "1. **Addestramento del modello** \n", - " - L'80% dei dati viene usato per insegnare al modello a riconoscere i pattern tra le feature e il target. \n", + "1. **Model Training** \n", + " - 80% of the data is used to teach the model to recognize patterns between features and the target variable. \n", "\n", - "2. **Valutazione del modello** \n", - " - Il restante 20% dei dati non viene usato nell'addestramento, ma serve per testare il modello su dati mai visti prima. \n", - " - Questo ci permette di capire se il modello è davvero in grado di fare previsioni accurate su nuovi dati. \n", + "2. **Model Evaluation** \n", + " - The remaining 20% of the data is not used in training but serves to test the model on unseen data. \n", + " - This helps us understand whether the model can make accurate predictions on new data. \n", "\n", - "3. **Evitare overfitting** \n", - " - Se testassimo il modello sugli stessi dati con cui è stato addestrato, potremmo ottenere risultati ingannevolmente buoni, perché il modello li avrebbe semplicemente memorizzati. \n", - " - Usare dati di test separati aiuta a verificare se il modello può generalizzare le sue previsioni a nuovi dati reali. \n", + "3. **Avoid Overfitting** \n", + " - If we tested the model on the same data it was trained on, we might get deceptively good results, as the model would have simply memorized them. \n", + " - Using separate test data helps verify whether the model can generalize its predictions to real-world data. \n", "\n", - "Questa suddivisione è un passaggio fondamentale per costruire un modello affidabile e capace di fare previsioni corrette su dati che non ha mai visto prima." + "This split is a crucial step in building a reliable model capable of making accurate predictions on unseen data." ] }, { @@ -136,26 +136,26 @@ "source": [ "from sklearn.model_selection import train_test_split\n", "\n", - "# Suddivisione dei dati (80% training, 20% testing)\n", + "# Dataset split (80% training, 20% testing)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "print(f\"Dimensioni del dataset di training: {len(X_train)}\")\n", - "print(f\"Dimensioni del dataset di test: {len(X_test)}\")" + "print(f\"Training dataset dimensions: {len(X_train)}\")\n", + "print(f\"Test dataset dimensions: {len(X_test)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Creazione e Addestramento del Modello \n", + "### 4. Building and Training the Model \n", "\n", - "Ora che abbiamo preparato i dati, possiamo costruire e addestrare un modello di Machine Learning. Per questo utilizzeremo un classificatore chiamato [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), una delle tecniche più utilizzate per problemi di classificazione. \n", + "Now that we have prepared the data, we can build and train a Machine Learning model. For this, we will use a classifier called [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), one of the most widely used techniques for classification problems. \n", "\n", - "#### Perché usiamo **Random Forest**? \n", - "- È un modello basato su **alberi decisionali**, che suddivide i dati in più passaggi per prendere decisioni accurate. \n", - "- È **robusto** e gestisce bene sia dati numerici che categoriali. \n", - "- È meno sensibile ai dati rumorosi rispetto a un singolo albero decisionale, perché combina più alberi per migliorare la precisione. \n", + "#### Why use **Random Forest**? \n", + "- It is a model based on **decision trees**, which divide the data into multiple steps to make accurate decisions. \n", + "- It is **robust** and works well with both numerical and categorical data. \n", + "- It is less sensitive to noisy data than a single decision tree because it combines multiple trees to improve accuracy. \n", "\n", - "Il modello verrà addestrato utilizzando i dati di **temperatura** e **umidità** per prevedere se ci sarà **pioggia** o meno. Dopo l'addestramento, lo testeremo su dati nuovi per valutarne l'accuratezza." + "The model will be trained using **temperature** and **humidity** data to predict whether there will be **rain** or not. After training, we will test it on new data to evaluate its accuracy." ] }, { @@ -166,7 +166,7 @@ "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", - "# Creazione e addestramento del modello\n", + "# Create and train the model\n", "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", "model.fit(X_train, y_train)" ] @@ -175,21 +175,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 5. Valutazione del Modello \n", + "### 5. Model Evaluation \n", "\n", - "Dopo aver addestrato il modello, dobbiamo verificare quanto è accurato nelle previsioni. Per farlo, calcoliamo **l'accuratezza** e altre metriche di valutazione. \n", + "After training the model, we need to verify how accurate its predictions are. To do this, we will calculate **accuracy** and other evaluation metrics. \n", "\n", - "#### Perché è importante valutare il modello? \n", - "Un modello di Machine Learning non è utile se non sappiamo quanto sia affidabile. La valutazione ci aiuta a capire: \n", - "- **Se il modello sta imparando correttamente dai dati** o se sta solo memorizzando le risposte (overfitting). \n", - "- **Se può essere utilizzato su dati nuovi** e fare previsioni realistiche. \n", + "#### Why is model evaluation important? \n", + "A Machine Learning model is not useful if we do not know how reliable it is. Evaluation helps us understand: \n", + "- **Whether the model is learning correctly from the data** or simply memorizing answers (overfitting). \n", + "- **Whether it can be used on new data** and make realistic predictions. \n", "\n", - "#### Matrice di Confusione \n", - "Oltre all'accuratezza, utilizzeremo la **matrice di confusione**, un metodo visivo che mostra dove il modello fa previsioni corrette e dove sbaglia. \n", - "- Ci aiuta a individuare **falsi positivi** e **falsi negativi**, che sono errori critici in molti scenari reali. \n", - "- È utile per migliorare il modello, ad esempio regolando soglie di decisione o bilanciando i dati di input. \n", + "#### Confusion Matrix \n", + "Besides accuracy, we will use the **confusion matrix**, a visual method that shows where the model makes correct predictions and where it makes mistakes. \n", + "- It helps identify **false positives** and **false negatives**, which are critical errors in many real-world scenarios. \n", + "- It is useful for improving the model, for example, by adjusting decision thresholds or balancing input data. \n", "\n", - "Con queste analisi possiamo capire se il nostro modello è pronto per essere utilizzato o se necessita di miglioramenti." + "With these analyses, we can determine whether our model is ready for use or needs improvement." ] }, { @@ -200,16 +200,16 @@ "source": [ "from sklearn.metrics import accuracy_score, f1_score, classification_report\n", "\n", - "# Predizioni sul set di test\n", + "# Prediction on test set\n", "y_pred = model.predict(X_test)\n", "\n", - "# Calcolo dell'accuratezza ed f1-score\n", + "# Compute accuracy and f1-score\n", "accuracy = accuracy_score(y_test, y_pred)\n", "f1 = f1_score(y_test, y_pred, average='weighted')\n", - "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", - "print(f\"F1-score del modello: {f1:.2f}\")\n", + "print(f\"Accuracy of the model: {accuracy:.2f}\")\n", + "print(f\"F1-score of the model: {f1:.2f}\")\n", "\n", - "# Report di classificazione\n", + "# Print classification report\n", "print(classification_report(y_test, y_pred))" ] }, @@ -223,18 +223,18 @@ "import matplotlib.pyplot as plt\n", "from sklearn.metrics import confusion_matrix\n", "\n", - "# Creiamo una heatmap con Seaborn\n", + "# Create a heatmap with Seaborn\n", "target_names = [\"No Rain\", \"Rain\"]\n", "cm = confusion_matrix(y_test, y_pred)\n", "plt.figure(figsize=(6, 4))\n", "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", "\n", - "# Aggiungiamo i titoli\n", + "# Add titles\n", "plt.xlabel(\"Predicted Label\")\n", "plt.ylabel(\"True Label\")\n", "plt.title(\"Confusion Matrix\")\n", "\n", - "# Mostriamo il grafico\n", + "# Show the plot\n", "plt.show()" ] }, @@ -242,17 +242,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Conclusione (Parte 1) \n", + "### Conclusion (Part 1) \n", "\n", - "In questa prima parte, abbiamo seguito un processo step-by-step per costruire un modello di Machine Learning in grado di prevedere la pioggia. Ecco cosa abbiamo fatto: \n", + "In this first part, we followed a step-by-step process to build a Machine Learning model capable of predicting rain. Here’s what we did: \n", "\n", - "1. **Caricato il dataset meteo** per analizzare temperatura, umidità e altre variabili. \n", - "2. **Pulito e preparato i dati**, gestendo eventuali valori mancanti e trasformando la variabile target in un formato comprensibile al modello. \n", - "3. **Suddiviso il dataset** in dati di training (80%) e di test (20%) per addestrare e valutare il modello correttamente. \n", - "4. **Creato un modello di classificazione** utilizzando **RandomForestClassifier**, un algoritmo potente e robusto. \n", - "5. **Valutato le prestazioni del modello** calcolando l’accuratezza e analizzando la matrice di confusione per identificare eventuali errori. \n", + "1. **Loaded the weather dataset** to analyze temperature, humidity, and other variables. \n", + "2. **Cleaned and prepared the data**, handling missing values and converting the target variable into a format the model can understand. \n", + "3. **Split the dataset** into training (80%) and test (20%) sets to properly train and evaluate the model. \n", + "4. **Built a classification model** using **RandomForestClassifier**, a powerful and robust algorithm. \n", + "5. **Evaluated the model’s performance** by calculating accuracy and analyzing the confusion matrix to identify errors. \n", "\n", - "Ora che abbiamo costruito il modello base, nella prossima parte esploreremo come integrare **MLflow** per tracciare gli esperimenti e migliorare ulteriormente le prestazioni." + "Now that we have built the base model, in the next part, we will explore how to integrate **MLflow** to track experiments and further improve performance." ] }, { @@ -266,55 +266,55 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tech: Installazione e Configurazione di MLflow \n", + "## Tech: Installing and Configuring MLflow \n", "\n", - "### Obiettivo \n", - "Configurare un'istanza locale di **MLflow** per registrare esperimenti, monitorare le metriche e gestire i modelli di Machine Learning in modo organizzato. \n", + "### Objective \n", + "Set up a local **MLflow** instance to log experiments, monitor metrics, and manage Machine Learning models in an organized way. \n", "\n", - "### 1. Avviare MLflow \n", + "### 1. Starting MLflow \n", "\n", - "Per avviare MLflow in locale, esegui il seguente comando nel terminale: \n", + "To start MLflow locally, run the following command in the terminal: \n", "\n", "```bash\n", " mlflow ui\n", "```\n", "\n", - "Dopo averlo avviato, l'interfaccia grafica sarà accessibile all'indirizzo: \n", + "Once started, the graphical interface will be accessible at: \n", "\n", "```\n", " http://127.0.0.1:5000\n", "```\n", "\n", - "Questa configurazione permette di **salvare esperimenti e modelli localmente**, consentendo di tracciare le diverse versioni dei modelli, confrontare le metriche di valutazione e ottimizzare i processi di addestramento. \n", + "This setup allows **saving experiments and models locally**, enabling tracking of different model versions, comparing evaluation metrics, and optimizing training processes. \n", "\n", - "Nelle prossime sezioni, vedremo come registrare parametri, metriche e modelli direttamente all'interno di MLflow." + "In the next sections, we will see how to log parameters, metrics, and models directly within MLflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### MLflow in Produzione \n", + "### MLflow in Production \n", "\n", - "In ambienti di produzione e presso i clienti, **MLflow non viene eseguito in locale**, ma viene integrato in un'infrastruttura più solida e scalabile. Questo evita problemi legati alla gestione manuale degli esperimenti e alla persistenza dei dati. \n", + "In production environments and for clients, **MLflow is not run locally** but is integrated into a more robust and scalable infrastructure. This prevents issues related to manual experiment management and data persistence. \n", "\n", - "Le soluzioni più comuni includono: \n", + "Common solutions include: \n", "\n", "- **Docker Compose** \n", - " - MLflow viene avviato utilizzando un file `docker-compose.yml`, che configura un database backend e uno storage remoto per salvare gli esperimenti. \n", - " - Questo approccio è utile per ambienti controllati, dove è necessario un setup rapido e replicabile. \n", - " - Un esempio di implementazione è disponibile nella repository interna: \n", + " - MLflow is started using a `docker-compose.yml` file, which configures a backend database and remote storage for saving experiments. \n", + " - This approach is useful for controlled environments where a quick and reproducible setup is needed. \n", + " - An example implementation is available in the internal repository: \n", " [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main). \n", "\n", "- **Kubernetes (K8s)** \n", - " - MLflow viene distribuito su un **cluster Kubernetes**, consentendo una gestione scalabile e centralizzata degli esperimenti. \n", - " - Questo approccio è ideale per ambienti enterprise, dove sono richiesti elevati livelli di affidabilità, sicurezza e scalabilità. \n", + " - MLflow is deployed on a **Kubernetes cluster**, allowing scalable and centralized experiment management. \n", + " - This approach is ideal for enterprise environments that require high levels of reliability, security, and scalability. \n", "\n", - "Entrambe le soluzioni si basano su un'**architettura multi-container**, che include: \n", - "- **Un database persistente** (es. PostgreSQL o MySQL) per memorizzare i metadati degli esperimenti. \n", - "- **Uno storage S3 o MinIO** per salvare i modelli e gli artefatti, garantendo una gestione sicura e scalabile dei dati. \n", + "Both solutions rely on a **multi-container architecture**, which includes: \n", + "- **A persistent database** (e.g., PostgreSQL or MySQL) to store experiment metadata. \n", + "- **An S3 or MinIO storage** to save models and artifacts, ensuring secure and scalable data management. \n", "\n", - "Questi approcci assicurano che MLflow possa essere utilizzato in modo affidabile in produzione, integrandosi con infrastrutture cloud o on-premise per una gestione efficace dei modelli di Machine Learning." + "These approaches ensure that MLflow can be reliably used in production, integrating with cloud or on-premise infrastructures for effective Machine Learning model management." ] }, { @@ -328,21 +328,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Parte 2: Integrazione con MLflow \n", + "## Part 2: MLflow Integration \n", "\n", - "Ora estenderemo il codice esistente per **tracciare i nostri esperimenti** utilizzando **MLflow**. Questo ci permetterà di monitorare il processo di addestramento del modello, confrontare le diverse configurazioni e gestire le versioni del modello in modo strutturato. \n", + "Now, we will extend the existing code to **track our experiments** using **MLflow**. This will allow us to monitor the model training process, compare different configurations, and manage model versions in a structured way. \n", "\n", - "### Perché integrare MLflow? \n", - "Con MLflow possiamo: \n", - "- **Registrare i parametri di addestramento** (es. `n_estimators` per Random Forest) per confrontare diverse configurazioni. \n", - "- **Salvare le metriche di valutazione** (es. accuratezza, F1-score) per monitorare le prestazioni del modello. \n", - "- **Archiviare il modello addestrato** per poterlo ricaricare facilmente in futuro e riutilizzarlo senza doverlo riaddestrare. \n", + "### Why integrate MLflow? \n", + "With MLflow, we can: \n", + "- **Log training parameters** (e.g., `n_estimators` for Random Forest) to compare different configurations. \n", + "- **Save evaluation metrics** (e.g., accuracy, F1-score) to monitor the model’s performance. \n", + "- **Store the trained model** to easily reload and reuse it in the future without retraining. \n", "\n", - "### Obiettivo \n", - "Integrare MLflow nel codice esistente per **tracciare e versionare i modelli**, registrando parametri, metriche e artefatti in modo strutturato. \n", + "### Objective \n", + "Integrate MLflow into the existing code to **track and version models**, logging parameters, metrics, and artifacts in a structured way. \n", "\n", - "### 1. Configurazione di MLflow \n", - "Prima di iniziare a tracciare gli esperimenti, impostiamo le variabili necessarie per utilizzare MLflow in questo progetto." + "### 1. MLflow Configuration \n", + "Before we start tracking experiments, let's set up the necessary variables to use MLflow in this project. " ] }, { @@ -354,7 +354,7 @@ "import mlflow\n", "import mlflow.sklearn\n", "\n", - "# Impostiamo un nome per l'esperimento\n", + "# Set name of the experiment and tracking URI of local instance\n", "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n", "mlflow.set_experiment(\"weather_classification_experiment\")" ] @@ -363,24 +363,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Verifichiamo che dalla [nostra interfaccia grafica](http://127.0.0.1:5000) il nuovo esperimento sia visibile." + "We verify that from our [web interface](http://127.0.0.1:5000), the new experiment is visible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Registrazione di Parametri, Metriche e Modello \n", + "### 2. Logging Parameters, Metrics, and Model \n", "\n", - "Con **MLflow**, possiamo salvare e tracciare automaticamente diverse informazioni durante l'addestramento del modello. Questo ci aiuta a confrontare le performance tra diverse configurazioni e a recuperare facilmente i modelli migliori. \n", + "With **MLflow**, we can automatically save and track various information during model training. This helps compare performances across different configurations and easily retrieve the best models. \n", "\n", - "Ecco cosa possiamo registrare: \n", + "Here’s what we can log: \n", "\n", - "- **Parametri** → Valori utilizzati per configurare il modello, come `n_estimators` (numero di alberi in Random Forest) e altri iperparametri. \n", - "- **Metriche** → Indicatori delle prestazioni del modello, come **accuratezza**, **F1-score**, precisione e recall. \n", - "- **Modello** → La versione del modello addestrato, che potrà essere ricaricata e riutilizzata senza bisogno di riaddestramento. \n", + "- **Parameters** → Values used to configure the model, such as `n_estimators` (number of trees in Random Forest) and other hyperparameters. \n", + "- **Metrics** → Performance indicators of the model, such as **accuracy**, **F1-score**, precision, and recall. \n", + "- **Model** → The trained model version, which can be reloaded and reused without retraining. \n", "\n", - "Registrando questi elementi, possiamo analizzare e confrontare le diverse versioni del modello in modo strutturato e riproducibile." + "By logging these elements, we can analyze and compare different model versions in a structured and reproducible way." ] }, { @@ -389,97 +389,97 @@ "metadata": {}, "outputs": [], "source": [ - "# Usando il modello creato nella Parte 1\n", + "# Use the same type of model used in Part 1\n", "import os\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import accuracy_score, f1_score\n", "from mlflow.models.signature import infer_signature\n", "\n", - "# Eseguiremo 4 esperimenti per addestrare più modelli \n", + "# Execute 4 experiments to train multiple models \n", "n_estimators = [1, 10, 100, 500]\n", "\n", "for n_e in n_estimators:\n", - " # Creiamo una nuova run MLflow\n", + " # Create a new MLFlow run\n", " with mlflow.start_run():\n", " # Log Param\n", " mlflow.log_param(\"n_estimators\", n_e)\n", "\n", - " # Creazione del modello\n", + " # Create and train the model instance\n", " rf_model = RandomForestClassifier(n_estimators=n_e, random_state=42)\n", " rf_model.fit(X_train, y_train)\n", "\n", - " # Calcolo metriche\n", + " # Compute metrics\n", " y_pred = rf_model.predict(X_test)\n", " accuracy = accuracy_score(y_test, y_pred)\n", " f1 = f1_score(y_test, y_pred, average='weighted')\n", " mlflow.log_metric(\"accuracy\", accuracy)\n", " mlflow.log_metric(\"f1\", f1)\n", "\n", - " # Creiamo una heatmap con Seaborn\n", + " # Create heatmap with Seaborn\n", " target_names = [\"No Rain\", \"Rain\"]\n", " cm = confusion_matrix(y_test, y_pred)\n", " plt.figure(figsize=(6, 4))\n", " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", "\n", - " # Aggiungiamo i titoli\n", + " # Add titles\n", " plt.xlabel(\"Predicted Label\")\n", " plt.ylabel(\"True Label\")\n", " plt.title(\"Confusion Matrix\")\n", "\n", - " # Salviamo l'immagine come PNG\n", + " # Save the plot as PNG\n", " if not os.path.exists(\"dev/\"):\n", " os.makedirs(\"dev/\")\n", " plt.savefig(\"dev/confusion_matrix.png\")\n", " plt.close()\n", - " # Salviamo la confusion matrix su MLFlow come artifatto\n", + " # Save the confusion matrix on MLFlow as artifact\n", " mlflow.log_artifact(\"dev/confusion_matrix.png\")\n", "\n", - " # Salviamo il modello\n", + " # Save the model on MLFlow\n", " example_dict = {'MinTemp': 1.1, 'MaxTemp': 1.1, 'Humidity3pm': 1.1, 'Humidity9am': 1.1}\n", " signature = infer_signature(model_input=example_dict)\n", " mlflow.sklearn.log_model(rf_model, \"random_forest_model\", signature=signature)\n", "\n", - " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" + " print(f\"Experiment finished. Registered accuracy: {accuracy:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Visualizzazione e Confronto dei Risultati \n", + "### 3. Viewing and Comparing Results \n", "\n", - "Dopo aver registrato i parametri, le metriche e i modelli, possiamo utilizzare **MLflow** per esplorare e confrontare i diversi esperimenti. \n", + "After logging parameters, metrics, and models, we can use **MLflow** to explore and compare different experiments. \n", "\n", - "MLflow fornisce un’interfaccia web accessibile all’indirizzo: \n", + "MLflow provides a web interface accessible at: \n", "\n", "```bash\n", " http://127.0.0.1:5000\n", "```\n", "\n", - "Accedendo a questa interfaccia, nella sezione **Experiments**, sarà possibile: \n", - "- **Esaminare i parametri** utilizzati in ogni esperimento. \n", - "- **Confrontare le metriche** tra diverse configurazioni di modello. \n", - "- **Visualizzare e scaricare i modelli salvati**, facilitando il riutilizzo e il deployment. \n", + "By accessing this interface, in the **Experiments** section, it will be possible to: \n", + "- **Examine the parameters** used in each experiment. \n", + "- **Compare metrics** across different model configurations. \n", + "- **View and download saved models**, making reuse and deployment easier. \n", "\n", - "Questa funzionalità consente di monitorare l’evoluzione delle performance del modello e di identificare rapidamente le configurazioni migliori." + "This feature allows monitoring the model's performance evolution and quickly identifying the best configurations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Caricare un Modello Salvato con MLflow \n", + "### 4. Loading a Saved Model with MLflow \n", "\n", - "MLflow consente di salvare e ricaricare facilmente i modelli addestrati, evitando la necessità di riaddestrarli ogni volta. \n", + "MLflow allows saving and reloading trained models easily, avoiding the need to retrain them every time. \n", "\n", - "Per recuperare un modello salvato in MLflow, è necessario copiare **l'ID della run** dell'esperimento eseguito. Questo ID identifica in modo univoco ogni esperimento registrato e permette di caricare il modello corrispondente per effettuare previsioni future. \n", + "To retrieve a saved model in MLflow, you need to copy the **run ID** of the executed experiment. This ID uniquely identifies each logged experiment and allows loading the corresponding model for future predictions. \n", "\n", - "Questa funzionalità è particolarmente utile per: \n", - "- **Riutilizzare un modello addestrato** senza dover ripetere il training. \n", - "- **Confrontare diverse versioni** di un modello per scegliere quella più performante. \n", - "- **Integrare il modello in applicazioni o API**, senza doverlo ricostruire da zero. \n", + "This feature is particularly useful for: \n", + "- **Reusing a trained model** without repeating the training process. \n", + "- **Comparing different model versions** to choose the most effective one. \n", + "- **Integrating the model into applications or APIs**, without rebuilding it from scratch. \n", "\n", - "Nelle prossime sezioni vedremo come eseguire questo processo in pratica." + "In the next sections, we will see how to perform this process in practice." ] }, { @@ -491,45 +491,45 @@ "import mlflow.sklearn\n", "from sklearn.metrics import accuracy_score\n", "\n", - "# Inserisci un run_id reale che trovi nell'interfaccia MLflow\n", - "RUN_ID = \"\"\n", + "# Insert a real run_id you find in MLFlow UI\n", + "RUN_ID = \"\"\n", "\n", "loaded_model = mlflow.sklearn.load_model(f\"runs:/{RUN_ID}/random_forest_model\")\n", "\n", - "# Verifichiamo l'accuratezza\n", + "# Verify accuracy\n", "y_loaded_pred = loaded_model.predict(X_test)\n", "acc_loaded = accuracy_score(y_test, y_loaded_pred)\n", - "print(f\"Accuratezza del modello caricato: {acc_loaded:.2f}\")" + "print(f\"Accuracy of the loaded model: {acc_loaded:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Conclusioni \n", + "## Conclusions \n", "\n", - "In questo laboratorio, abbiamo seguito un processo completo per costruire e monitorare un modello di Machine Learning applicato ai dati meteorologici. In particolare, abbiamo: \n", + "In this lab, we followed a complete process to build and monitor a Machine Learning model applied to weather data. Specifically, we: \n", "\n", - "1. **Creato un modello di classificazione** utilizzando **Scikit-learn**, sfruttando temperatura e umidità per prevedere la pioggia. \n", - "2. **Integrato MLflow** per tracciare i parametri di addestramento, registrare le metriche di valutazione e gestire le versioni del modello in modo strutturato. \n", - "3. **Esplorato e confrontato i risultati** attraverso l’interfaccia di MLflow UI, verificando le diverse configurazioni e caricando un modello salvato per future previsioni. \n", + "1. **Created a classification model** using **Scikit-learn**, leveraging temperature and humidity to predict rain. \n", + "2. **Integrated MLflow** to track training parameters, log evaluation metrics, and manage model versions in a structured way. \n", + "3. **Explored and compared results** through the MLflow UI interface, reviewing different configurations and loading a saved model for future predictions. \n", "\n", - "Questo approccio ci permette di migliorare il processo di sviluppo dei modelli di Machine Learning, rendendolo più organizzato, riproducibile e scalabile." + "This approach allows us to improve the Machine Learning model development process, making it more organized, reproducible, and scalable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Prossimi Passi \n", + "## Next Steps \n", "\n", - "Ora che abbiamo costruito e tracciato il nostro modello, possiamo esplorare ulteriori miglioramenti e integrare il lavoro in un flusso più avanzato. \n", + "Now that we have built and tracked our model, we can explore further improvements and integrate the work into a more advanced workflow. \n", "\n", - "- **Ottimizzazione degli iperparametri**: testare diverse configurazioni di `n_estimators`, `max_depth` e altri parametri del modello, registrando i risultati in **MLflow** per identificare la combinazione migliore. \n", - "- **Automazione con CI/CD**: integrare un sistema di **Continuous Integration/Continuous Deployment (CI/CD)** per addestrare e distribuire automaticamente nuove versioni del modello, riducendo il rischio di errori manuali. \n", - "- **Monitoraggio del modello in produzione**: implementare un sistema di **monitoraggio del drift del modello**, per rilevare eventuali cali di accuratezza nel tempo e decidere quando è necessario riaddestrarlo con nuovi dati. \n", + "- **Hyperparameter Optimization**: Test different configurations of `n_estimators`, `max_depth`, and other model parameters, logging results in **MLflow** to identify the best combination. \n", + "- **Automation with CI/CD**: Integrate a **Continuous Integration/Continuous Deployment (CI/CD)** system to automatically train and deploy new model versions, reducing the risk of manual errors. \n", + "- **Model Monitoring in Production**: Implement a **model drift monitoring** system to detect any drops in accuracy over time and determine when retraining with new data is necessary. \n", "\n", - "Questi passaggi aiutano a trasformare il modello sviluppato in un sistema robusto e affidabile, pronto per essere utilizzato in scenari reali." + "These steps help transform the developed model into a robust and reliable system, ready for real-world applications." ] } ], diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt b/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt deleted file mode 100644 index 2580f85..0000000 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt +++ /dev/null @@ -1,153 +0,0 @@ -alembic==1.14.1 -annotated-types==0.7.0 -anyio==4.8.0 -appnope==0.1.4 -argon2-cffi==23.1.0 -argon2-cffi-bindings==21.2.0 -arrow==1.3.0 -asttokens==3.0.0 -async-lru==2.0.4 -attrs==25.1.0 -babel==2.17.0 -beautifulsoup4==4.13.3 -bleach==6.2.0 -blinker==1.9.0 -cachetools==5.5.2 -certifi==2025.1.31 -cffi==1.17.1 -charset-normalizer==3.4.1 -click==8.1.8 -cloudpickle==3.1.1 -comm==0.2.2 -contourpy==1.3.1 -cycler==0.12.1 -databricks-sdk==0.44.1 -debugpy==1.8.12 -decorator==5.2.1 -defusedxml==0.7.1 -Deprecated==1.2.18 -docker==7.1.0 -exceptiongroup==1.2.2 -executing==2.2.0 -fastjsonschema==2.21.1 -Flask==3.1.0 -fonttools==4.56.0 -fqdn==1.5.1 -gitdb==4.0.12 -GitPython==3.1.44 -google-auth==2.38.0 -graphene==3.4.3 -graphql-core==3.2.6 -graphql-relay==3.2.0 -gunicorn==23.0.0 -h11==0.14.0 -httpcore==1.0.7 -httpx==0.28.1 -idna==3.10 -importlib_metadata==8.5.0 -ipykernel==6.29.5 -ipython==8.33.0 -ipywidgets==8.1.5 -isoduration==20.11.0 -itsdangerous==2.2.0 -jedi==0.19.2 -Jinja2==3.1.5 -joblib==1.4.2 -json5==0.10.0 -jsonpointer==3.0.0 -jsonschema==4.23.0 -jsonschema-specifications==2024.10.1 -jupyter==1.1.1 -jupyter-console==6.6.3 -jupyter-events==0.12.0 -jupyter-lsp==2.2.5 -jupyter_client==8.6.3 -jupyter_core==5.7.2 -jupyter_server==2.15.0 -jupyter_server_terminals==0.5.3 -jupyterlab==4.3.5 -jupyterlab_pygments==0.3.0 -jupyterlab_server==2.27.3 -jupyterlab_widgets==3.0.13 -kiwisolver==1.4.8 -Mako==1.3.9 -Markdown==3.7 -MarkupSafe==3.0.2 -matplotlib==3.10.1 -matplotlib-inline==0.1.7 -mistune==3.1.2 -mlflow==2.20.3 -mlflow-skinny==2.20.3 -nbclient==0.10.2 -nbconvert==7.16.6 -nbformat==5.10.4 -nest-asyncio==1.6.0 -notebook==7.3.2 -notebook_shim==0.2.4 -numpy==2.2.3 -opentelemetry-api==1.30.0 -opentelemetry-sdk==1.30.0 -opentelemetry-semantic-conventions==0.51b0 -overrides==7.7.0 -packaging==24.2 -pandas==2.2.3 -pandocfilters==1.5.1 -parso==0.8.4 -pexpect==4.9.0 -pillow==11.1.0 -platformdirs==4.3.6 -prometheus_client==0.21.1 -prompt_toolkit==3.0.50 -protobuf==5.29.3 -psutil==7.0.0 -ptyprocess==0.7.0 -pure_eval==0.2.3 -pyarrow==19.0.1 -pyasn1==0.6.1 -pyasn1_modules==0.4.1 -pycparser==2.22 -pydantic==2.10.6 -pydantic_core==2.27.2 -Pygments==2.19.1 -pyparsing==3.2.1 -python-dateutil==2.9.0.post0 -python-json-logger==3.2.1 -pytz==2025.1 -PyYAML==6.0.2 -pyzmq==26.2.1 -referencing==0.36.2 -requests==2.32.3 -rfc3339-validator==0.1.4 -rfc3986-validator==0.1.1 -rpds-py==0.23.1 -rsa==4.9 -scikit-learn==1.6.1 -scipy==1.15.2 -seaborn==0.13.2 -Send2Trash==1.8.3 -six==1.17.0 -smmap==5.0.2 -sniffio==1.3.1 -soupsieve==2.6 -SQLAlchemy==2.0.38 -sqlparse==0.5.3 -stack-data==0.6.3 -terminado==0.18.1 -threadpoolctl==3.5.0 -tinycss2==1.4.0 -tomli==2.2.1 -tornado==6.4.2 -traitlets==5.14.3 -types-python-dateutil==2.9.0.20241206 -typing_extensions==4.12.2 -tzdata==2025.1 -uri-template==1.3.0 -urllib3==2.3.0 -wcwidth==0.2.13 -webcolors==24.11.1 -webencodings==0.5.1 -websocket-client==1.8.0 -Werkzeug==3.1.3 -widgetsnbextension==4.0.13 -wrapt==1.17.2 -zipp==3.21.0 diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb deleted file mode 100644 index 848696a..0000000 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb +++ /dev/null @@ -1,154 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# URL del dataset (sostituiscilo con il tuo link)\n", - "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", - "# Carica il dataset\n", - "df = pd.read_csv(url)\n", - "\n", - "# 1. Rimozione dei valori mancanti\n", - "df = df.dropna()\n", - "# 2. Conversione della colonna 'RainToday' in valori numerici\n", - "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", - "# 3. Selezione delle feature \n", - "features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am']\n", - "X = df[features]\n", - "y = df['RainToday']\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "# Suddivisione dei dati (80% training, 20% testing)\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "print(f\"Dimensioni del dataset di training: {len(X_train)}\")\n", - "print(f\"Dimensioni del dataset di test: {len(X_test)}\")\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "# Creazione e addestramento del modello\n", - "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", - "model.fit(X_train, y_train)\n", - "\n", - "from sklearn.metrics import accuracy_score, classification_report\n", - "# Predizioni sul set di test\n", - "y_pred = model.predict(X_test)\n", - "# Calcolo dell'accuratezza\n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", - "# Report di classificazione\n", - "print(classification_report(y_test, y_pred))\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import confusion_matrix\n", - "# Creiamo una heatmap con Seaborn\n", - "target_names = [\"No Rain\", \"Rain\"]\n", - "cm = confusion_matrix(y_test, y_pred)\n", - "plt.figure(figsize=(6, 4))\n", - "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", - "# Aggiungiamo i titoli\n", - "plt.xlabel(\"Predicted Label\")\n", - "plt.ylabel(\"True Label\")\n", - "plt.title(\"Confusion Matrix\")\n", - "# Mostriamo il grafico\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow\n", - "import mlflow.sklearn\n", - "\n", - "# Impostiamo un nome per l'esperimento\n", - "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n", - "mlflow.set_experiment(\"weather_classification_experiment\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Usando il modello creato nella Parte 1\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "n_estimators = 100\n", - "\n", - "# Creiamo una nuova run MLflow\n", - "with mlflow.start_run():\n", - " # Log Param\n", - " mlflow.log_param(\"n_estimators\", n_estimators)\n", - "\n", - " # Creazione del modello\n", - " rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n", - " rf_model.fit(X_train, y_train)\n", - "\n", - " # Calcolo metriche\n", - " y_pred = rf_model.predict(X_test)\n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " mlflow.log_metric(\"accuracy\", accuracy)\n", - "\n", - " # Salviamo il modello\n", - " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", - "\n", - " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow.sklearn\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "# Inserisci un run_id reale che trovi nell'interfaccia MLflow\n", - "RUN_ID = \"\"\n", - "\n", - "loaded_model = mlflow.sklearn.load_model(f\"runs:/{RUN_ID}/random_forest_model\")\n", - "\n", - "# Verifichiamo l'accuratezza\n", - "y_loaded_pred = loaded_model.predict(X_test)\n", - "acc_loaded = accuracy_score(y_test, y_loaded_pred)\n", - "print(f\"Accuratezza del modello caricato: {acc_loaded:.2f}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.10 ('venv': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "vscode": { - "interpreter": { - "hash": "a8e57e261110579b348b220fe1c71aea6cd1a8cd238281550a2a6c068185ef06" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 895da8b00241b0c3ab83c0a5051a381cee897249 Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 12 Mar 2025 13:32:52 +0100 Subject: [PATCH 08/70] fix MD lint --- AI/Academy/1-Intro-To-ML-And-MLOps/README.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md index 76da717..3928205 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md @@ -16,17 +16,20 @@ The first part focuses on Scikit-learn and the weather dataset. The second part Before we begin, we need to ensure we have everything required to run the lab properly. -#### **Requirements** +### **Requirements** This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well. In order to execute this laboratory, you will be asked to install a set of tools common in Machine Learning field: + - [**scikit-learn**](https://scikit-learn.org/stable/index.html): Machine learning library with modeling and evaluation tools. - [**pandas**](https://pandas.pydata.org/): Framework for data manipulation and analysis in Python. - [**seaborn**](https://seaborn.pydata.org/): Data visualization library based on Matplotlib. - [**mlflow**](https://mlflow.org/): Tool for tracking and managing machine learning experiments. ### 1. Clone the Repository + To start, clone the lab repository by running the following command in the terminal: + ```sh git clone https://github.com/kiratech/labs.git ``` @@ -44,6 +47,7 @@ Then, checkout the `lab` branch: ```sh git checkout academy-ai ``` + This folder contains resources related to AI-themed labs. ### 3. Navigate to the 1-Intro-To-ML-And-MLOps Folder @@ -57,6 +61,7 @@ Go to the folder of the first lab: ### 4. Open the Project in VSCode At this point, open VSCode from the file explorer or by running the command: + ```sh code . ``` @@ -72,6 +77,7 @@ Using the **VSCode** terminal, create a virtual environment: ``` Activate the Virtual Environment: + ```sh conda activate lab_env ``` @@ -82,18 +88,21 @@ You should see the `(lab_env)` prefix in the terminal, indicating that the virtu Besides the default packages in the environment, additional libraries may be needed for the lab. Before installing them, it's always a good practice to update `pip` to avoid compatibility issues: + ```sh pip install --upgrade pip ``` Now, install some essential packages for data analysis and machine learning: + ```sh pip install scikit-learn pandas seaborn mlflow ipykernel ``` Verify that the packages were installed correctly with: + ```sh conda list ``` -At this point, you can proceed with the `lab.ipynb` file. \ No newline at end of file +At this point, you can proceed with the `lab.ipynb` file. From 2b83613372b8879851449e85e7156a33ffa84c5c Mon Sep 17 00:00:00 2001 From: Ale3e Date: Thu, 13 Mar 2025 17:10:48 +0100 Subject: [PATCH 09/70] Change folder structure --- Academy/AI/1-Intro-To-ML-And-MLOps/.DS_Store | Bin 0 -> 6148 bytes .../AI}/1-Intro-To-ML-And-MLOps/.gitignore | 0 .../AI}/1-Intro-To-ML-And-MLOps/README.md | 0 .../AI}/1-Intro-To-ML-And-MLOps/lab.ipynb | 0 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Academy/AI/1-Intro-To-ML-And-MLOps/.DS_Store rename {AI/Academy => Academy/AI}/1-Intro-To-ML-And-MLOps/.gitignore (100%) rename {AI/Academy => Academy/AI}/1-Intro-To-ML-And-MLOps/README.md (100%) rename {AI/Academy => Academy/AI}/1-Intro-To-ML-And-MLOps/lab.ipynb (100%) diff --git a/Academy/AI/1-Intro-To-ML-And-MLOps/.DS_Store b/Academy/AI/1-Intro-To-ML-And-MLOps/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..eff540416d709edfdecfcc302f2f9ff1093ebf89 GIT binary patch literal 6148 zcmeH~F>b>!3`IW^4*{}x?5HIN$PI)bIYBQ_G;XIN8G>fk(fdhpsnazF0T;svrMTPG_t%jv Date: Fri, 4 Apr 2025 09:51:28 +0200 Subject: [PATCH 10/70] Update README.md --- Academy/AI/1-Intro-To-ML-And-MLOps/README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/Academy/AI/1-Intro-To-ML-And-MLOps/README.md b/Academy/AI/1-Intro-To-ML-And-MLOps/README.md index 3928205..e4c0705 100644 --- a/Academy/AI/1-Intro-To-ML-And-MLOps/README.md +++ b/Academy/AI/1-Intro-To-ML-And-MLOps/README.md @@ -58,15 +58,7 @@ Go to the folder of the first lab: cd AI/Academy/1-Intro-To-ML-And-MLOps ``` -### 4. Open the Project in VSCode - -At this point, open VSCode from the file explorer or by running the command: - -```sh - code . -``` - -### 5. Create a Virtual Environment +### 4. Create a Virtual Environment A virtual environment allows you to isolate the project's dependencies from the system-wide ones. @@ -84,7 +76,7 @@ Activate the Virtual Environment: You should see the `(lab_env)` prefix in the terminal, indicating that the virtual environment is active. -### 6. Install Required Packages +### 5. Install Required Packages Besides the default packages in the environment, additional libraries may be needed for the lab. Before installing them, it's always a good practice to update `pip` to avoid compatibility issues: @@ -106,3 +98,11 @@ Verify that the packages were installed correctly with: ``` At this point, you can proceed with the `lab.ipynb` file. + +### 6. Open the Project in VSCode + +At this point, open VSCode from the file explorer or by running the command: + +```sh + code . +``` From c056e32b3172b35967c9b7d3ede4fb12c0a2a15b Mon Sep 17 00:00:00 2001 From: rndg <37615999+rndg@users.noreply.github.com> Date: Fri, 4 Apr 2025 09:56:09 +0200 Subject: [PATCH 11/70] Update README.md --- Academy/AI/1-Intro-To-ML-And-MLOps/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Academy/AI/1-Intro-To-ML-And-MLOps/README.md b/Academy/AI/1-Intro-To-ML-And-MLOps/README.md index e4c0705..648588c 100644 --- a/Academy/AI/1-Intro-To-ML-And-MLOps/README.md +++ b/Academy/AI/1-Intro-To-ML-And-MLOps/README.md @@ -55,7 +55,7 @@ This folder contains resources related to AI-themed labs. Go to the folder of the first lab: ```sh - cd AI/Academy/1-Intro-To-ML-And-MLOps + cd Academy/AI/1-Intro-To-ML-And-MLOps ``` ### 4. Create a Virtual Environment From 69cee4fef3a07373c50159e92a2ce637023ce8d6 Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 23 Apr 2025 22:40:02 +0200 Subject: [PATCH 12/70] first skeleton for part 2 --- Academy/AI/2-ML-Model-Lifecycle/.DS_Store | Bin 0 -> 6148 bytes Academy/AI/2-ML-Model-Lifecycle/.gitignore | 179 ++++++++++++++++++ .../.pre-commit-config.yaml | 23 +++ Academy/AI/2-ML-Model-Lifecycle/Makefile | 22 +++ Academy/AI/2-ML-Model-Lifecycle/README.md | 4 + .../AI/2-ML-Model-Lifecycle/environment.yaml | 17 ++ .../2-ML-Model-Lifecycle/notebooks/.DS_Store | Bin 0 -> 6148 bytes .../notebooks/01_explore_and_train.ipynb | 93 +++++++++ Academy/AI/2-ML-Model-Lifecycle/noxfile.py | 40 ++++ .../AI/2-ML-Model-Lifecycle/pyproject.toml | 26 +++ .../AI/2-ML-Model-Lifecycle/src/__init__.py | 0 Academy/AI/2-ML-Model-Lifecycle/src/config.py | 12 ++ Academy/AI/2-ML-Model-Lifecycle/src/data.py | 8 + .../AI/2-ML-Model-Lifecycle/src/monitor.py | 23 +++ .../AI/2-ML-Model-Lifecycle/src/pipeline.py | 45 +++++ .../AI/2-ML-Model-Lifecycle/src/retrain.py | 25 +++ Academy/AI/2-ML-Model-Lifecycle/src/serve.py | 25 +++ Academy/AI/2-ML-Model-Lifecycle/src/train.py | 35 ++++ .../2-ML-Model-Lifecycle/tests/test_data.py | 15 ++ .../2-ML-Model-Lifecycle/tests/test_train.py | 7 + 20 files changed, 599 insertions(+) create mode 100644 Academy/AI/2-ML-Model-Lifecycle/.DS_Store create mode 100644 Academy/AI/2-ML-Model-Lifecycle/.gitignore create mode 100644 Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml create mode 100644 Academy/AI/2-ML-Model-Lifecycle/Makefile create mode 100644 Academy/AI/2-ML-Model-Lifecycle/README.md create mode 100644 Academy/AI/2-ML-Model-Lifecycle/environment.yaml create mode 100644 Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store create mode 100644 Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb create mode 100644 Academy/AI/2-ML-Model-Lifecycle/noxfile.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/pyproject.toml create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/__init__.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/config.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/data.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/monitor.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/retrain.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/serve.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/train.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py diff --git a/Academy/AI/2-ML-Model-Lifecycle/.DS_Store b/Academy/AI/2-ML-Model-Lifecycle/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5172429f264de2441865cb4700216d4256da9242 GIT binary patch literal 6148 zcmeH~J!%6%427R!7lt%jx}3%b$PET#pTHLgIFQEJ;E>dF^gR7ES*H$5cmnB-G%I%Z zD|S`@Z2$T80!#olbXV*=%*>dt@PRwdU#I)^a=X5>;#J@&VrHyNnC;iLL0pQvfVyTmjO&;ssLc!1UOG})p;=82 zR;?Ceh}WZ?+UmMqI#RP8R>OzYoz15hnq@nzF`-!xQ4j$Um=RcIKKc27r2jVm&svm< zfC&6E0=7P!4tu^-ovjbA=k?dB`g+i*aXG_}p8zI)6mRKa+;6_1_R^8c3Qa!(fk8n8 H{*=HsM+*^= literal 0 HcmV?d00001 diff --git a/Academy/AI/2-ML-Model-Lifecycle/.gitignore b/Academy/AI/2-ML-Model-Lifecycle/.gitignore new file mode 100644 index 0000000..1c5d00a --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/.gitignore @@ -0,0 +1,179 @@ +# local mlflow +mlartifacts/* +mlruns/* +dev/* + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc \ No newline at end of file diff --git a/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml b/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml new file mode 100644 index 0000000..fde97d9 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + - repo: https://github.com/psf/black + rev: 24.3.0 + hooks: + - id: black + language_version: python3.11 + - repo: https://github.com/PyCQA/isort + rev: 5.13.2 + hooks: + - id: isort + language_version: python3.11 + - repo: https://github.com/PyCQA/flake8 + rev: 7.0.0 + hooks: + - id: flake8 + additional_dependencies: [flake8-bugbear] + - repo: local + hooks: + - id: pytest + name: pytest + entry: pytest -q + language: system + pass_filenames: false diff --git a/Academy/AI/2-ML-Model-Lifecycle/Makefile b/Academy/AI/2-ML-Model-Lifecycle/Makefile new file mode 100644 index 0000000..5baa515 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/Makefile @@ -0,0 +1,22 @@ +.PHONY: setup ci train serve monitor retrain pipeline clean +SHELL := /bin/zsh +ENV := lab_env_2 +CONDA_ACTIVATE = source $$(conda info --base)/etc/profile.d/conda.sh ; conda activate ; conda activate +NOX := $(CONDA_ACTIVATE) $(ENV) && nox + +setup: ## create environment + install hooks + conda env create -f environment.yaml --quiet + $(NOX) pre-commit install + +## ---------------------------------------------- +## simulazione "push" su vari branch Git / GitHub +## ---------------------------------------------- + +push-feature: ## simula push su feature/* + $(NOX) -R -s feature_pipeline + +push-develop: ## simula push su develop + $(NOX) -R -s lint -s tests -s develop_pipeline + +push-master: ## simula push su master/main + $(NOX) -R -s lint -s tests -s master_pipeline diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md new file mode 100644 index 0000000..89eb251 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -0,0 +1,4 @@ +ML Model Lifecycle +- makefile for pushes +- nox for gh actions +- prefect for training orchestrator diff --git a/Academy/AI/2-ML-Model-Lifecycle/environment.yaml b/Academy/AI/2-ML-Model-Lifecycle/environment.yaml new file mode 100644 index 0000000..9b0e1b1 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/environment.yaml @@ -0,0 +1,17 @@ +name: lab_env_2 +channels: [conda-forge] +dependencies: + - python=3.11 + - pip + - pip: + - scikit-learn + - pandas + - mlflow + - prefect==2.* + - fastapi + - uvicorn[standard] + - pydantic + - pytest + - nox + - typer[all] + - evidently # per il drift monitoring diff --git a/Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store b/Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 per esplorare gli esperimenti." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.6 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.6" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Academy/AI/2-ML-Model-Lifecycle/noxfile.py b/Academy/AI/2-ML-Model-Lifecycle/noxfile.py new file mode 100644 index 0000000..8dac37e --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/noxfile.py @@ -0,0 +1,40 @@ +import nox +nox.options.reuse_existing_virtualenvs = True + +PYTHON = "3.11" + +# ----- helper ------------------------------------------------ +def install_project(session): + session.install("-e", ".") # pacchetto src/ + +# ----------------- CI step di base -------------------------- +@nox.session(python=PYTHON) +def lint(session): + session.install("pre-commit") + session.run("pre-commit", "run", "--all-files", "--show-diff-on-failure") + +@nox.session(python=PYTHON) +def tests(session): + session.install("pytest", "scikit-learn", "pandas") + install_project(session) + session.run("pytest", "-q") + +# ----------------- WORKFLOW SIMULATI ------------------------ +@nox.session(python=PYTHON) +def feature_pipeline(session): + """Hyper-parameter sweep – solo metadati.""" + install_project(session) + # lancia Prefect flow + session.run("python", "-m", "src.pipeline", "train_sweep") + +@nox.session(python=PYTHON) +def develop_pipeline(session): + """Train con best hp – salva anche il modello.""" + install_project(session) + session.run("python", "-m", "src.pipeline", "train_best") + +@nox.session(python=PYTHON) +def master_pipeline(session): + """Serve il miglior modello salvato.""" + install_project(session) + session.run("python", "-m", "src.pipeline", "serve_best") diff --git a/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml b/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml new file mode 100644 index 0000000..176fbe7 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml @@ -0,0 +1,26 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "mlops_lab" +version = "0.1.0" +description = "End-to-end MLOps lab pipeline" +authors = [{ name = "ranl", email = "lorenzo.randazzo@kiratech.ch" }] +readme = "README.md" +license = { text = "MIT" } +dependencies = [ + "scikit-learn", + "pandas", + "mlflow", + "prefect", + "fastapi", + "uvicorn[standard]", + "pydantic", + "typer[all]", + "evidently", +] +requires-python = ">=3.11" + +[tool.setuptools] +packages = ["src"] diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/__init__.py b/Academy/AI/2-ML-Model-Lifecycle/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/config.py b/Academy/AI/2-ML-Model-Lifecycle/src/config.py new file mode 100644 index 0000000..b80101e --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/config.py @@ -0,0 +1,12 @@ +# src/config.py +from pathlib import Path +from pydantic import BaseModel + +class Settings(BaseModel): + project_root: Path = Path(__file__).resolve().parents[1] + artifacts_dir: Path = project_root / "artifacts" + model_name: str = "wine_rf.pkl" + mlflow_uri: str = f"file:{project_root/'mlruns'}" + +CFG = Settings() +CFG.artifacts_dir.mkdir(exist_ok=True) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/data.py b/Academy/AI/2-ML-Model-Lifecycle/src/data.py new file mode 100644 index 0000000..66d4946 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/data.py @@ -0,0 +1,8 @@ +# src/data.py +import pandas as pd +from sklearn.datasets import load_wine +from sklearn.model_selection import train_test_split + +def load(split: float = .2, seed: int = 42): + X, y = load_wine(return_X_y=True, as_frame=True) + return train_test_split(X, y, test_size=split, random_state=seed) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py b/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py new file mode 100644 index 0000000..97af243 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py @@ -0,0 +1,23 @@ +# src/monitor.py +""" +Lettura delle predizioni servite, verifica di drift su Evidently +e scrittura di un report HTML. +Richiamabile via cron o manualmente: python monitor.py run +""" +import typer, pandas as pd +from evidently import Report +from evidently.metrics import DataDriftPreset + +app = typer.Typer() + +@app.command() +def run(ref_csv: str = "data/reference.csv", + cur_csv: str = "data/current.csv"): + ref, cur = pd.read_csv(ref_csv), pd.read_csv(cur_csv) + report = Report(metrics=[DataDriftPreset()]) + report.run(ref, cur) + report.save_html("artifacts/drift_report.html") + typer.echo("✅ Drift report scritto in artifacts/drift_report.html") + +if __name__ == "__main__": + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py new file mode 100644 index 0000000..bb76ffd --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py @@ -0,0 +1,45 @@ +import subprocess +from prefect import flow +from itertools import product +import mlflow, tempfile, subprocess + +# ----------------------------------------------------------- +# 1) sweep: lancia molti train con log_model=False +@flow +def train_sweep(): + params_grid = { + "n_estimators": [50, 100, 200, 400], + "max_depth": [None, 5, 10], + } + for n_est, d in product(params_grid["n_estimators"], params_grid["max_depth"]): + train.with_options(name=f"rf_ne{n_est}_md{d}").submit( + n_estimators=n_est, max_depth=d, log_model=False + ) + +# 2) train_best: sceglie il run con max accuracy e salva il modello +@flow +def train_best(): + client = mlflow.MlflowClient() + runs = mlflow.search_runs(order_by=["metrics.accuracy DESC"], max_results=1) + top = runs.iloc[0] + best_params = { + "n_estimators": int(top["params.n_estimators"]), + "max_depth": None if top["params.max_depth"] == "None" else int(top["params.max_depth"]), + } + train(**best_params, log_model=True) + +# 3) serve_best: scarica l'ultimo modello loggato e avvia FastAPI +@flow +def serve_best(): + runs = mlflow.search_runs( + filter_string="tags.mlflow.runName LIKE '%rf_%'", + order_by=["start_time DESC"], + max_results=1, + ) + run_id = runs.iloc[0]["run_id"] + client = mlflow.MlflowClient() + art = client.list_artifacts(run_id) + model_path = next(a.path for a in art if a.path.endswith(".pkl")) + with tempfile.TemporaryDirectory() as tmp: + local_pkl = client.download_artifacts(run_id, model_path, tmp) + subprocess.run(["python", "-m", "src.serve", local_pkl]) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py b/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py new file mode 100644 index 0000000..77875ec --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py @@ -0,0 +1,25 @@ +# src/retrain.py +""" +Retrain se il drift supera soglia; esegue train.run() con parametri di default. +""" +import json, subprocess, typer, pathlib + +DRIFT_PATH = pathlib.Path("artifacts/drift_report.html") + +app = typer.Typer() + +@app.command() +def run(threshold: float = 0.6): + from bs4 import BeautifulSoup + drift = 0.0 + if DRIFT_PATH.exists(): + soup = BeautifulSoup(DRIFT_PATH.read_text(), "html.parser") + drift = float(soup.select_one("#drift_score").text) + if drift > threshold: + typer.echo(f"⚠️ Drift {drift:.2f} > {threshold}: retraining…") + subprocess.run(["python", "-m", "src.train", "run"]) + else: + typer.echo(f"👍 Drift ok ({drift:.2f} ≤ {threshold})") + +if __name__ == "__main__": + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py new file mode 100644 index 0000000..42a62da --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py @@ -0,0 +1,25 @@ +# src/serve.py +import joblib, uvicorn, typer +from fastapi import FastAPI +from pydantic import BaseModel +from config import CFG + +app = FastAPI(title="Wine Quality Inference") + +model = joblib.load(CFG.artifacts_dir / CFG.model_name) + +class Features(BaseModel): # 11 feature numeriche + alcohol: float + magnesium: float + hue: float + # … (completare con le altre) + +@app.post("/predict") +def predict(feat: Features): + import numpy as np, pandas as pd + X = pd.DataFrame([feat.dict()]) + proba = model.predict_proba(X)[0].tolist() + return {"class": int(model.predict(X)[0]), "proba": proba} + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/train.py b/Academy/AI/2-ML-Model-Lifecycle/src/train.py new file mode 100644 index 0000000..1328cac --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/train.py @@ -0,0 +1,35 @@ +# src/train.py +import mlflow, joblib, typer +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +from src.config import CFG +from src.data import load + +app = typer.Typer() + +@app.command() +def run( + n_estimators: int = 200, + max_depth: int | None = None, + log_model: bool = typer.Option(True, "--log-model/--no-log-model") +): + mlflow.set_tracking_uri("http://127.0.0.1:5000") + mlflow.set_experiment("weather_classification_experiment") + + X_tr, X_te, y_tr, y_te = load() + with mlflow.start_run(): + model = RandomForestClassifier( + n_estimators=n_estimators, max_depth=max_depth, random_state=42 + ).fit(X_tr, y_tr) + + acc = accuracy_score(y_te, model.predict(X_te)) + mlflow.log_param("n_estimators", n_estimators) + mlflow.log_metric("accuracy", acc) + + if log_model: + joblib.dump(model, CFG.artifacts_dir / CFG.model_name) + mlflow.log_artifact(CFG.artifacts_dir / CFG.model_name) + typer.echo(f"✅ accuracy={acc:0.3f}") + +if __name__ == "__main__": + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py new file mode 100644 index 0000000..7604141 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py @@ -0,0 +1,15 @@ +"""Test basilari sul caricamento e integrità del dataset""" + +from src.data import load +import pandas as pd + +def test_split_shapes(): + X_tr, X_te, y_tr, y_te = load() + assert len(X_tr) > 0 and len(X_te) > 0 + assert len(X_tr) == len(y_tr) + assert len(X_te) == len(y_te) + +def test_no_missing_values(): + X_tr, X_te, y_tr, y_te = load() + assert not X_tr.isna().any().any() + assert not X_te.isna().any().any() diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py new file mode 100644 index 0000000..3ef3065 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py @@ -0,0 +1,7 @@ +# tests/test_train.py +from src.train import RandomForestClassifier, load + +def test_accuracy(): + X_tr, X_te, y_tr, y_te = load() + model = RandomForestClassifier(random_state=0).fit(X_tr, y_tr) + assert model.score(X_te, y_te) > 0.8 From 9f3b17ba3ba465bab27d7de7c3bb5ec60220550b Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 24 Apr 2025 00:06:25 +0200 Subject: [PATCH 13/70] working prefect and mlflow --- .../artifacts/wine_rf.pkl | Bin 0 -> 210881 bytes .../AI/2-ML-Model-Lifecycle/environment.yaml | 2 + Academy/AI/2-ML-Model-Lifecycle/load_data.py | 6 + .../notebooks/01_explore_and_train.ipynb | 46 ++++++- Academy/AI/2-ML-Model-Lifecycle/src/config.py | 9 +- .../AI/2-ML-Model-Lifecycle/src/pipeline.py | 126 +++++++++++++----- Academy/AI/2-ML-Model-Lifecycle/src/serve.py | 73 +++++++--- Academy/AI/2-ML-Model-Lifecycle/src/sweep.py | 58 ++++++++ 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zec{LrU3PGn_X=gvpl9pe`OTxAKkI}PAGrk#$uD=ld92hVG8yOZ&tGbw%a7x7;F4r2axzC)hZXuk9+bANwkeV=u8=JWiXBmPGXu937e`+Q3EJpMoz z7^3gKKSnQs2Mpm`d?~tSJpnvm2;ZHrcafU=iu~#Ml~2EL!H(?2VE z$B7(_zgNe5{6pw{hLy+PY5#C`9G~Xj^ds$Yp?R+7#o6bBgz}As>ZzEnp$9(wqVrn3 znDozr^n5MyaFPcCJQq@LsnF%-WUc3Ko^=TPCiqcYE^_e`Xbku@_|><+?l}IilePl; zYcsFsPanGOhF)`4RJtDk{R~tir}BU9ChPoT{|Aq`#hZT@pPJfF$W`nw3qQ`+HRkAd z&KW;yhMRAzsCSBw+yaK=m+OBxPV#0U7qJiECw?y5%aS@n8oobrg?D~N^OQdN3j5RI zU)eA9hGR+_h+hnVbn*vfuZ)J-&MWpWBm2t0YmuE z)A&ZdVYgvN(VwcEIzMK$sQ2^IreD>0&D#&tL+W~Rw_n}y%cS_e{bjwsDqlivS=?mm z>hHu8?tU7}|3bTg`L-WCU \u001b[39m\u001b[32m57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:42\u001b[39m, in \u001b[36mBunch.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 38\u001b[39m warnings.warn(\n\u001b[32m 39\u001b[39m \u001b[38;5;28mself\u001b[39m._deprecated_key_to_warnings[key],\n\u001b[32m 40\u001b[39m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m 41\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__getitem__\u001b[39m(key)\n", + "\u001b[31mKeyError\u001b[39m: 'head'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_wine\n\u001b[32m 3\u001b[39m data = load_wine(as_frame=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m data.head()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:59\u001b[39m, in \u001b[36mBunch.__getattr__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(key)\n", + "\u001b[31mAttributeError\u001b[39m: head" + ] + } + ], + "source": [ + "from sklearn.datasets import load_wine\n", + "\n", + "data = load_wine(as_frame=True)\n", + "\n", + "data.data.head()" + ] + }, { "cell_type": "markdown", "id": "28e5c13e", @@ -74,17 +106,25 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.6 64-bit", + "display_name": "Python 3.11.12 ('lab_env_2')", "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.9.6" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.12" }, "vscode": { "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + "hash": "f3091eeb433eba5ffd1439d05e3608594fa2a8935778ae687bbee724d90b3358" } } }, diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/config.py b/Academy/AI/2-ML-Model-Lifecycle/src/config.py index b80101e..d757d3d 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/config.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/config.py @@ -4,9 +4,10 @@ class Settings(BaseModel): project_root: Path = Path(__file__).resolve().parents[1] - artifacts_dir: Path = project_root / "artifacts" - model_name: str = "wine_rf.pkl" - mlflow_uri: str = f"file:{project_root/'mlruns'}" - + artifacts_dir: Path = project_root / "artifacts" + model_name: str = "wine_rf.pkl" + mlflow_uri: str = f"file:{project_root/'mlruns'}" + experiment_name: str = "WineSweepDemo" + CFG = Settings() CFG.artifacts_dir.mkdir(exist_ok=True) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py index bb76ffd..322bcc4 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py @@ -1,45 +1,101 @@ -import subprocess -from prefect import flow +# src/pipeline.py +"""Prefect 2 flows: + • train_sweep grid-search parallelo (solo metadati) + • train_best recupera best hp + addestra salvando modello + • serve_best scarica modello migliore e lo serve via FastAPI +""" from itertools import product -import mlflow, tempfile, subprocess +from pathlib import Path +from subprocess import Popen +import tempfile -# ----------------------------------------------------------- -# 1) sweep: lancia molti train con log_model=False -@flow -def train_sweep(): - params_grid = { - "n_estimators": [50, 100, 200, 400], - "max_depth": [None, 5, 10], - } - for n_est, d in product(params_grid["n_estimators"], params_grid["max_depth"]): - train.with_options(name=f"rf_ne{n_est}_md{d}").submit( - n_estimators=n_est, max_depth=d, log_model=False - ) +import mlflow +from prefect import flow, task, get_run_logger -# 2) train_best: sceglie il run con max accuracy e salva il modello -@flow -def train_best(): - client = mlflow.MlflowClient() - runs = mlflow.search_runs(order_by=["metrics.accuracy DESC"], max_results=1) - top = runs.iloc[0] - best_params = { - "n_estimators": int(top["params.n_estimators"]), - "max_depth": None if top["params.max_depth"] == "None" else int(top["params.max_depth"]), +from src.config import CFG +from src.train import run as train_run +from src.serve import run as serve_run + +# --------------------------------------------------------------------------- +# Tasks +# --------------------------------------------------------------------------- + + +@task +def train_task(n_estimators: int, max_depth: int | None): + return train_run(n_estimators=n_estimators, max_depth=max_depth, log_model=False) + + +@task +def find_best_params(metric: str = "accuracy") -> dict: + logger = get_run_logger() + runs = mlflow.search_runs(order_by=[f"metrics.{metric} DESC"], max_results=1) + best = runs.iloc[0] + params = { + "n_estimators": int(best["params.n_estimators"]), + "max_depth": None + if best["params.max_depth"] in ("", "None") + else int(best["params.max_depth"]), } - train(**best_params, log_model=True) + logger.info(f"Best params found: {params}") + return params -# 3) serve_best: scarica l'ultimo modello loggato e avvia FastAPI -@flow -def serve_best(): + +@task +def download_best_model(metric: str = "accuracy") -> Path: + logger = get_run_logger() + EXP_ID = mlflow.get_experiment_by_name(CFG.experiment_name).experiment_id runs = mlflow.search_runs( - filter_string="tags.mlflow.runName LIKE '%rf_%'", - order_by=["start_time DESC"], - max_results=1, + experiment_ids=[EXP_ID], order_by=[f"metrics.{metric} DESC"], max_results=1 ) run_id = runs.iloc[0]["run_id"] + client = mlflow.MlflowClient() art = client.list_artifacts(run_id) - model_path = next(a.path for a in art if a.path.endswith(".pkl")) - with tempfile.TemporaryDirectory() as tmp: - local_pkl = client.download_artifacts(run_id, model_path, tmp) - subprocess.run(["python", "-m", "src.serve", local_pkl]) + pkl_path = next(a.path for a in art if a.path.endswith(".pkl")) + + tmp_dir = tempfile.mkdtemp() + local_path = Path(client.download_artifacts(run_id, pkl_path, tmp_dir)) + logger.info(f"Downloaded best model to: {local_path}") + return local_path + + +@task +def serve_model(model_path: Path): + # Avvia l'API in un processo dedicato (non blocca il flow calling) + Popen(["python", "-m", "src.serve", "--model-path", str(model_path)]) + + +# --------------------------------------------------------------------------- +# Flows +# --------------------------------------------------------------------------- + + +@flow(name="train_sweep") +def train_sweep(): + grid = { + "n_estimators": [50, 100, 200], + "max_depth": [None, 5, 10], + } + for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): + train_task.submit(n_estimators=n_est, max_depth=m_depth) + + +@flow(name="train_best") +def train_best(): + params = find_best_params() + # unpack dict in **kwargs, salva anche il modello + train_run(**params, log_model=True) + + +@flow(name="serve_best") +def serve_best(): + model_path = download_best_model() + serve_model(model_path) + + +if __name__ == "__main__": + import sys + + # Facile CLI: python -m src.pipeline + locals()[sys.argv[1]]() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py index 42a62da..fc7b4e3 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py @@ -1,25 +1,66 @@ # src/serve.py -import joblib, uvicorn, typer +"""Inferenza FastAPI: serve un modello RF salvato con joblib.""" +from pathlib import Path +from typing import Optional + +import joblib +import typer +import uvicorn from fastapi import FastAPI from pydantic import BaseModel -from config import CFG -app = FastAPI(title="Wine Quality Inference") +from src.config import CFG + +app_cli = typer.Typer(add_completion=False) + + +def build_app(model_path: Path) -> FastAPI: + model = joblib.load(model_path) + + class Features(BaseModel): + alcohol: float + malic_acid: float + ash: float + alcalinity_of_ash: float + magnesium: float + total_phenols: float + flavanoids: float + nonflavanoid_phenols: float + proanthocyanins: float + color_intensity: float + hue: float + od280_od315_of_diluted_wines: float + proline: float + + api = FastAPI(title="Wine Quality Inference") + + @api.post("/predict") + def predict(feat: Features): + import pandas as pd + + X = pd.DataFrame([feat.dict()]) + proba = model.predict_proba(X)[0].tolist() + return {"class": int(model.predict(X)[0]), "proba": proba} + + return api -model = joblib.load(CFG.artifacts_dir / CFG.model_name) -class Features(BaseModel): # 11 feature numeriche - alcohol: float - magnesium: float - hue: float - # … (completare con le altre) +@app_cli.command() +def run( + model_path: Optional[Path] = typer.Option( + None, + "--model-path", + "-m", + help="Percorso del modello .pkl; default = ultimo train locale.", + ), + host: str = "0.0.0.0", + port: int = 8000, +): + """Avvia l'API sul modello indicato (o su quello di default).""" + path = model_path or (CFG.artifacts_dir / CFG.model_name) + api = build_app(path) + uvicorn.run(api, host=host, port=port) -@app.post("/predict") -def predict(feat: Features): - import numpy as np, pandas as pd - X = pd.DataFrame([feat.dict()]) - proba = model.predict_proba(X)[0].tolist() - return {"class": int(model.predict(X)[0]), "proba": proba} if __name__ == "__main__": - uvicorn.run(app, host="0.0.0.0", port=8000) + app_cli() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py b/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py new file mode 100644 index 0000000..91e141c --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py @@ -0,0 +1,58 @@ +# src/sweep.py +""" +Train-sweep demo +——————— +• Ciclo su piccoli hyper-parametri RF +• Log parametri + accuracy su MLflow +• Flow tracciata sulla UI di Prefect 2 (Orion) +""" + +from itertools import product + +import mlflow +from prefect import flow, task +from sklearn.datasets import load_wine +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split + +# -------------------------------------------------------------------- +# configura MLflow ➜ cartella locale "mlruns" nella root del progetto +mlflow.set_tracking_uri("file:mlruns") + +# -------------------------------------------------------------------- +@task(log_prints=True) +def train_once(n_estimators: int, max_depth: int | None): + """Un singolo addestramento + log su MLflow.""" + X, y = load_wine(return_X_y=True, as_frame=True) + X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42) + + print(f"Training with n_estimators={n_estimators}, max_depth={max_depth}") + with mlflow.start_run(): + mlflow.log_param("n_estimators", n_estimators) + mlflow.log_param("max_depth", max_depth) + + model = RandomForestClassifier( + n_estimators=n_estimators, max_depth=max_depth, random_state=0 + ).fit(X_tr, y_tr) + + acc = accuracy_score(y_te, model.predict(X_te)) + mlflow.log_metric("accuracy", acc) + + return acc + + +@flow(name="train_sweep") +def train_sweep(): + """Grid-search minimal su due hyper-parametri RF.""" + grid = { + "n_estimators": [1, 10, 100], + "max_depth": [None, 5, 10], + } + + for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): + train_once.submit(n_estimators=n_est, max_depth=m_depth) # .submit = task async, appare come sub-run + + +if __name__ == "__main__": + train_sweep() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/train.py b/Academy/AI/2-ML-Model-Lifecycle/src/train.py index 1328cac..6dfbbd9 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/train.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/train.py @@ -1,35 +1,62 @@ # src/train.py -import mlflow, joblib, typer +"""Un singolo ciclo di training + logging su MLflow.""" + +from pathlib import Path + +import joblib +import mlflow +import typer +from sklearn.datasets import load_wine from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split + from src.config import CFG -from src.data import load -app = typer.Typer() +app = typer.Typer(add_completion=False) + +# Configura il backend MLflow (cartella "mlruns" nella root) +mlflow.set_tracking_uri(CFG.mlflow_uri) +mlflow.set_experiment(CFG.experiment_name) + +def _train_core(n_estimators: int, max_depth: int | None): + """Ritorna modello addestrato e accuracy.""" + X, y = load_wine(return_X_y=True, as_frame=True) + X.columns = X.columns.str.replace('/', '_') + X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42) + + model = RandomForestClassifier( + n_estimators=n_estimators, max_depth=max_depth, random_state=0 + ).fit(X_tr, y_tr) + + acc = accuracy_score(y_te, model.predict(X_te)) + return model, acc + @app.command() def run( n_estimators: int = 200, max_depth: int | None = None, - log_model: bool = typer.Option(True, "--log-model/--no-log-model") -): - mlflow.set_tracking_uri("http://127.0.0.1:5000") - mlflow.set_experiment("weather_classification_experiment") - - X_tr, X_te, y_tr, y_te = load() + log_model: bool = typer.Option( + True, "--log-model/--no-log-model", help="Salva anche il modello su MLflow." + ), +) -> float: + """Addestra e logga parametri + metriche (+ modello opzionale).""" with mlflow.start_run(): - model = RandomForestClassifier( - n_estimators=n_estimators, max_depth=max_depth, random_state=42 - ).fit(X_tr, y_tr) - - acc = accuracy_score(y_te, model.predict(X_te)) mlflow.log_param("n_estimators", n_estimators) + mlflow.log_param("max_depth", max_depth) + + model, acc = _train_core(n_estimators, max_depth) mlflow.log_metric("accuracy", acc) - + if log_model: - joblib.dump(model, CFG.artifacts_dir / CFG.model_name) - mlflow.log_artifact(CFG.artifacts_dir / CFG.model_name) - typer.echo(f"✅ accuracy={acc:0.3f}") + outfile = CFG.artifacts_dir / CFG.model_name + joblib.dump(model, outfile) + mlflow.log_artifact(outfile) + + typer.echo(f"✅ accuracy={acc:0.3f}") + return acc # utile se importato come funzione + if __name__ == "__main__": app() From 0fe51dc305f6e9a21041a61934d48efb147e06b9 Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 24 Apr 2025 00:07:39 +0200 Subject: [PATCH 14/70] rm of model pkl --- Academy/AI/2-ML-Model-Lifecycle/.gitignore | 2 +- .../2-ML-Model-Lifecycle/artifacts/wine_rf.pkl | Bin 210881 -> 0 bytes 2 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/artifacts/wine_rf.pkl diff --git a/Academy/AI/2-ML-Model-Lifecycle/.gitignore b/Academy/AI/2-ML-Model-Lifecycle/.gitignore index 1c5d00a..202df2a 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/.gitignore +++ b/Academy/AI/2-ML-Model-Lifecycle/.gitignore @@ -1,5 +1,5 @@ # local mlflow -mlartifacts/* +artifacts/* mlruns/* dev/* diff --git a/Academy/AI/2-ML-Model-Lifecycle/artifacts/wine_rf.pkl b/Academy/AI/2-ML-Model-Lifecycle/artifacts/wine_rf.pkl deleted file mode 100644 index f9ca17686db2e228d58a3a6af199cd7e36dd7e1d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 210881 zcmeEv2Y436_V>~x^oTSOAtEA0DK=2R89D;e^kPsn3C*C80zn1E8x;^WiY3_PqJUTc 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Academy/AI/2-ML-Model-Lifecycle/load_data.py | 6 - .../notebooks/01_explore_and_train.ipynb | 196 ++++++++++++-- Academy/AI/2-ML-Model-Lifecycle/noxfile.py | 69 +++-- .../AI/2-ML-Model-Lifecycle/pyproject.toml | 15 +- .../AI/2-ML-Model-Lifecycle/requirements.txt | 212 +++++++++++++++ Academy/AI/2-ML-Model-Lifecycle/src/config.py | 45 +++- Academy/AI/2-ML-Model-Lifecycle/src/data.py | 102 ++++++- .../AI/2-ML-Model-Lifecycle/src/monitor.py | 114 ++++++-- .../AI/2-ML-Model-Lifecycle/src/pipeline.py | 101 ------- .../2-ML-Model-Lifecycle/src/pipeline_ml.py | 195 ++++++++++++++ .../src/pipeline_monitoring.py | 111 ++++++++ .../AI/2-ML-Model-Lifecycle/src/retrain.py | 25 -- Academy/AI/2-ML-Model-Lifecycle/src/serve.py | 91 ++++++- Academy/AI/2-ML-Model-Lifecycle/src/sweep.py | 58 ---- Academy/AI/2-ML-Model-Lifecycle/src/train.py | 88 +++++-- Academy/AI/2-ML-Model-Lifecycle/src/utils.py | 29 ++ .../AI/2-ML-Model-Lifecycle/tests/__init__.py | 0 .../2-ML-Model-Lifecycle/tests/test_data.py | 24 +- .../2-ML-Model-Lifecycle/tests/test_serve.py | 33 +++ .../2-ML-Model-Lifecycle/tests/test_train.py | 13 +- 26 files changed, 1630 insertions(+), 357 deletions(-) delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/load_data.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/requirements.txt delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/pipeline_ml.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/pipeline_monitoring.py delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/retrain.py delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/sweep.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/utils.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/tests/__init__.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py diff --git a/Academy/AI/2-ML-Model-Lifecycle/.DS_Store b/Academy/AI/2-ML-Model-Lifecycle/.DS_Store index 5172429f264de2441865cb4700216d4256da9242..b5c4c43ba91e8c72d6c550df3ec3bb345705cb2b 100644 GIT binary patch delta 353 zcmZoMXfc=|#>B)qu~3YagMop8V`8C*EEA9c0?a_n$iN`Lz>vm}$dJrX!cd%4zOisQ zV?9WQ6DU>$6w5@GEiOpP$xi~x?no-g$t*50Fu2CZ#LU9V#?HaP!OamHoRME1T#{H) zTI`fq6b<5qqy7GO&29EVw8yCqFM8D9gw&QCDhX`V*GP1|lq*xq;pS2H?iR@640= UMHE>AjHu~68Yk%57MnW31Wh#|QwxF|0tKQA390up6lU;>j69#BMp0U^Ix sk@*?RW&`H^Oq0BN0U!lS%7Mq~vu0ItRr3jhEB diff --git a/Academy/AI/2-ML-Model-Lifecycle/.gitignore b/Academy/AI/2-ML-Model-Lifecycle/.gitignore index 202df2a..7869f53 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/.gitignore +++ b/Academy/AI/2-ML-Model-Lifecycle/.gitignore @@ -1,7 +1,8 @@ # local mlflow -artifacts/* -mlruns/* -dev/* +artifacts +mlruns +dev +data # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml b/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml index fde97d9..7f9e0df 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml +++ b/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml @@ -1,23 +1,6 @@ +# .pre-commit-config.yaml repos: - - repo: https://github.com/psf/black - rev: 24.3.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.3.0 hooks: - - id: black - language_version: python3.11 - - repo: https://github.com/PyCQA/isort - rev: 5.13.2 - hooks: - - id: isort - language_version: python3.11 - - repo: https://github.com/PyCQA/flake8 - rev: 7.0.0 - hooks: - - id: flake8 - additional_dependencies: [flake8-bugbear] - - repo: local - hooks: - - id: pytest - name: pytest - entry: pytest -q - language: system - pass_filenames: false + - id: ruff diff --git a/Academy/AI/2-ML-Model-Lifecycle/Makefile b/Academy/AI/2-ML-Model-Lifecycle/Makefile index 5baa515..14b3806 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/Makefile +++ b/Academy/AI/2-ML-Model-Lifecycle/Makefile @@ -1,22 +1,35 @@ +# Makefile — Local GitHub Actions simulation using nox + conda environments .PHONY: setup ci train serve monitor retrain pipeline clean SHELL := /bin/zsh + +# Conda environment name ENV := lab_env_2 + +# Activate Conda + run nox in the correct environment CONDA_ACTIVATE = source $$(conda info --base)/etc/profile.d/conda.sh ; conda activate ; conda activate -NOX := $(CONDA_ACTIVATE) $(ENV) && nox +NOX := $(CONDA_ACTIVATE) $(ENV) && nox --no-venv + +# ------------------------------------------------------------ +# Environment setup +# ------------------------------------------------------------ -setup: ## create environment + install hooks +setup: ## Create conda environment + install pre-commit hooks conda env create -f environment.yaml --quiet $(NOX) pre-commit install -## ---------------------------------------------- -## simulazione "push" su vari branch Git / GitHub -## ---------------------------------------------- +# ------------------------------------------------------------ +# Simulated Git branch pushes (as CI/CD entrypoints) +# ------------------------------------------------------------ -push-feature: ## simula push su feature/* +push-feature: ## Simulate push to feature/* → hyperparameter sweep only $(NOX) -R -s feature_pipeline -push-develop: ## simula push su develop - $(NOX) -R -s lint -s tests -s develop_pipeline +push-develop: ## Simulate push to develop → lint + tests + train best model + $(NOX) -R -s lint + $(NOX) -R -s tests + $(NOX) -R -s develop_pipeline -push-master: ## simula push su master/main - $(NOX) -R -s lint -s tests -s master_pipeline +push-main: ## Simulate push to master/main → lint + tests + serve model + $(NOX) -R -s lint + $(NOX) -R -s tests + $(NOX) -R -s main_pipeline diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 89eb251..30e159f 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -1,4 +1,142 @@ -ML Model Lifecycle -- makefile for pushes -- nox for gh actions -- prefect for training orchestrator + +# Wine Quality ML Pipeline – Technical Review Guide + +This README provides a step-by-step guide for reviewing the Wine Quality MLOps proof of concept. +All workflows must be tested using the `Makefile` commands only. + +--- + +## 1. Environment Setup + +Create and activate the Conda environment (`lab_env_2`): + +```bash +conda env create -f environment.yaml +conda activate lab_env_2 +``` + +--- + +## 2. Start Required Services + +### MLflow UI + +```bash +conda activate lab_env_2 +mlflow ui +``` + +Open: [http://localhost:5000](http://localhost:5000) + +--- + +### Prefect Server + +```bash +conda activate lab_env_2 +prefect server start +``` + +Open: [http://localhost:4200](http://localhost:4200) + +In another terminal: + +```bash +prefect agent start +``` + +--- + +## 3. Run Workflows via `make` + +All workflows must be executed via the provided `Makefile` in another terminal: +```bash +conda activate lab_env_2 +``` + +### Feature Branch (Hyperparameter Sweep) + +```bash +make push-feature +``` + +- Runs `train_experiment` on a parameter grid +- Logs metadata only (no model artifact) + +--- + +### Develop Branch (Train Best Model) + +```bash +make push-develop +``` + +- Runs: `lint`, `tests`, `train_best` +- Trains model using best hyperparams from MLflow +- Logs model artifact to MLflow + +--- + +### Main Branch (Model Serving) + +```bash +make push-main +``` + +- Runs: `lint`, `tests`, `serve_best` +- Starts a FastAPI app at [http://localhost:9000/predict](http://localhost:9000/predict) +- To shutdown the FastAPI server: `kill -9 $(lsof -ti :9000)` + +Example test request: + +```bash +curl -X POST http://localhost:9000/predict -H "Content-Type: application/json" \ + -d '{"alcohol":13.0,"malic_acid":2.0,"ash":2.5,"alcalinity_of_ash":15.0, + "magnesium":100.0,"total_phenols":2.0,"flavanoids":2.5, + "nonflavanoid_phenols":0.3,"proanthocyanins":1.9,"color_intensity":5.0, + "hue":1.0,"od280_od315_of_diluted_wines":3.0,"proline":1000}' +``` + +--- + +### Monitoring + Drift Detection + +This runs a simulated batch with drift. If drift is detected (p ≤ 0.05), it retriggers training. + +```bash +python -m src.pipeline_monitoring +``` + +Generates: +- A deployment of flow running every minute +- `artifacts/drift_report.html` +- `artifacts/drift_report.json` +- Retraining of best model if triggered + +--- + +## 4. Verify Output in UI Tools + +| Component | URL | Check | +|----------|-----|-------| +| **MLflow UI** | http://localhost:5000 | Parameters, metrics, model | +| **Prefect UI** | http://localhost:4200 | Flow runs, logs | +| **FastAPI** | http://localhost:9000/predict | Model serving | +| **Drift Report** | `artifacts/drift_report.html` | Open manually in browser | + +--- + +## Summary + +This PoC runs fully locally using: + +- Conda +- Nox +- Make +- MLflow +- Prefect +- FastAPI + +No Docker or remote services are required. + +Please run and verify each `make` command as documented above. diff --git a/Academy/AI/2-ML-Model-Lifecycle/environment.yaml b/Academy/AI/2-ML-Model-Lifecycle/environment.yaml index 0233080..6a85694 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/environment.yaml +++ b/Academy/AI/2-ML-Model-Lifecycle/environment.yaml @@ -1,19 +1,236 @@ name: lab_env_2 -channels: [conda-forge] +channels: + - conda-forge + - defaults dependencies: - - python=3.11 - - ipykernel - - pip + - appnope=0.1.2 + - argcomplete=1.12.3 + - asttokens=3.0.0 + - attrs=25.3.0 + - backports=1.0 + - backports.functools_lru_cache=2.0.0 + - bzip2=1.0.8 + - ca-certificates=2025.2.25 + - cachetools=5.5.2 + - chardet=5.2.0 + - colorama=0.4.6 + - colorlog=5.0.1 + - comm=0.2.1 + - debugpy=1.8.11 + - decorator=5.1.1 + - dependency-groups=1.3.0 + - distlib=0.3.8 + - exceptiongroup=1.2.2 + - executing=0.8.3 + - filelock=3.18.0 + - importlib_metadata=4.13.0 + - ipykernel=6.29.5 + - ipython=9.1.0 + - ipython_pygments_lexers=1.1.1 + - jedi=0.19.2 + - jinja2=3.1.6 + - jupyter_client=8.6.3 + - jupyter_core=5.7.2 + - libcxx=14.0.6 + - libexpat=2.7.0 + - libffi=3.4.6 + - liblzma=5.8.1 + - libsodium=1.0.18 + - libsqlite=3.49.1 + - libzlib=1.3.1 + - markupsafe=3.0.2 + - matplotlib-inline=0.1.6 + - ncurses=6.5 + - nest-asyncio=1.6.0 + - nox=2025.2.9 + - openssl=3.5.0 + - packaging=24.2 + - parso=0.8.4 + - pexpect=4.8.0 + - pickleshare=0.7.5 + - pip=25.0.1 + - platformdirs=4.3.7 + - pluggy=1.5.0 + - prompt-toolkit=3.0.43 + - prompt_toolkit=3.0.43 + - psutil=5.9.0 + - ptyprocess=0.7.0 + - pure_eval=0.2.2 + - pygments=2.19.1 + - pyproject-api=1.9.0 + - python=3.11.12 + - python-dateutil=2.9.0post0 + - python_abi=3.11 + - pyzmq=26.2.0 + - readline=8.2 + - setuptools=78.1.0 + - six=1.17.0 + - stack_data=0.2.0 + - tk=8.6.13 + - tomli=2.2.1 + - tornado=6.4.2 + - tox=4.25.0 + - traitlets=5.14.3 + - virtualenv=20.30.0 + - wcwidth=0.2.5 + - wheel=0.45.1 + - zeromq=4.3.5 + - zipp=3.21.0 - pip: - - config - - scikit-learn - - pandas - - mlflow - - prefect==2.* - - fastapi - - uvicorn[standard] - - pydantic - - pytest - - nox - - typer[all] - - evidently # per il drift monitoring + - aiosqlite==0.21.0 + - alembic==1.15.2 + - annotated-types==0.7.0 + - anyio==4.6.0 + - appdirs==1.4.4 + - apprise==1.9.3 + - asgi-lifespan==2.1.0 + - async-timeout==5.0.1 + - asyncpg==0.29.0 + - blinker==1.9.0 + - certifi==2025.1.31 + - cffi==1.17.1 + - cfgv==3.4.0 + - charset-normalizer==3.4.1 + - click==8.1.8 + - cloudpickle==3.1.1 + - contourpy==1.3.2 + - coolname==2.2.0 + - croniter==5.0.1 + - cryptography==44.0.2 + - cycler==0.12.1 + - databricks-sdk==0.50.0 + - dateparser==1.2.1 + - deprecated==1.2.18 + - deprecation==2.1.0 + - distro==1.9.0 + - dnspython==2.7.0 + - docker==7.1.0 + - durationpy==0.9 + - dynaconf==3.2.10 + - email-validator==2.2.0 + - evidently==0.4.40 + - faker==37.1.0 + - fastapi==0.115.12 + - flask==3.1.0 + - fonttools==4.57.0 + - fsspec==2025.3.2 + - gitdb==4.0.12 + - gitpython==3.1.44 + - google-auth==2.39.0 + - graphene==3.4.3 + - graphql-core==3.2.6 + - graphql-relay==3.2.0 + - greenlet==3.2.0 + - griffe==1.7.2 + - gunicorn==23.0.0 + - h11==0.14.0 + - h2==4.2.0 + - hpack==4.1.0 + - httpcore==1.0.8 + - httptools==0.6.4 + - httpx==0.28.1 + - humanize==4.12.2 + - hyperframe==6.1.0 + - identify==2.6.10 + - idna==3.10 + - importlib-metadata==8.6.1 + - importlib-resources==6.4.5 + - iniconfig==2.1.0 + - iterative-telemetry==0.0.10 + - itsdangerous==2.2.0 + - jinja2-humanize-extension==0.4.0 + - joblib==1.4.2 + - jsonpatch==1.33 + - jsonpointer==3.0.0 + - jsonschema==4.23.0 + - jsonschema-specifications==2024.10.1 + - kiwisolver==1.4.8 + - kubernetes==31.0.0 + - litestar==2.15.2 + - litestar-htmx==0.4.1 + - mako==1.3.10 + - markdown==3.8 + - markdown-it-py==3.0.0 + - matplotlib==3.10.1 + - mdurl==0.1.2 + - mlflow==2.21.3 + - mlflow-skinny==2.21.3 + - msgspec==0.19.0 + - multidict==6.4.3 + - multipart==1.2.1 + - mypy-extensions==1.0.0 + - nltk==3.9.1 + - nodeenv==1.9.1 + - numpy==2.0.2 + - oauthlib==3.2.2 + - opentelemetry-api==1.32.1 + - opentelemetry-sdk==1.32.1 + - opentelemetry-semantic-conventions==0.53b1 + - orjson==3.10.16 + - pandas==2.2.3 + - pathspec==0.12.1 + - patsy==1.0.1 + - pendulum==2.1.2 + - pillow==11.2.1 + - plotly==5.24.1 + - polyfactory==2.21.0 + - pre-commit==4.2.0 + - prefect==2.20.17 + - protobuf==5.29.4 + - pyarrow==19.0.1 + - pyasn1==0.6.1 + - pyasn1-modules==0.4.2 + - pycparser==2.22 + - pydantic==2.11.3 + - pydantic-core==2.33.1 + - pyparsing==3.2.3 + - pytest==8.3.5 + - python-dotenv==1.1.0 + - python-multipart==0.0.20 + - python-slugify==8.0.4 + - pytz==2024.2 + - pytzdata==2020.1 + - pyyaml==6.0.2 + - readchar==4.2.1 + - referencing==0.36.2 + - regex==2024.11.6 + - requests==2.32.3 + - requests-oauthlib==2.0.0 + - rfc3339-validator==0.1.4 + - rich==13.9.4 + - rich-click==1.8.8 + - rpds-py==0.24.0 + - rsa==4.9.1 + - ruamel-yaml==0.18.10 + - ruamel-yaml-clib==0.2.12 + - scikit-learn==1.6.1 + - scipy==1.15.2 + - shellingham==1.5.4 + - smmap==5.0.2 + - sniffio==1.3.1 + - sqlalchemy==2.0.35 + - sqlparse==0.5.3 + - starlette==0.46.2 + - statsmodels==0.14.4 + - tenacity==9.1.2 + - text-unidecode==1.3 + - threadpoolctl==3.6.0 + - toml==0.10.2 + - tqdm==4.67.1 + - typer==0.15.2 + - typing-extensions==4.13.2 + - typing-inspect==0.9.0 + - typing-inspection==0.4.0 + - tzdata==2025.2 + - tzlocal==5.3.1 + - ujson==5.10.0 + - urllib3==2.4.0 + - uuid6==2024.7.10 + - uvicorn==0.34.1 + - uvloop==0.21.0 + - watchdog==6.0.0 + - watchfiles==1.0.5 + - websocket-client==1.8.0 + - websockets==13.1 + - werkzeug==3.1.3 + - wrapt==1.17.2 diff --git a/Academy/AI/2-ML-Model-Lifecycle/load_data.py b/Academy/AI/2-ML-Model-Lifecycle/load_data.py deleted file mode 100644 index 32fc1ac..0000000 --- a/Academy/AI/2-ML-Model-Lifecycle/load_data.py +++ /dev/null @@ -1,6 +0,0 @@ -from sklearn.datasets import load_wine - -data, y = load_wine(as_frame=True) - -data.head() - diff --git a/Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb b/Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb index fd968c1..a80094e 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb +++ b/Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb @@ -60,26 +60,157 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "6d5b1b79", "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "head", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:57\u001b[39m, in \u001b[36mBunch.__getattr__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 56\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:42\u001b[39m, in \u001b[36mBunch.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 38\u001b[39m warnings.warn(\n\u001b[32m 39\u001b[39m \u001b[38;5;28mself\u001b[39m._deprecated_key_to_warnings[key],\n\u001b[32m 40\u001b[39m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m 41\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__getitem__\u001b[39m(key)\n", - "\u001b[31mKeyError\u001b[39m: 'head'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_wine\n\u001b[32m 3\u001b[39m data = load_wine(as_frame=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m data.head()\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:59\u001b[39m, in \u001b[36mBunch.__getattr__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(key)\n", - "\u001b[31mAttributeError\u001b[39m: head" - ] + "data": { + "text/html": [ + "

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alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.0
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" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280/od315_of_diluted_wines proline \n", + "0 3.92 1065.0 \n", + "1 3.40 1050.0 \n", + "2 3.17 1185.0 \n", + "3 3.45 1480.0 \n", + "4 2.93 735.0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -90,6 +221,39 @@ "data.data.head()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2884b86d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['alcohol',\n", + " 'malic_acid',\n", + " 'ash',\n", + " 'alcalinity_of_ash',\n", + " 'magnesium',\n", + " 'total_phenols',\n", + " 'flavanoids',\n", + " 'nonflavanoid_phenols',\n", + " 'proanthocyanins',\n", + " 'color_intensity',\n", + " 'hue',\n", + " 'od280/od315_of_diluted_wines',\n", + " 'proline']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.feature_names" + ] + }, { "cell_type": "markdown", "id": "28e5c13e", diff --git a/Academy/AI/2-ML-Model-Lifecycle/noxfile.py b/Academy/AI/2-ML-Model-Lifecycle/noxfile.py index 8dac37e..1f11bf7 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/noxfile.py +++ b/Academy/AI/2-ML-Model-Lifecycle/noxfile.py @@ -1,40 +1,79 @@ +# noxfile.py — Local CI/CD simulation using Nox sessions import nox + +# Reuse virtualenvs across runs to avoid reinstallation nox.options.reuse_existing_virtualenvs = True +# Shared environment for all sessions +nox.options.envdir = ".nox/shared" +# Python version to use in all sessions PYTHON = "3.11" -# ----- helper ------------------------------------------------ +# ------------------------------------------------------------ +# Shared setup for installing local package +# ------------------------------------------------------------ + + def install_project(session): - session.install("-e", ".") # pacchetto src/ + """ + Install the local src/ package in editable mode. + """ + session.install("-e", ".") + + +# ------------------------------------------------------------ +# Core CI checks: linting and unit tests +# ------------------------------------------------------------ + -# ----------------- CI step di base -------------------------- @nox.session(python=PYTHON) def lint(session): - session.install("pre-commit") - session.run("pre-commit", "run", "--all-files", "--show-diff-on-failure") + """ + Run pre-commit hooks for linting and formatting. + """ + session.run("pre-commit", "run", "--all-files", "--hook-stage", "manual") @nox.session(python=PYTHON) def tests(session): - session.install("pytest", "scikit-learn", "pandas") + """ + Run unit tests using pytest. + """ install_project(session) session.run("pytest", "-q") -# ----------------- WORKFLOW SIMULATI ------------------------ + +# ------------------------------------------------------------ +# Simulated ML workflows triggered by Git pushes +# ------------------------------------------------------------ + + @nox.session(python=PYTHON) def feature_pipeline(session): - """Hyper-parameter sweep – solo metadati.""" + """ + Simulates a push to feature/*: + - Runs hyperparameter sweep (no model saved). + """ install_project(session) - # lancia Prefect flow - session.run("python", "-m", "src.pipeline", "train_sweep") + session.run("python", "-m", "src.pipeline_ml", "train_experiment") + @nox.session(python=PYTHON) def develop_pipeline(session): - """Train con best hp – salva anche il modello.""" + """ + Simulates a push to develop: + - Trains the model using best hyperparameters. + - Logs metrics and saves model to MLflow. + """ install_project(session) - session.run("python", "-m", "src.pipeline", "train_best") + session.run("python", "-m", "src.pipeline_ml", "train_best") + @nox.session(python=PYTHON) -def master_pipeline(session): - """Serve il miglior modello salvato.""" +def main_pipeline(session): + """ + Simulates a push to main: + - Downloads the best saved model. + - Serves the model through FastAPI. + """ install_project(session) - session.run("python", "-m", "src.pipeline", "serve_best") + session.run("python", "-m", "src.pipeline_ml", "serve_best") diff --git a/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml b/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml index 176fbe7..734e0c8 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml +++ b/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml @@ -9,18 +9,11 @@ description = "End-to-end MLOps lab pipeline" authors = [{ name = "ranl", email = "lorenzo.randazzo@kiratech.ch" }] readme = "README.md" license = { text = "MIT" } -dependencies = [ - "scikit-learn", - "pandas", - "mlflow", - "prefect", - "fastapi", - "uvicorn[standard]", - "pydantic", - "typer[all]", - "evidently", -] +dynamic = ["dependencies"] requires-python = ">=3.11" [tool.setuptools] packages = ["src"] + +[tool.setuptools.dynamic] +dependencies = {file = ["requirements.txt"]} diff --git a/Academy/AI/2-ML-Model-Lifecycle/requirements.txt b/Academy/AI/2-ML-Model-Lifecycle/requirements.txt new file mode 100644 index 0000000..05e2720 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/requirements.txt @@ -0,0 +1,212 @@ +aiosqlite==0.21.0 +alembic==1.15.2 +annotated-types==0.7.0 +anyio==4.6.0 +appdirs==1.4.4 +appnope==0.1.2 +apprise==1.9.3 +argcomplete==1.12.3 +asgi-lifespan==2.1.0 +asttokens==3.0.0 +async-timeout==5.0.1 +asyncpg==0.29.0 +attrs==25.3.0 +backports.functools_lru_cache==2.0.0 +blinker==1.9.0 +cachetools==5.5.2 +certifi==2025.1.31 +cffi==1.17.1 +cfgv==3.4.0 +chardet==5.2.0 +charset-normalizer==3.4.1 +click==8.1.8 +cloudpickle==3.1.1 +colorama==0.4.6 +colorlog==5.0.1 +comm==0.2.1 +contourpy==1.3.2 +coolname==2.2.0 +croniter==5.0.1 +cryptography==44.0.2 +cycler==0.12.1 +databricks-sdk==0.50.0 +dateparser==1.2.1 +debugpy==1.8.11 +decorator==5.1.1 +dependency-groups==1.3.0 +Deprecated==1.2.18 +deprecation==2.1.0 +distlib==0.3.8 +distro==1.9.0 +dnspython==2.7.0 +docker==7.1.0 +durationpy==0.9 +dynaconf==3.2.10 +email_validator==2.2.0 +evidently==0.4.40 +exceptiongroup==1.2.2 +executing==0.8.3 +Faker==37.1.0 +fastapi==0.115.12 +filelock==3.18.0 +Flask==3.1.0 +fonttools==4.57.0 +fsspec==2025.3.2 +gitdb==4.0.12 +GitPython==3.1.44 +google-auth==2.39.0 +graphene==3.4.3 +graphql-core==3.2.6 +graphql-relay==3.2.0 +graphviz==0.20.3 +greenlet==3.2.0 +griffe==1.7.2 +gunicorn==23.0.0 +h11==0.14.0 +h2==4.2.0 +hpack==4.1.0 +httpcore==1.0.8 +httptools==0.6.4 +httpx==0.28.1 +humanize==4.12.2 +hyperframe==6.1.0 +identify==2.6.10 +idna==3.10 +importlib_metadata==8.6.1 +importlib_resources==6.4.5 +iniconfig==2.1.0 +ipykernel==6.29.5 +ipython==9.1.0 +ipython_pygments_lexers==1.1.1 +iterative-telemetry==0.0.10 +itsdangerous==2.2.0 +jedi==0.19.2 +Jinja2==3.1.6 +jinja2-humanize-extension==0.4.0 +joblib==1.4.2 +jsonpatch==1.33 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2024.10.1 +jupyter_client==8.6.3 +jupyter_core==5.7.2 +kiwisolver==1.4.8 +kubernetes==31.0.0 +litestar==2.15.2 +litestar-htmx==0.4.1 +Mako==1.3.10 +Markdown==3.8 +markdown-it-py==3.0.0 +MarkupSafe==3.0.2 +matplotlib==3.10.1 +matplotlib-inline==0.1.6 +mdurl==0.1.2 +mlflow==2.21.3 +mlflow-skinny==2.21.3 +msgspec==0.19.0 +multidict==6.4.3 +multipart==1.2.1 +mypy-extensions==1.0.0 +nest_asyncio==1.6.0 +nltk==3.9.1 +nodeenv==1.9.1 +nox==2025.2.9 +numpy==2.0.2 +oauthlib==3.2.2 +opentelemetry-api==1.32.1 +opentelemetry-sdk==1.32.1 +opentelemetry-semantic-conventions==0.53b1 +orjson==3.10.16 +packaging==24.2 +pandas==2.2.3 +parso==0.8.4 +pathspec==0.12.1 +patsy==1.0.1 +pendulum==2.1.2 +pexpect==4.8.0 +pickleshare==0.7.5 +pillow==11.2.1 +pip==25.0.1 +platformdirs==4.3.7 +plotly==5.24.1 +pluggy==1.5.0 +polyfactory==2.21.0 +pre_commit==4.2.0 +prefect==2.20.17 +prompt_toolkit==3.0.43 +protobuf==5.29.4 +psutil==5.9.0 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==19.0.1 +pyasn1==0.6.1 +pyasn1_modules==0.4.2 +pycparser==2.22 +pydantic==2.11.3 +pydantic_core==2.33.1 +Pygments==2.19.1 +pyparsing==3.2.3 +pyproject-api==1.9.0 +pytest==8.3.5 +python-dateutil==2.9.0.post0 +python-dotenv==1.1.0 +python-multipart==0.0.20 +python-slugify==8.0.4 +pytz==2024.2 +pytzdata==2020.1 +PyYAML==6.0.2 +pyzmq==26.2.0 +readchar==4.2.1 +referencing==0.36.2 +regex==2024.11.6 +requests==2.32.3 +requests-oauthlib==2.0.0 +rfc3339-validator==0.1.4 +rich==13.9.4 +rich-click==1.8.8 +rpds-py==0.24.0 +rsa==4.9.1 +ruamel.yaml==0.18.10 +ruamel.yaml.clib==0.2.12 +scikit-learn==1.6.1 +scipy==1.15.2 +setuptools==78.1.0 +shellingham==1.5.4 +six==1.17.0 +smmap==5.0.2 +sniffio==1.3.1 +SQLAlchemy==2.0.35 +sqlparse==0.5.3 +stack-data==0.2.0 +starlette==0.46.2 +statsmodels==0.14.4 +tenacity==9.1.2 +text-unidecode==1.3 +threadpoolctl==3.6.0 +toml==0.10.2 +tomli==2.2.1 +tornado==6.4.2 +tox==4.25.0 +tqdm==4.67.1 +traitlets==5.14.3 +typer==0.15.2 +typing_extensions==4.13.2 +typing-inspect==0.9.0 +typing-inspection==0.4.0 +tzdata==2025.2 +tzlocal==5.3.1 +ujson==5.10.0 +urllib3==2.4.0 +uuid6==2024.7.10 +uvicorn==0.34.1 +uvloop==0.21.0 +virtualenv==20.30.0 +watchdog==6.0.0 +watchfiles==1.0.5 +wcwidth==0.2.5 +websocket-client==1.8.0 +websockets==13.1 +Werkzeug==3.1.3 +wheel==0.45.1 +wrapt==1.17.2 +zipp==3.21.0 diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/config.py b/Academy/AI/2-ML-Model-Lifecycle/src/config.py index d757d3d..56f3add 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/config.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/config.py @@ -1,13 +1,54 @@ -# src/config.py +""" +src/config.py +Configuration module for the Wine Quality pipeline. + +- Defines global paths, MLflow settings, artifact naming, and Evidently monitoring endpoints. +- Uses Pydantic's BaseModel for structured configuration. +""" + from pathlib import Path + from pydantic import BaseModel + class Settings(BaseModel): + """ + Centralized application settings. + + Attributes: + project_root (Path): Root directory of the project. + artifacts_dir (Path): Directory where artifacts (models, reports) will be stored. + model_name (str): Default filename for the trained model artifact. + mlflow_uri (str): URI where MLflow tracking server or local files are stored. + experiment_name (str): Name of the MLflow experiment. + evidently_url (str): Base URL where the Evidently UI dashboard is running. + evidently_project (str): Project name under Evidently for monitoring. + """ + + # Root of the project (assumes config.py is in src/) project_root: Path = Path(__file__).resolve().parents[1] + + # Directory for storing artifacts (models, drift reports, etc.) artifacts_dir: Path = project_root / "artifacts" + + # Default model filename for serialization model_name: str = "wine_rf.pkl" + + # MLflow tracking URI (using local file storage in 'mlruns/') mlflow_uri: str = f"file:{project_root/'mlruns'}" - experiment_name: str = "WineSweepDemo" + # MLflow experiment name for tracking runs + experiment_name: str = "WineSweepDemo" + + # Evidently monitoring - URL to the local Evidently UI server + evidently_url: str = "http://localhost:8000" + + # Evidently monitoring - Name of the project for batch drift tracking + evidently_project: str = "wine_quality_monitoring" + + +# Singleton instance used throughout the project CFG = Settings() + +# Ensure artifacts directory exists (create if it doesn't) CFG.artifacts_dir.mkdir(exist_ok=True) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/data.py b/Academy/AI/2-ML-Model-Lifecycle/src/data.py index 66d4946..d8a5843 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/data.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/data.py @@ -1,8 +1,104 @@ -# src/data.py +""" +src/data.py +Data handling module for the Wine Quality pipeline. + +Provides: +- data_collection: Loads the wine dataset from scikit-learn. +- new_data_collection: Simulates a new batch with data drift in alcohol. +- data_preparation: Prepares the dataset (renames columns, performs train-test split). +""" + +import random + import pandas as pd +import typer from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split +from src.utils import get_logger + +app = typer.Typer(add_completion=False) + -def load(split: float = .2, seed: int = 42): +def data_collection() -> tuple[pd.DataFrame, pd.Series]: + """ + Loads the wine dataset. + + Returns: + tuple: + - X (pd.DataFrame): Feature matrix. + - y (pd.Series): Target labels. + """ + logger = get_logger() + logger.info("Loading Wine dataset...") X, y = load_wine(return_X_y=True, as_frame=True) - return train_test_split(X, y, test_size=split, random_state=seed) + logger.info( + f"Wine dataset loaded with {X.shape[0]} samples and {X.shape[1]} features." + ) + return (X, y) + + +def new_data_collection() -> pd.DataFrame: + """ + Loads the wine dataset and introduces data drift in the 'alcohol' feature. + + Randomly perturbs alcohol values within a multiplier range + to simulate a distributional change for monitoring. + + Returns: + pd.DataFrame: Drifted dataset (features only, no target). + """ + X, _ = data_collection() + df = X.copy() + + logger = get_logger() + logger.info("Generating drifted data batch...") + + # Filter high alcohol wines + df_filtered = df[df["alcohol"] > 13] + + # Apply random scaling to simulate drift + rnd_drift = random.uniform(1.03, 1.13) + df_filtered["alcohol"] = df["alcohol"] * rnd_drift + logger.warning(f"Random drif generated for alchol with coefficient {rnd_drift}.") + # Sample up to 100 instances + df_current = df_filtered.sample(min(100, len(df_filtered)), random_state=0) + return df_current + + +def data_preparation( + dataset: tuple[pd.DataFrame, pd.Series], split: float = 0.2, seed: int = 42 +) -> tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]: + """ + Prepares the dataset for training: + - Renames any problematic columns (e.g., replaces slashes). + - Splits the dataset into train and test sets. + + Args: + dataset (tuple): Tuple containing (X, y). + split (float, optional): Proportion of the dataset to include in the test split (default: 0.2). + seed (int, optional): Random seed for reproducibility (default: 42). + + Returns: + tuple: + - X_train (pd.DataFrame): Training features. + - X_test (pd.DataFrame): Testing features. + - y_train (pd.Series): Training labels. + - y_test (pd.Series): Testing labels. + """ + X, y = dataset + logger = get_logger() + # Ensure column names are safe (replace slashes with underscores) + logger.info("Replacing / with - in column names...") + X.columns = X.columns.str.replace("/", "_") + + # Split into train/test + logger.info(f"Preparing dataset: split={split}, seed={seed}...") + X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=split, random_state=seed) + logger.info( + f"Dataset split into {X_tr.shape[0]} train and {X_te.shape[0]} test samples." + ) + return (X_tr, X_te, y_tr, y_te) + + +if __name__ == "__main__": + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py b/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py index 97af243..56b6bdc 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py @@ -1,23 +1,107 @@ -# src/monitor.py """ -Lettura delle predizioni servite, verifica di drift su Evidently -e scrittura di un report HTML. -Richiamabile via cron o manualmente: python monitor.py run +src/monitor.py +Data drift monitoring module using Evidently. + +- Compares current data to a reference dataset. +- Generates an HTML and JSON report saved to artifacts/. +- Can be called via CLI or integrated as a Prefect task. """ -import typer, pandas as pd -from evidently import Report -from evidently.metrics import DataDriftPreset -app = typer.Typer() +import json +from pathlib import Path + +import pandas as pd +import typer +from evidently.metric_preset import DataDriftPreset +from evidently.report import Report +from src.utils import get_logger +from src.config import CFG + +app = typer.Typer(add_completion=False) + + +def compute_drift( + output_json: Path = Path(CFG.artifacts_dir / "drift_report.json"), +) -> float: + """ + Computes the minimum drift score from an Evidently JSON report. + + Args: + output_json (Path, optional): Path to the JSON drift report (default: artifacts/drift_report.json). + + Returns: + float: The minimum drift score across all monitored features. + """ + logger = get_logger() + logger.info("Computing minimum drift score from report...") + + # Load the JSON report + report = json.loads(output_json.read_text()) + pvals = [] + + # Extract drift scores for each monitored column + for attribute, value in report["metrics"][1]["result"]["drift_by_columns"].items(): + pvals.append(value["drift_score"]) + + # Compute and return the minimum p-value + min_p = min(pvals) + logger.info(f"Minimum drift score detected: {min_p}.") + return min_p -@app.command() -def run(ref_csv: str = "data/reference.csv", - cur_csv: str = "data/current.csv"): - ref, cur = pd.read_csv(ref_csv), pd.read_csv(cur_csv) + +def generate_drift_report( + reference_data: pd.DataFrame, + current_data: pd.DataFrame, + output_path_html: Path, + output_path_json: Path, +) -> Path: + """ + Generates a drift report comparing current data with reference data. + + Args: + reference_data (pd.DataFrame): DataFrame containing the reference dataset. + current_data (pd.DataFrame): DataFrame containing the current dataset to monitor. + output_path_html (Path): Path to save the generated HTML report. + output_path_json (Path): Path to save the generated JSON report. + + Returns: + Path: Path to the generated JSON report (for downstream processing). + """ + logger = get_logger() + logger.info("Starting drift report generation...") + + # Initialize Evidently report with DataDriftPreset report = Report(metrics=[DataDriftPreset()]) - report.run(ref, cur) - report.save_html("artifacts/drift_report.html") - typer.echo("✅ Drift report scritto in artifacts/drift_report.html") + report.run(reference_data=reference_data, current_data=current_data) + + # Save the report as both HTML and JSON + output_path_html.parent.mkdir(exist_ok=True) + report.save_html(str(output_path_html)) + report.save_json(str(output_path_json)) + logger.info(f"Drift report saved to {output_path_html} and {output_path_json}.") + return output_path_json + + +def run( + reference_data: pd.DataFrame, + current_data: pd.DataFrame, + output_html: Path = Path(CFG.artifacts_dir / "drift_report.html"), + output_json: Path = Path(CFG.artifacts_dir / "drift_report.json"), +) -> Path: + """ + CLI entry point for generating a drift report. + + Args: + reference_data (pd.DataFrame): DataFrame containing the reference dataset. + current_data (pd.DataFrame): DataFrame containing the current dataset. + output_html (Path, optional): Output path for the HTML report (default: artifacts/drift_report.html). + output_json (Path, optional): Output path for the JSON report (default: artifacts/drift_report.json). + + Returns: + Path: Path to the generated JSON report. + """ + return generate_drift_report(reference_data, current_data, output_html, output_json) + if __name__ == "__main__": app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py deleted file mode 100644 index 322bcc4..0000000 --- a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py +++ /dev/null @@ -1,101 +0,0 @@ -# src/pipeline.py -"""Prefect 2 flows: - • train_sweep grid-search parallelo (solo metadati) - • train_best recupera best hp + addestra salvando modello - • serve_best scarica modello migliore e lo serve via FastAPI -""" -from itertools import product -from pathlib import Path -from subprocess import Popen -import tempfile - -import mlflow -from prefect import flow, task, get_run_logger - -from src.config import CFG -from src.train import run as train_run -from src.serve import run as serve_run - -# --------------------------------------------------------------------------- -# Tasks -# --------------------------------------------------------------------------- - - -@task -def train_task(n_estimators: int, max_depth: int | None): - return train_run(n_estimators=n_estimators, max_depth=max_depth, log_model=False) - - -@task -def find_best_params(metric: str = "accuracy") -> dict: - logger = get_run_logger() - runs = mlflow.search_runs(order_by=[f"metrics.{metric} DESC"], max_results=1) - best = runs.iloc[0] - params = { - "n_estimators": int(best["params.n_estimators"]), - "max_depth": None - if best["params.max_depth"] in ("", "None") - else int(best["params.max_depth"]), - } - logger.info(f"Best params found: {params}") - return params - - -@task -def download_best_model(metric: str = "accuracy") -> Path: - logger = get_run_logger() - EXP_ID = mlflow.get_experiment_by_name(CFG.experiment_name).experiment_id - runs = mlflow.search_runs( - experiment_ids=[EXP_ID], order_by=[f"metrics.{metric} DESC"], max_results=1 - ) - run_id = runs.iloc[0]["run_id"] - - client = mlflow.MlflowClient() - art = client.list_artifacts(run_id) - pkl_path = next(a.path for a in art if a.path.endswith(".pkl")) - - tmp_dir = tempfile.mkdtemp() - local_path = Path(client.download_artifacts(run_id, pkl_path, tmp_dir)) - logger.info(f"Downloaded best model to: {local_path}") - return local_path - - -@task -def serve_model(model_path: Path): - # Avvia l'API in un processo dedicato (non blocca il flow calling) - Popen(["python", "-m", "src.serve", "--model-path", str(model_path)]) - - -# --------------------------------------------------------------------------- -# Flows -# --------------------------------------------------------------------------- - - -@flow(name="train_sweep") -def train_sweep(): - grid = { - "n_estimators": [50, 100, 200], - "max_depth": [None, 5, 10], - } - for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): - train_task.submit(n_estimators=n_est, max_depth=m_depth) - - -@flow(name="train_best") -def train_best(): - params = find_best_params() - # unpack dict in **kwargs, salva anche il modello - train_run(**params, log_model=True) - - -@flow(name="serve_best") -def serve_best(): - model_path = download_best_model() - serve_model(model_path) - - -if __name__ == "__main__": - import sys - - # Facile CLI: python -m src.pipeline - locals()[sys.argv[1]]() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_ml.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_ml.py new file mode 100644 index 0000000..a43d425 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_ml.py @@ -0,0 +1,195 @@ +""" +src/pipeline_ml.py +Prefect 2 flows for the Wine Quality ML pipeline. + +Flows: +- train_experiment: Performs a grid search (hyperparameter sweep) without saving models. +- train_best: Trains the best model based on MLflow runs and saves the model artifact. +- serve_best: Downloads the best saved model and serves it through a FastAPI endpoint. +""" + +from itertools import product +from pathlib import Path +import tempfile + +import mlflow +from prefect import flow, task + +from src.config import CFG +from src.data import data_collection as data_coll_run, data_preparation as data_prep_run +from src.train import run as train_run +from src.serve import deploy +from src.utils import get_logger + +# --------------------------------------------------------------------------- +# Tasks +# --------------------------------------------------------------------------- + +@task +def data_collection_task() -> tuple: + """ + Collects the dataset (features and labels). + + Returns: + tuple: (X, y) dataset. + """ + return data_coll_run() + +@task +def data_preparation_task(dataset: tuple) -> tuple: + """ + Prepares the dataset for training: + - Fixes feature names. + - Splits into train/test sets. + + Args: + dataset (tuple): (X, y) dataset. + + Returns: + tuple: (X_train, X_test, y_train, y_test). + """ + return data_prep_run(dataset) + +@task +def model_training_task( + dataset: tuple, n_estimators: int, max_depth: int | None, log_model: bool = False +) -> float: + """ + Trains a RandomForest model and optionally logs it to MLflow. + + Args: + dataset (tuple): Prepared train/test splits. + n_estimators (int): Number of trees. + max_depth (int | None): Maximum depth per tree. + log_model (bool): Whether to save model artifact to MLflow. + + Returns: + float: Model accuracy. + """ + return train_run(dataset, n_estimators=n_estimators, max_depth=max_depth, log_model=log_model) + +@task +def find_best_params_task(metric: str = "accuracy") -> tuple: + """ + Retrieves the best model hyperparameters from MLflow by specified metric. + + Args: + metric (str, optional): Metric to order by (default: 'accuracy'). + + Returns: + tuple: (n_estimators, max_depth) for best model. + """ + logger = get_logger() + logger.info(f"Searching best hyperparameters by {metric}...") + + runs = mlflow.search_runs(order_by=[f"metrics.{metric} DESC"], max_results=1) + best = runs.iloc[0] + params = { + "n_estimators": int(best["params.n_estimators"]), + "max_depth": None if best["params.max_depth"] in ("", "None") else int(best["params.max_depth"]), + } + logger.info(f"Best hyperparameters found: n_estimators={params['n_estimators']}, max_depth={params['max_depth']}.") + return params["n_estimators"], params["max_depth"] + +@task +def download_best_model(metric: str = "accuracy") -> Path: + """ + Downloads the best model artifact from MLflow. + + Args: + metric (str, optional): Metric to order by (default: 'accuracy'). + + Returns: + Path: Local path to downloaded model (.pkl file). + """ + logger = get_logger() + logger.info(f"Downloading best model artifact in experiment {CFG.experiment_name}...") + + exp = mlflow.get_experiment_by_name(CFG.experiment_name) + runs = mlflow.search_runs( + experiment_ids=[exp.experiment_id], order_by=[f"metrics.{metric} DESC"], max_results=1 + ) + run_id = runs.iloc[0]["run_id"] + + client = mlflow.MlflowClient() + artifacts = client.list_artifacts(run_id) + pkl_path = next(a.path for a in artifacts if a.path.endswith(".pkl")) + + tmp_dir = tempfile.mkdtemp() + local_path = Path(client.download_artifacts(run_id, pkl_path, tmp_dir)) + logger.info(f"Downloaded best model to: {local_path}") + return local_path + +@task +def serve_model_task(model_path: Path): + """ + Deploys a FastAPI app for model inference. + + Args: + model_path (Path): Path to serialized model (.pkl). + """ + deploy(model_path=model_path) + +# --------------------------------------------------------------------------- +# Flows +# --------------------------------------------------------------------------- + +@flow(name="train_experiment") +def train_experiment(): + """ + End-to-end grid search training flow: + - Collects and prepares dataset. + - Launches multiple model training tasks for different hyperparameters. + - Only logs parameters and metrics, no model artifacts. + """ + logger = get_logger() + logger.info("Starting train_experiment flow...") + + dataset = data_collection_task() + dataset_prep = data_preparation_task(dataset) + + grid = { + "n_estimators": [1, 5, 10], + "max_depth": [None, 5, 10], + } + + for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): + model_training_task.submit( + dataset=dataset_prep, n_estimators=n_est, max_depth=m_depth + ) + +@flow(name="train_best") +def train_best(): + """ + Retrains the best model based on MLflow search: + - Finds best hyperparameters. + - Retrains model and saves it as artifact. + """ + logger = get_logger() + logger.info("Starting train_best flow...") + + dataset = data_collection_task() + dataset_prep = data_preparation_task(dataset) + n_estimators, max_depth = find_best_params_task() + model_training_task( + dataset_prep, n_estimators=n_estimators, max_depth=max_depth, log_model=True + ) + +@flow(name="serve_best") +def serve_best(): + """ + Deploys the best model as a live API: + - Downloads best model from MLflow. + - Builds FastAPI app. + - Launches serving. + """ + logger = get_logger() + logger.info("Starting serve_best flow...") + + model_path = download_best_model() + #api = build_app_task(model_path=model_path) + serve_model_task(model_path) + +if __name__ == "__main__": + import sys + locals()[sys.argv[1]]() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_monitoring.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_monitoring.py new file mode 100644 index 0000000..9209f54 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_monitoring.py @@ -0,0 +1,111 @@ +""" +src/pipeline_monitoring.py +Prefect 2 flows for the Wine Quality ML pipeline. + +Flow: +- monitoring_best: Detects data drift and triggers retraining if significant drift is found. +""" + +import pandas as pd +from pathlib import Path + +from prefect import flow, task +from src.data import data_collection as data_coll_run, new_data_collection as new_data_coll_run +from src.monitor import run as monitoring_run, compute_drift +from src.pipeline_ml import train_best +from src.utils import get_logger + +# --------------------------------------------------------------------------- +# Tasks +# --------------------------------------------------------------------------- + +@task +def data_collection_task() -> tuple: + """ + Collects the original reference dataset. + + Returns: + tuple: (X, y) dataset. + """ + return data_coll_run() + +@task +def get_drift_data_task() -> pd.DataFrame: + """ + Generates a new dataset with simulated data drift. + + Returns: + pd.DataFrame: New batch of data with potential drift. + """ + return new_data_coll_run() + +@task +def create_drift_report_task(current_data: pd.DataFrame, reference_data: pd.DataFrame) -> Path: + """ + Creates a drift monitoring report comparing current batch vs reference. + + Args: + current_data (pd.DataFrame): Current dataset batch. + reference_data (pd.DataFrame): Original reference dataset. + + Returns: + Path: Path to the generated JSON drift report. + """ + return monitoring_run(current_data=current_data, reference_data=reference_data) + +@task +def compute_drift_task(output_json: Path) -> float: + """ + Computes the minimum drift score from the JSON report. + + Args: + output_json (Path): Path to the generated JSON report. + + Returns: + float: Minimum p-value detected across all features. + """ + return compute_drift(output_json) + +@task +def trigger_if_drift_task(min_p: float, threshold: float = 0.05): + """ + Triggers retraining if minimum drift p-value is below the threshold. + + Args: + min_p (float): Minimum p-value detected. + threshold (float, optional): Threshold below which retraining is triggered (default: 0.05). + """ + logger = get_logger() + logger.info("Starting monitoring_best flow...") + + if min_p <= threshold: + logger.warning(f"Drift detected (min_p={min_p}). Launching retraining...") + train_best() + else: + logger.warning(f"No drift detected (min_p={min_p}).") + +# --------------------------------------------------------------------------- +# Flows +# --------------------------------------------------------------------------- + +@flow(name="monitoring_best") +def monitoring_best(): + """ + Drift monitoring flow: + - Collects reference and new batch datasets. + - Generates drift report (HTML + JSON). + - Computes drift metrics. + - Triggers model retraining if drift is significant. + """ + logger = get_logger() + logger.info("Starting monitoring_best flow...") + + training_data, _ = data_collection_task() + new_data = get_drift_data_task() + report_json = create_drift_report_task(current_data=new_data, reference_data=training_data) + p_val = compute_drift_task(report_json) + trigger_if_drift_task(min_p=p_val) + +if __name__ == "__main__": + # Serve this flow with a cron schedule (here: every minute) + monitoring_best.serve(cron="* * * * *") diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py b/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py deleted file mode 100644 index 77875ec..0000000 --- a/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py +++ /dev/null @@ -1,25 +0,0 @@ -# src/retrain.py -""" -Retrain se il drift supera soglia; esegue train.run() con parametri di default. -""" -import json, subprocess, typer, pathlib - -DRIFT_PATH = pathlib.Path("artifacts/drift_report.html") - -app = typer.Typer() - -@app.command() -def run(threshold: float = 0.6): - from bs4 import BeautifulSoup - drift = 0.0 - if DRIFT_PATH.exists(): - soup = BeautifulSoup(DRIFT_PATH.read_text(), "html.parser") - drift = float(soup.select_one("#drift_score").text) - if drift > threshold: - typer.echo(f"⚠️ Drift {drift:.2f} > {threshold}: retraining…") - subprocess.run(["python", "-m", "src.train", "run"]) - else: - typer.echo(f"👍 Drift ok ({drift:.2f} ≤ {threshold})") - -if __name__ == "__main__": - app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py index fc7b4e3..3f0c0fb 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py @@ -1,5 +1,16 @@ -# src/serve.py -"""Inferenza FastAPI: serve un modello RF salvato con joblib.""" +""" +src/serve.py +FastAPI inference service for the Wine Quality model. + +- Loads a RandomForestClassifier model (joblib). +- Serves predictions via a REST API endpoint. +- CLI options allow flexible deployment or local testing. + +Endpoints: +- POST /predict : Accepts wine features and returns the predicted class and probabilities. +""" + +import subprocess from pathlib import Path from typing import Optional @@ -9,15 +20,29 @@ from fastapi import FastAPI from pydantic import BaseModel -from src.config import CFG +from src.utils import get_logger -app_cli = typer.Typer(add_completion=False) +app = typer.Typer(add_completion=False) def build_app(model_path: Path) -> FastAPI: + """ + Constructs the FastAPI app with the loaded model. + + Args: + model_path (Path): Path to the trained model file (.pkl). + + Returns: + FastAPI: Configured FastAPI app with one /predict endpoint. + """ + # Load trained model model = joblib.load(model_path) class Features(BaseModel): + """ + Request body schema for prediction. + """ + alcohol: float malic_acid: float ash: float @@ -28,39 +53,79 @@ class Features(BaseModel): nonflavanoid_phenols: float proanthocyanins: float color_intensity: float - hue: float + hue: float od280_od315_of_diluted_wines: float proline: float + # Initialize FastAPI app api = FastAPI(title="Wine Quality Inference") @api.post("/predict") def predict(feat: Features): + """ + Predicts the wine quality class based on provided features. + + Args: + feat (Features): Input wine features. + + Returns: + dict: Predicted class and probabilities for each class. + """ import pandas as pd + # Convert input features to DataFrame X = pd.DataFrame([feat.dict()]) + + # Predict class and class probabilities proba = model.predict_proba(X)[0].tolist() - return {"class": int(model.predict(X)[0]), "proba": proba} + predicted_class = int(model.predict(X)[0]) + + return {"class": predicted_class, "proba": proba} return api -@app_cli.command() +def deploy(model_path: Path, host: str = "0.0.0.0", port: int = 9000): + """ + Deploys the given FastAPI app with Uvicorn. + + Args: + api (FastAPI): The app to serve. + host (str, optional): Host IP address to bind (default: 0.0.0.0). + port (int, optional): Port number to expose (default: 9000). + """ + logger = get_logger() + logger.info(f"Building FastAPI app for model from {model_path}...") + logger.info(f"Starting Uvicorn server on {host}:{port}...") + subprocess.Popen(["python", "-m", "src.serve", "--model-path", str(model_path)]) + + +@app.command() def run( model_path: Optional[Path] = typer.Option( None, "--model-path", "-m", - help="Percorso del modello .pkl; default = ultimo train locale.", + help="Path to the .pkl model file.", ), host: str = "0.0.0.0", - port: int = 8000, + port: int = 9000, ): - """Avvia l'API sul modello indicato (o su quello di default).""" - path = model_path or (CFG.artifacts_dir / CFG.model_name) - api = build_app(path) + """ + CLI entry point for running the API locally. + + - Loads the model from the given path (or default artifacts directory). + - Builds the FastAPI app. + - Serves the API with Uvicorn. + + Args: + model_path (Optional[Path], optional): Path to the .pkl model file. + host (str, optional): Host IP to bind (default: 0.0.0.0). + port (int, optional): Port to expose the API (default: 9000). + """ + api = build_app(model_path) uvicorn.run(api, host=host, port=port) if __name__ == "__main__": - app_cli() + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py b/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py deleted file mode 100644 index 91e141c..0000000 --- a/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py +++ /dev/null @@ -1,58 +0,0 @@ -# src/sweep.py -""" -Train-sweep demo -——————— -• Ciclo su piccoli hyper-parametri RF -• Log parametri + accuracy su MLflow -• Flow tracciata sulla UI di Prefect 2 (Orion) -""" - -from itertools import product - -import mlflow -from prefect import flow, task -from sklearn.datasets import load_wine -from sklearn.ensemble import RandomForestClassifier -from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split - -# -------------------------------------------------------------------- -# configura MLflow ➜ cartella locale "mlruns" nella root del progetto -mlflow.set_tracking_uri("file:mlruns") - -# -------------------------------------------------------------------- -@task(log_prints=True) -def train_once(n_estimators: int, max_depth: int | None): - """Un singolo addestramento + log su MLflow.""" - X, y = load_wine(return_X_y=True, as_frame=True) - X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42) - - print(f"Training with n_estimators={n_estimators}, max_depth={max_depth}") - with mlflow.start_run(): - mlflow.log_param("n_estimators", n_estimators) - mlflow.log_param("max_depth", max_depth) - - model = RandomForestClassifier( - n_estimators=n_estimators, max_depth=max_depth, random_state=0 - ).fit(X_tr, y_tr) - - acc = accuracy_score(y_te, model.predict(X_te)) - mlflow.log_metric("accuracy", acc) - - return acc - - -@flow(name="train_sweep") -def train_sweep(): - """Grid-search minimal su due hyper-parametri RF.""" - grid = { - "n_estimators": [1, 10, 100], - "max_depth": [None, 5, 10], - } - - for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): - train_once.submit(n_estimators=n_est, max_depth=m_depth) # .submit = task async, appare come sub-run - - -if __name__ == "__main__": - train_sweep() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/train.py b/Academy/AI/2-ML-Model-Lifecycle/src/train.py index 6dfbbd9..e700190 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/train.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/train.py @@ -1,61 +1,111 @@ -# src/train.py -"""Un singolo ciclo di training + logging su MLflow.""" +""" +src/train.py +Model training module for the Wine Quality pipeline. -from pathlib import Path +- Trains a RandomForestClassifier on provided dataset splits. +- Logs parameters, metrics, and optionally the trained model to MLflow. +- Can be executed both via CLI (Typer) or imported in Prefect flows. +""" import joblib import mlflow import typer -from sklearn.datasets import load_wine from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split - from src.config import CFG +from src.utils import get_logger + app = typer.Typer(add_completion=False) -# Configura il backend MLflow (cartella "mlruns" nella root) +# Configure MLflow tracking (uses the experiment defined in CFG) mlflow.set_tracking_uri(CFG.mlflow_uri) -mlflow.set_experiment(CFG.experiment_name) +mlflow.set_experiment(CFG.experiment_name) + + +def train_model( + dataset_split: tuple, n_estimators: int, max_depth: int | None +) -> tuple: + """ + Trains a RandomForest model on the provided dataset. + + Args: + dataset_split (tuple): Tuple of (X_train, X_test, y_train, y_test) splits. + n_estimators (int): Number of trees in the forest. + max_depth (int | None): Maximum depth of the trees (None means no limit). + + Returns: + tuple: + - model (RandomForestClassifier): Trained model. + - acc (float): Accuracy on the test set. + """ + logger = get_logger() + logger.info( + f"Training RandomForest with n_estimators={n_estimators}, max_depth={max_depth}..." + ) -def _train_core(n_estimators: int, max_depth: int | None): - """Ritorna modello addestrato e accuracy.""" - X, y = load_wine(return_X_y=True, as_frame=True) - X.columns = X.columns.str.replace('/', '_') - X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42) + X_tr, X_te, y_tr, y_te = dataset_split + # Train the RandomForest model model = RandomForestClassifier( n_estimators=n_estimators, max_depth=max_depth, random_state=0 ).fit(X_tr, y_tr) + # Compute accuracy on the test set acc = accuracy_score(y_te, model.predict(X_te)) + logger.info(f"Model trained. Accuracy={acc:.4f}.") return model, acc -@app.command() def run( + dataset_split: tuple, n_estimators: int = 200, max_depth: int | None = None, log_model: bool = typer.Option( - True, "--log-model/--no-log-model", help="Salva anche il modello su MLflow." + True, + "--log-model/--no-log-model", + help="Whether to log the model artifact to MLflow.", ), ) -> float: - """Addestra e logga parametri + metriche (+ modello opzionale).""" + """ + Main training entry point. + + - Trains a model using specified hyperparameters. + - Logs training parameters, accuracy metric, and optionally the trained model to MLflow. + + Args: + dataset_split (tuple): Tuple of (X_train, X_test, y_train, y_test) splits. + n_estimators (int, optional): Number of trees (default: 200). + max_depth (int | None, optional): Maximum tree depth (default: None, unlimited). + log_model (bool, optional): If True, logs the model artifact to MLflow (default: True). + + Returns: + float: Accuracy score of the trained model. + """ + logger = get_logger() + logger.info( + f"Running full training pipeline (MLflow logging enabled={log_model})..." + ) + with mlflow.start_run(): + # Log hyperparameters mlflow.log_param("n_estimators", n_estimators) mlflow.log_param("max_depth", max_depth) - model, acc = _train_core(n_estimators, max_depth) + # Train the model + model, acc = train_model(dataset_split, n_estimators, max_depth) + + # Log model accuracy mlflow.log_metric("accuracy", acc) + # Optionally serialize and log the trained model if log_model: outfile = CFG.artifacts_dir / CFG.model_name joblib.dump(model, outfile) mlflow.log_artifact(outfile) - typer.echo(f"✅ accuracy={acc:0.3f}") - return acc # utile se importato come funzione + logger.info("Training run completed and logged to MLflow.") + return acc # Useful for Prefect tasks or external calls if __name__ == "__main__": diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/utils.py b/Academy/AI/2-ML-Model-Lifecycle/src/utils.py new file mode 100644 index 0000000..a7c53ba --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/utils.py @@ -0,0 +1,29 @@ +""" +src/utils.py +Utility functions for the Wine Quality ML project. + +Includes: +- get_logger(): returns a Prefect logger if available, otherwise falls back to standard Python logging. +""" + +import logging + +def get_logger(): + """ + Returns a logger instance. + + - If inside a Prefect flow or task, returns the Prefect run logger. + - If outside Prefect (e.g., during unit tests or CLI usage), returns a standard Python logger. + + This allows all modules to use consistent logging behavior without crashing when Prefect context is missing. + + Returns: + Logger: Either a Prefect run logger or a standard Python logger. + """ + try: + from prefect import get_run_logger + return get_run_logger() + except RuntimeError: + # No active Prefect context; use fallback logger + logging.basicConfig(level=logging.INFO) + return logging.getLogger("fallback") diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/__init__.py b/Academy/AI/2-ML-Model-Lifecycle/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py index 7604141..88dde81 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py @@ -1,15 +1,13 @@ -"""Test basilari sul caricamento e integrità del dataset""" +from src import data -from src.data import load -import pandas as pd +def test_data_collection(): + X, y = data.data_collection() + assert not X.empty + assert len(X) == len(y) + assert "alcohol" in X.columns -def test_split_shapes(): - X_tr, X_te, y_tr, y_te = load() - assert len(X_tr) > 0 and len(X_te) > 0 - assert len(X_tr) == len(y_tr) - assert len(X_te) == len(y_te) - -def test_no_missing_values(): - X_tr, X_te, y_tr, y_te = load() - assert not X_tr.isna().any().any() - assert not X_te.isna().any().any() +def test_data_preparation(): + X, y = data.data_collection() + X_tr, X_te, y_tr, y_te = data.data_preparation((X, y), split=0.2) + assert len(X_tr) + len(X_te) == len(X) + assert len(y_tr) + len(y_te) == len(y) diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py new file mode 100644 index 0000000..5a5c545 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py @@ -0,0 +1,33 @@ +from src import serve +from src.config import CFG + +def test_build_app_and_predict(): + # Carica modello (assicurati che esista già da train_best) + model_path = CFG.artifacts_dir / CFG.model_name + assert model_path.exists(), "Trained model file is missing" + + api = serve.build_app(model_path) + + sample = { + "alcohol": 13.0, + "malic_acid": 2.0, + "ash": 2.5, + "alcalinity_of_ash": 15.0, + "magnesium": 100.0, + "total_phenols": 2.0, + "flavanoids": 2.5, + "nonflavanoid_phenols": 0.3, + "proanthocyanins": 1.9, + "color_intensity": 5.0, + "hue": 1.0, + "od280_od315_of_diluted_wines": 3.0, + "proline": 1000, + } + + from fastapi.testclient import TestClient + client = TestClient(api) + response = client.post("/predict", json=sample) + assert response.status_code == 200 + result = response.json() + assert "class" in result + assert isinstance(result["class"], int) diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py index 3ef3065..eba4412 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py @@ -1,7 +1,8 @@ -# tests/test_train.py -from src.train import RandomForestClassifier, load +from src import data, train -def test_accuracy(): - X_tr, X_te, y_tr, y_te = load() - model = RandomForestClassifier(random_state=0).fit(X_tr, y_tr) - assert model.score(X_te, y_te) > 0.8 +def test_train_model_accuracy_threshold(): + dataset = data.data_collection() + prepared = data.data_preparation(dataset) + model, acc = train.train_model(prepared, n_estimators=10, max_depth=5) + assert acc > 0.5 # basic threshold check + assert hasattr(model, "predict") From eb21bb08ececfa8ad4dc36d4661f045394b873d6 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 5 May 2025 17:17:25 +0200 Subject: [PATCH 16/70] fix readme --- Academy/AI/2-ML-Model-Lifecycle/README.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 30e159f..463d558 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -39,12 +39,6 @@ prefect server start Open: [http://localhost:4200](http://localhost:4200) -In another terminal: - -```bash -prefect agent start -``` - --- ## 3. Run Workflows via `make` From 86805165b98676b75138294eb69008c2e818b27c Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:11:57 +0200 Subject: [PATCH 17/70] fix tests and prepare readme --- Academy/AI/2-ML-Model-Lifecycle/README.md | 332 +++++++++++++----- .../2-ML-Model-Lifecycle/notebooks/.DS_Store | Bin 6148 -> 0 bytes .../notebooks/01_explore_and_train.ipynb | 297 ---------------- .../2-ML-Model-Lifecycle/tests/test_data.py | 32 +- .../2-ML-Model-Lifecycle/tests/test_serve.py | 47 ++- .../2-ML-Model-Lifecycle/tests/test_train.py | 30 +- 6 files changed, 337 insertions(+), 401 deletions(-) delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 463d558..cdbff36 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -1,136 +1,298 @@ -# Wine Quality ML Pipeline – Technical Review Guide -This README provides a step-by-step guide for reviewing the Wine Quality MLOps proof of concept. -All workflows must be tested using the `Makefile` commands only. +# Lab: From Experiments to Production with Wine Classification +Welcome to this live lab! +In the previous lab session we: +1. **Created a classification model** using **Scikit-learn**, leveraging temperature and humidity to predict rain. +2. **Integrated MLflow** to track training parameters, log evaluation metrics, and manage model versions in a structured way. +3. **Explored and compared results** through the MLflow UI interface, reviewing different configurations and loading a saved model for future predictions. ---- +In this session, you will learn how to: +1. **Train a classification model** on a real dataset using `Scikit-learn`. +2. **Manage and track your experiments** with `MLflow`. +3. **Build CI/CD-like workflows** using `Makefile` and `nox`. +4. **Build scalable and orchestrated ML pipelines** using `Prefect`. +5. **Monitor data drift over time** and trigger retraining with `Evidently`. -## 1. Environment Setup +We will follow a step-by-step approach combining **code execution**, **conceptual explanation**, and **UI walkthroughs**. -Create and activate the Conda environment (`lab_env_2`): +--- -```bash -conda env create -f environment.yaml -conda activate lab_env_2 -``` +## 1 Context and Story +A mid‑sized wine producer wants to modernise quality control. +Instead of relying on manual tasting alone, the company decides to analyse +chemical characteristics of each batch and predict the wine’s *cultivar* +(three classes) using machine learning. +The stakes: +- **Repeatable experimentation** – new blends need fresh tuning +- **Fast promotion** – once a model looks good, it must move to production quickly +- **Operational monitoring** – chemical profiles may drift with soil and weather; the system must recognise drift and retrain autonomously ---- +Our lab demonstrates how to meet these requirements **entirely on a laptop**, +using open‑source tools and zero cloud resources. -## 2. Start Required Services +--- +## 2 Dataset Overview – UCI Wine Recognition + +The lab uses the well-known [**Wine Recognition**](https://archive.ics.uci.edu/dataset/109/wine) dataset from the UCI Machine Learning Repository. +Originally collected at the Institute of Oenology and Viticulture in Italy, the dataset contains the results of a chemical analysis of **178 wine samples** from three different *cultivars* (grape varieties) grown in the same region. +Each sample is described by **13 continuous variables** that capture key chemical properties influencing flavour and quality. + +### 2.1 Features +| Feature | Description | Typical Range | +| ------------------------------ | ------------------------------------------- | ------------- | +| `alcohol` | Ethyl-alcohol content (%) | 11 – 14 % | +| `malic_acid` | Malic acid concentration (g/L) | 0.7 – 5.8 | +| `ash` | Total ash (g/100 mL) | 1.3 – 3.3 | +| `alcalinity_of_ash` | Ash alkalinity (mEq NaOH) | 10 – 30 | +| `magnesium` | Magnesium (mg/L) | 70 – 162 | +| `total_phenols` | Total phenolic content (g/L) | 0.9 – 3.9 | +| `flavanoids` | Flavonoid phenols (g/L) | 0.3 – 5.1 | +| `nonflavanoid_phenols` | Non-flavonoid phenols (g/L) | 0.1 – 0.7 | +| `proanthocyanins` | Proanthocyanins (g/L) | 0.4 – 3.6 | +| `color_intensity` | Red/blue color intensity | 1.3 – 13.0 | +| `hue` | Hue at 520 nm relative to 420 nm | 0.4 – 1.8 | +| `od280_od315_of_diluted_wines` | Optical density ratio (aromatic compounds) | 1.3 – 4.0 | +| `proline` | Proline (mg/L) – associated with mouth-feel | 270 – 1680 | + +### 2.2 Target variable: `class` +The classes are almost balanced (59 / 71 / 48 samples): +- **Class 0** – Cultivar A +- **Class 1** – Cultivar B +- **Class 2** – Cultivar C + +### 2.3 Why This Dataset Fits the Demo +1. **Compact and clean** – You can train models in seconds, perfect for live coding. +2. **Chemically interpretable features** – Easy to discuss how drifting alcohol content affects predictions. +3. **Multi-class problem** – Demonstrates probabilities and class selection in the REST API. +4. **No privacy concerns** – The data are public domain, ideal for workshops. + +### 2.4 Synthetic Drift Generation +To illustrate monitoring, the lab creates a *current batch* where `alcohol` values are artificially increased (e.g., harvest with higher sugar). +Evidently then compares this drifted batch to the original reference set, detects the shift and—if severe—triggers retraining through Prefect. -### MLflow UI -```bash -conda activate lab_env_2 -mlflow ui -``` +--- -Open: [http://localhost:5000](http://localhost:5000) +## 3 Tooling Overview – Concept + Our Usage +| Tool | Key Concept | How We Use It in the Lab | +|------|-------------|--------------------------| +| [**Makefile**](https://www.gnu.org/software/make/manual/make.html) | Declarative build/task runner | Simulates Git push events and invokes Nox sessions (`push-feature`, `push-develop`, `push-master`). | +| [**Nox**](https://nox.thea.codes/en/stable/) | Python automation with virtual/conda envs | Acts like GitHub Actions runners; executes lint, tests and Prefect flows in a reproducible and isolated way. | +| [**Prefect**](https://docs.prefect.io/v3/get-started) | Workflow orchestration + observability | Wraps training, serving and monitoring as flows; offers scheduling, retries and a UI at [http://127.0.0.1:4200](http://127.0.0.1:4200). | +| [**MLflow**](https://mlflow.org/) | Experiment tracking & model registry | Logs parameters, metrics and model artefacts; source of truth for the “best model”. UI at [http://127.0.0.1:5000](http://127.0.0.1:5000). | +| [**Scikit-learn**](https://scikit-learn.org/stable/index.html) | ML algorithms and utilities | Provides a fast RandomForest classifier for our demo. | +| [**FastAPI**](https://fastapi.tiangolo.com/) | High‑performance Python web API | Exposes the model at `/predict` with auto‑generated docs at [http://127.0.0.1:9000](http://127.0.0.1:9000). | +| [**Evidently**](https://docs.evidentlyai.com/introduction) | Data drift & model monitoring | Generates HTML/JSON drift reports; triggers retraining when drift is detected. | --- -### Prefect Server +## 4 Environment Setup +### 4.0 Requirements +This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well. +As in the previous lab, in order to execute this laboratory, you will be asked to install a set of tools common in MLOps field. -```bash -conda activate lab_env_2 -prefect server start +### 4.1 Clone the Repository +To start, clone the lab repository by running the following command in the terminal: +```sh + git clone https://github.com/kiratech/labs.git ``` -Open: [http://localhost:4200](http://localhost:4200) +### 4.2 Checkout the Lab Branch +After cloning the repository, checkout the `academy-ai` branch: +```sh + git checkout academy-ai +``` +Then, navigate to the project folder: +```sh + cd labs/Academy/AI/2-ML-Model-Lifecycle +``` +This folder contains resources related to this lab. ---- +### 4.3 Create a Virtual Environment -## 3. Run Workflows via `make` +A virtual environment allows you to isolate the project's dependencies from the system-wide ones. +In the previous lab, we first created the environment and then installed the dependencies. This time we'll do it with a single command, creating the environment with all the necessary dependencies. -All workflows must be executed via the provided `Makefile` in another terminal: -```bash -conda activate lab_env_2 +In your terminal, create a virtual environment from an existing file: + +```sh + conda env create -f environment.yaml ``` -### Feature Branch (Hyperparameter Sweep) +Activate the Virtual Environment: -```bash -make push-feature +```sh + conda activate lab_env_2 ``` -- Runs `train_experiment` on a parameter grid -- Logs metadata only (no model artifact) +You should see the `(lab_env_2)` prefix in the terminal, indicating that the virtual environment is active. ---- +### 4.4 Open the Project in VSCode -### Develop Branch (Train Best Model) +At this point, open VSCode from the file explorer or by running the command: -```bash -make push-develop +```sh + code . ``` -- Runs: `lint`, `tests`, `train_best` -- Trains model using best hyperparams from MLflow -- Logs model artifact to MLflow +### 4.5 Start the services +Open three integrated terminals and in each one activate the environemnt with `conda activate lab_env_2` and then: + +| Terminal | Command | Purpose | +|----------|---------|---------| +| T‑1 | `prefect server start` | Prefect API + UI | +| T‑2 | `mlflow ui` | Experiment tracking UI | +| T‑3 | Run workflow commands (next sections) | + +### 4.6 Open services UI +Open in the browser Prefect and MLflow at: +| Service | Address | +|----------|---------| +| Prefect | [http://127.0.0.1:4200](http://127.0.0.1:4200) | +| MLflow | [http://127.0.0.1:5000](http://127.0.0.1:5000) | --- -### Main Branch (Model Serving) +## 5 Branch Workflows -```bash -make push-main +### 5.1 Feature Branch Workflow +- **Command** +In your terminal, once the conda environment is active, run: +```sh + make push-feature ``` +- **Goal** + Explore the parameter space quickly and cheaply. No model is intended for production at this stage. + We only want evidence that “something promising” exists. +- **What the Command Simulates** + A developer pushes code to **`feature/`**. + CI/CD should **only run experiments**, producing metrics the team can compare later. +- **What It Does** + - A Nox session calls Prefect flow `train_experiment`. + - Nine RandomForest training tasks run in parallel (3  `n_estimators` × 3  `max_depth`). + - Each task logs **parameters + accuracy** to MLflow, but **does not store a model file**. + - Prefect captures task logs and execution graph. +- **What to Explore** + - MLflow UI: nine runs with different hyper‑params, no artifacts saved. + - Prefect UI: one flow, nine parallel tasks—visual confirmation of parallel runs. -- Runs: `lint`, `tests`, `serve_best` -- Starts a FastAPI app at [http://localhost:9000/predict](http://localhost:9000/predict) -- To shutdown the FastAPI server: `kill -9 $(lsof -ti :9000)` - -Example test request: +--- -```bash -curl -X POST http://localhost:9000/predict -H "Content-Type: application/json" \ - -d '{"alcohol":13.0,"malic_acid":2.0,"ash":2.5,"alcalinity_of_ash":15.0, - "magnesium":100.0,"total_phenols":2.0,"flavanoids":2.5, - "nonflavanoid_phenols":0.3,"proanthocyanins":1.9,"color_intensity":5.0, - "hue":1.0,"od280_od315_of_diluted_wines":3.0,"proline":1000}' +### 5.2 Develop Branch Workflow  +- **Command** +In your terminal, once the conda environment is active, run: +```sh + make push-develop ``` +- **Goal** + Promote the best experimental configuration, validate code quality, and log a **deployable model artifact**. +- **What the Command Simulates** + A merge/push to **`develop`**. CI/CD should lint, test, **re‑train with chosen hyper‑params**, and register the resulting model. +- **What It Does** + 1. **Lint** (`ruff via pre‑commit`) and **unit tests** (`pytest`) run first. Build stops on failure. + 2. Prefect flow `train_best` queries MLflow, grabs the run with highest accuracy. + 3. It re‑trains a RandomForest using those parameters on fresh data splits. + 4. Saves the `.pkl` artifact to `artifacts/` and logs it to MLflow. +- **What to Explore** + - Terminal: lint/test output. + - MLflow UI: a new run with an **artifact path**, this is the candidate for production. + - Prefect UI: see the “find best params” task feeding the “train” task. --- -### Monitoring + Drift Detection - -This runs a simulated batch with drift. If drift is detected (p ≤ 0.05), it retriggers training. +### 5.3 Master Branch Workflow  `make push-master` +- **Command** +In your terminal, once the conda environment is active, run: +```sh + make push-main +``` +- **Goal** + Deploy the champion model as an HTTP service usable by downstream teams. +- **What the Command Simulates** + A merge/push to **`main`**. CI/CD should perform a last sanity check, then bring the model online. +- **What It Does** + 1. Re‑runs lint and tests (quick safety net). + 2. Prefect flow `serve_best` downloads the best model artifact from MLflow. + 3. Builds a FastAPI app and launches Uvicorn on **port 9000**. +- **What to Explore** + - Swagger UI at `http://127.0.0.1:9000/docs`: live documentation, try a prediction. + - Prefect UI: a small flow made of download, build app and serve. + - MLflow UI: confirm the run ID of the served model matches the develop stage. +### 5.4 Querying the Best Model + +Once the **main branch workflow** has deployed the FastAPI service, you can send a prediction request directly from the terminal: ```bash -python -m src.pipeline_monitoring +curl -X POST http://localhost:9000/predict \ + -H "Content-Type: application/json" \ + -d '{ + "alcohol": 13.0, + "malic_acid": 2.0, + "ash": 2.5, + "alcalinity_of_ash": 15.0, + "magnesium": 100.0, + "total_phenols": 2.0, + "flavanoids": 2.5, + "nonflavanoid_phenols": 0.3, + "proanthocyanins": 1.9, + "color_intensity": 5.0, + "hue": 1.0, + "od280_od315_of_diluted_wines": 3.0, + "proline": 1000 + }' ``` -Generates: -- A deployment of flow running every minute -- `artifacts/drift_report.html` -- `artifacts/drift_report.json` -- Retraining of best model if triggered +Expected JSON response: ---- - -## 4. Verify Output in UI Tools - -| Component | URL | Check | -|----------|-----|-------| -| **MLflow UI** | http://localhost:5000 | Parameters, metrics, model | -| **Prefect UI** | http://localhost:4200 | Flow runs, logs | -| **FastAPI** | http://localhost:9000/predict | Model serving | -| **Drift Report** | `artifacts/drift_report.html` | Open manually in browser | +```json +{ + "class": 1, + "proba": [0.02, 0.95, 0.03] +} +``` --- -## Summary - -This PoC runs fully locally using: - -- Conda -- Nox -- Make -- MLflow -- Prefect -- FastAPI +## 6 Monitoring & Auto‑Retraining  +- **Command** +In your terminal, once the conda environment is active, run: +```sh + python -m src.pipeline_monitoring monitoring_best +``` +- **Goal** + Detect distributional drift and kick off retraining only when needed. +- **What the Command Simulates** + A **scheduled batch job** (cron in Prefect) that runs nightly, comparing that day’s data against a reference baseline. +- **What It Does** + 1. Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. + 2. Evidently creates an HTML + JSON drift report. + 3. Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. + 4. All actions—report generation and optional retraining—are logged in Prefect and MLflow. +- **What to Explore** + - Drift Report: open `artifacts/drift_report.html`; discuss which features drifted. + - Prefect UI: see conditional branching—one path ends, the other chains into a training flow. + - MLflow UI: a new run appears **only** when drift threshold is exceeded, proving closed‑loop automation. -No Docker or remote services are required. +--- -Please run and verify each `make` command as documented above. +## 7 Conclusions + +Over the course of this lab we have: +- **Simulated a full CI/CD loop locally** + using Makefile to trigger branch‑style workflows and Nox as a lightweight stand‑in for GitHub Actions. +- **Captured the complete model lineage** + in MLflow, from exploratory runs to the promoted, deployable artifact. +- **Orchestrated repeatable pipelines** + with Prefect, gaining retry logic, scheduling, and an audit‑friendly UI. +- **Deployed an inference endpoint** + via FastAPI that is immediately testable through cURL or Swagger docs. +- **Closed the monitoring loop** + by integrating Evidently to detect drift and automatically retrain when data shifts. + +Together these elements demonstrate an end‑to‑end, production‑style MLOps workflow that fits entirely on a developer laptop—yet scales conceptually to cloud or on‑prem environments. +The key takeaway: **tooling synergy** matters more than individual components. +By combining focused, purpose‑built tools, we achieve reproducibility, observability and automation without heavyweight infrastructure. + +## 8 Next steps +TBD \ No newline at end of file diff --git a/Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store b/Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "" - ], - "text/plain": [ - " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", - "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", - "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", - "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", - "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", - "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", - "\n", - " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", - "0 3.06 0.28 2.29 5.64 1.04 \n", - "1 2.76 0.26 1.28 4.38 1.05 \n", - "2 3.24 0.30 2.81 5.68 1.03 \n", - "3 3.49 0.24 2.18 7.80 0.86 \n", - "4 2.69 0.39 1.82 4.32 1.04 \n", - "\n", - " od280/od315_of_diluted_wines proline \n", - "0 3.92 1065.0 \n", - "1 3.40 1050.0 \n", - "2 3.17 1185.0 \n", - "3 3.45 1480.0 \n", - "4 2.93 735.0 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.datasets import load_wine\n", - "\n", - "data = load_wine(as_frame=True)\n", - "\n", - "data.data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2884b86d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['alcohol',\n", - " 'malic_acid',\n", - " 'ash',\n", - " 'alcalinity_of_ash',\n", - " 'magnesium',\n", - " 'total_phenols',\n", - " 'flavanoids',\n", - " 'nonflavanoid_phenols',\n", - " 'proanthocyanins',\n", - " 'color_intensity',\n", - " 'hue',\n", - " 'od280/od315_of_diluted_wines',\n", - " 'proline']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.feature_names" - ] - }, - { - "cell_type": "markdown", - "id": "28e5c13e", - "metadata": {}, - "source": [ - "### Aprire MLflow UI\n", - "Da terminale, nella cartella di progetto:\n", - "```bash\n", - "mlflow ui --port 5001\n", - "```\n", - "e navigate su per esplorare gli esperimenti." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.11.12 ('lab_env_2')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.12" - }, - "vscode": { - "interpreter": { - "hash": "f3091eeb433eba5ffd1439d05e3608594fa2a8935778ae687bbee724d90b3358" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py index 88dde81..f0848f2 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py @@ -1,13 +1,35 @@ +""" +Unit tests for the data module in the Wine Quality ML pipeline. + +Covers: +- Dataset loading +- Train-test splitting logic +""" + from src import data def test_data_collection(): + """ + Test that the wine dataset is loaded correctly with non-empty features and labels. + """ X, y = data.data_collection() - assert not X.empty - assert len(X) == len(y) - assert "alcohol" in X.columns + + # Assert features and labels are not empty and match in length + assert not X.empty, "Feature dataframe is empty" + assert len(X) == len(y), "Mismatch between features and labels" + assert "alcohol" in X.columns, "'alcohol' column should exist in features" def test_data_preparation(): + """ + Test that the dataset is correctly split into train and test sets. + """ X, y = data.data_collection() X_tr, X_te, y_tr, y_te = data.data_preparation((X, y), split=0.2) - assert len(X_tr) + len(X_te) == len(X) - assert len(y_tr) + len(y_te) == len(y) + + # Assert that the train + test sizes add up to the original + assert len(X_tr) + len(X_te) == len(X), "Train + test features mismatch" + assert len(y_tr) + len(y_te) == len(y), "Train + test labels mismatch" + + # Optional: Check that no data was lost and all parts are non-empty + assert not X_tr.empty and not X_te.empty, "Split feature sets are empty" + assert not y_tr.empty and not y_te.empty, "Split label sets are empty" diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py index 5a5c545..8adeb52 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py @@ -1,14 +1,28 @@ -from src import serve -from src.config import CFG +""" +Unit test for FastAPI model serving without relying on a pre-existing trained model. -def test_build_app_and_predict(): - # Carica modello (assicurati che esista già da train_best) - model_path = CFG.artifacts_dir / CFG.model_name - assert model_path.exists(), "Trained model file is missing" +- Creates a dummy RandomForestClassifier +- Saves it to a temporary .pkl file +- Builds the FastAPI app using the test model +- Sends a prediction request to /predict +""" - api = serve.build_app(model_path) +import tempfile +import joblib +from pathlib import Path +from fastapi.testclient import TestClient +from sklearn.ensemble import RandomForestClassifier +import pandas as pd +from src import serve - sample = { +def test_build_app_and_predict(): + """ + Test that the FastAPI app is correctly built with a temporary dummy model, + and that the /predict endpoint returns a valid response. + """ + # --- Create a dummy model and train it on minimal synthetic data --- + model = RandomForestClassifier() + X_dummy = pd.DataFrame([{ "alcohol": 13.0, "malic_acid": 2.0, "ash": 2.5, @@ -22,11 +36,24 @@ def test_build_app_and_predict(): "hue": 1.0, "od280_od315_of_diluted_wines": 3.0, "proline": 1000, - } + }]) + y_dummy = [1] + model.fit(X_dummy, y_dummy) - from fastapi.testclient import TestClient + # --- Save model to a temporary .pkl file --- + with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp: + model_path = Path(tmp.name) + joblib.dump(model, model_path) + + # --- Build FastAPI app using the dummy model --- + api = serve.build_app(model_path) + + # --- Create a test client and send a sample request --- + sample = X_dummy.iloc[0].to_dict() client = TestClient(api) response = client.post("/predict", json=sample) + + # --- Assertions --- assert response.status_code == 200 result = response.json() assert "class" in result diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py index eba4412..52203e0 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py @@ -1,8 +1,30 @@ +""" +Unit test for the train module in the Wine Quality ML pipeline. + +Covers: +- Training a RandomForestClassifier +- Validating model accuracy and structure +""" + from src import data, train def test_train_model_accuracy_threshold(): - dataset = data.data_collection() - prepared = data.data_preparation(dataset) + """ + Test that the train_model function returns a trained model with acceptable accuracy. + + - Uses a real dataset split + - Checks that accuracy is above a baseline threshold + - Verifies that the model supports .predict() + """ + # Load and split the dataset + X, y = data.data_collection() + prepared = data.data_preparation((X, y)) + + # Train a RandomForest model with basic hyperparameters model, acc = train.train_model(prepared, n_estimators=10, max_depth=5) - assert acc > 0.5 # basic threshold check - assert hasattr(model, "predict") + + # Assert accuracy is above 50% (weak sanity check) + assert acc > 0.5, f"Expected accuracy > 0.5, got {acc}" + + # Check that model has a predict method + assert hasattr(model, "predict"), "Model does not implement .predict()" From 3c0b7179790a8b2869d06222880c3ed7619cdbdd Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:23:20 +0200 Subject: [PATCH 18/70] fix readme lint --- Academy/AI/2-ML-Model-Lifecycle/README.md | 57 ++++++++++++++++++----- 1 file changed, 46 insertions(+), 11 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index cdbff36..e74bdf5 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -1,13 +1,15 @@ - # Lab: From Experiments to Production with Wine Classification -Welcome to this live lab! + +Welcome to this live lab! In the previous lab session we: + 1. **Created a classification model** using **Scikit-learn**, leveraging temperature and humidity to predict rain. 2. **Integrated MLflow** to track training parameters, log evaluation metrics, and manage model versions in a structured way. 3. **Explored and compared results** through the MLflow UI interface, reviewing different configurations and loading a saved model for future predictions. In this session, you will learn how to: + 1. **Train a classification model** on a real dataset using `Scikit-learn`. 2. **Manage and track your experiments** with `MLflow`. 3. **Build CI/CD-like workflows** using `Makefile` and `nox`. @@ -19,11 +21,13 @@ We will follow a step-by-step approach combining **code execution**, **conceptua --- ## 1 Context and Story + A mid‑sized wine producer wants to modernise quality control. Instead of relying on manual tasting alone, the company decides to analyse chemical characteristics of each batch and predict the wine’s *cultivar* (three classes) using machine learning. The stakes: + - **Repeatable experimentation** – new blends need fresh tuning - **Fast promotion** – once a model looks good, it must move to production quickly - **Operational monitoring** – chemical profiles may drift with soil and weather; the system must recognise drift and retrain autonomously @@ -39,6 +43,7 @@ Originally collected at the Institute of Oenology and Viticulture in Italy, the Each sample is described by **13 continuous variables** that capture key chemical properties influencing flavour and quality. ### 2.1 Features + | Feature | Description | Typical Range | | ------------------------------ | ------------------------------------------- | ------------- | | `alcohol` | Ethyl-alcohol content (%) | 11 – 14 % | @@ -56,25 +61,29 @@ Each sample is described by **13 continuous variables** that capture key chemica | `proline` | Proline (mg/L) – associated with mouth-feel | 270 – 1680 | ### 2.2 Target variable: `class` + The classes are almost balanced (59 / 71 / 48 samples): + - **Class 0** – Cultivar A - **Class 1** – Cultivar B - **Class 2** – Cultivar C ### 2.3 Why This Dataset Fits the Demo + 1. **Compact and clean** – You can train models in seconds, perfect for live coding. 2. **Chemically interpretable features** – Easy to discuss how drifting alcohol content affects predictions. 3. **Multi-class problem** – Demonstrates probabilities and class selection in the REST API. 4. **No privacy concerns** – The data are public domain, ideal for workshops. ### 2.4 Synthetic Drift Generation + To illustrate monitoring, the lab creates a *current batch* where `alcohol` values are artificially increased (e.g., harvest with higher sugar). Evidently then compares this drifted batch to the original reference set, detects the shift and—if severe—triggers retraining through Prefect. - --- ## 3 Tooling Overview – Concept + Our Usage + | Tool | Key Concept | How We Use It in the Lab | |------|-------------|--------------------------| | [**Makefile**](https://www.gnu.org/software/make/manual/make.html) | Declarative build/task runner | Simulates Git push events and invokes Nox sessions (`push-feature`, `push-develop`, `push-master`). | @@ -88,25 +97,34 @@ Evidently then compares this drifted batch to the original reference set, detect --- ## 4 Environment Setup + ### 4.0 Requirements + This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well. As in the previous lab, in order to execute this laboratory, you will be asked to install a set of tools common in MLOps field. ### 4.1 Clone the Repository + To start, clone the lab repository by running the following command in the terminal: + ```sh git clone https://github.com/kiratech/labs.git ``` ### 4.2 Checkout the Lab Branch + After cloning the repository, checkout the `academy-ai` branch: + ```sh git checkout academy-ai ``` + Then, navigate to the project folder: + ```sh cd labs/Academy/AI/2-ML-Model-Lifecycle ``` + This folder contains resources related to this lab. ### 4.3 Create a Virtual Environment @@ -136,16 +154,18 @@ At this point, open VSCode from the file explorer or by running the command: code . ``` -### 4.5 Start the services +### 4.5 Start the services + Open three integrated terminals and in each one activate the environemnt with `conda activate lab_env_2` and then: | Terminal | Command | Purpose | |----------|---------|---------| | T‑1 | `prefect server start` | Prefect API + UI | | T‑2 | `mlflow ui` | Experiment tracking UI | -| T‑3 | Run workflow commands (next sections) | +| T‑3 | `-` | Run workflow commands (next sections) | ### 4.6 Open services UI + Open in the browser Prefect and MLflow at: | Service | Address | |----------|---------| @@ -157,11 +177,14 @@ Open in the browser Prefect and MLflow at: ## 5 Branch Workflows ### 5.1 Feature Branch Workflow + - **Command** In your terminal, once the conda environment is active, run: + ```sh make push-feature ``` + - **Goal** Explore the parameter space quickly and cheaply. No model is intended for production at this stage. We only want evidence that “something promising” exists. @@ -177,44 +200,50 @@ In your terminal, once the conda environment is active, run: - MLflow UI: nine runs with different hyper‑params, no artifacts saved. - Prefect UI: one flow, nine parallel tasks—visual confirmation of parallel runs. ---- - ### 5.2 Develop Branch Workflow  + - **Command** In your terminal, once the conda environment is active, run: + ```sh make push-develop ``` + - **Goal** Promote the best experimental configuration, validate code quality, and log a **deployable model artifact**. - **What the Command Simulates** A merge/push to **`develop`**. CI/CD should lint, test, **re‑train with chosen hyper‑params**, and register the resulting model. - **What It Does** + 1. **Lint** (`ruff via pre‑commit`) and **unit tests** (`pytest`) run first. Build stops on failure. 2. Prefect flow `train_best` queries MLflow, grabs the run with highest accuracy. 3. It re‑trains a RandomForest using those parameters on fresh data splits. 4. Saves the `.pkl` artifact to `artifacts/` and logs it to MLflow. + - **What to Explore** - Terminal: lint/test output. - MLflow UI: a new run with an **artifact path**, this is the candidate for production. - Prefect UI: see the “find best params” task feeding the “train” task. ---- - ### 5.3 Master Branch Workflow  `make push-master` + - **Command** In your terminal, once the conda environment is active, run: + ```sh make push-main ``` + - **Goal** Deploy the champion model as an HTTP service usable by downstream teams. - **What the Command Simulates** A merge/push to **`main`**. CI/CD should perform a last sanity check, then bring the model online. - **What It Does** + 1. Re‑runs lint and tests (quick safety net). 2. Prefect flow `serve_best` downloads the best model artifact from MLflow. 3. Builds a FastAPI app and launches Uvicorn on **port 9000**. + - **What to Explore** - Swagger UI at `http://127.0.0.1:9000/docs`: live documentation, try a prediction. - Prefect UI: a small flow made of download, build app and serve. @@ -250,25 +279,30 @@ Expected JSON response: "class": 1, "proba": [0.02, 0.95, 0.03] } -``` +``` --- ## 6 Monitoring & Auto‑Retraining  + - **Command** In your terminal, once the conda environment is active, run: + ```sh python -m src.pipeline_monitoring monitoring_best ``` + - **Goal** Detect distributional drift and kick off retraining only when needed. - **What the Command Simulates** A **scheduled batch job** (cron in Prefect) that runs nightly, comparing that day’s data against a reference baseline. - **What It Does** + 1. Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. 2. Evidently creates an HTML + JSON drift report. 3. Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. 4. All actions—report generation and optional retraining—are logged in Prefect and MLflow. + - **What to Explore** - Drift Report: open `artifacts/drift_report.html`; discuss which features drifted. - Prefect UI: see conditional branching—one path ends, the other chains into a training flow. @@ -279,6 +313,7 @@ In your terminal, once the conda environment is active, run: ## 7 Conclusions Over the course of this lab we have: + - **Simulated a full CI/CD loop locally** using Makefile to trigger branch‑style workflows and Nox as a lightweight stand‑in for GitHub Actions. - **Captured the complete model lineage** @@ -295,4 +330,4 @@ The key takeaway: **tooling synergy** matters more than individual components. By combining focused, purpose‑built tools, we achieve reproducibility, observability and automation without heavyweight infrastructure. ## 8 Next steps -TBD \ No newline at end of file +TBD From a35ebc3374d930e6f9af44248bf02daf1a827462 Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:30:45 +0200 Subject: [PATCH 19/70] fix readme lint --- Academy/AI/2-ML-Model-Lifecycle/README.md | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index e74bdf5..943d68f 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -36,6 +36,7 @@ Our lab demonstrates how to meet these requirements **entirely on a laptop**, using open‑source tools and zero cloud resources. --- + ## 2 Dataset Overview – UCI Wine Recognition The lab uses the well-known [**Wine Recognition**](https://archive.ics.uci.edu/dataset/109/wine) dataset from the UCI Machine Learning Repository. @@ -192,11 +193,14 @@ In your terminal, once the conda environment is active, run: A developer pushes code to **`feature/`**. CI/CD should **only run experiments**, producing metrics the team can compare later. - **What It Does** + - A Nox session calls Prefect flow `train_experiment`. - Nine RandomForest training tasks run in parallel (3  `n_estimators` × 3  `max_depth`). - Each task logs **parameters + accuracy** to MLflow, but **does not store a model file**. - Prefect captures task logs and execution graph. + - **What to Explore** + - MLflow UI: nine runs with different hyper‑params, no artifacts saved. - Prefect UI: one flow, nine parallel tasks—visual confirmation of parallel runs. @@ -214,13 +218,14 @@ In your terminal, once the conda environment is active, run: - **What the Command Simulates** A merge/push to **`develop`**. CI/CD should lint, test, **re‑train with chosen hyper‑params**, and register the resulting model. - **What It Does** - + 1. **Lint** (`ruff via pre‑commit`) and **unit tests** (`pytest`) run first. Build stops on failure. 2. Prefect flow `train_best` queries MLflow, grabs the run with highest accuracy. 3. It re‑trains a RandomForest using those parameters on fresh data splits. 4. Saves the `.pkl` artifact to `artifacts/` and logs it to MLflow. - **What to Explore** + - Terminal: lint/test output. - MLflow UI: a new run with an **artifact path**, this is the candidate for production. - Prefect UI: see the “find best params” task feeding the “train” task. @@ -239,15 +244,17 @@ In your terminal, once the conda environment is active, run: - **What the Command Simulates** A merge/push to **`main`**. CI/CD should perform a last sanity check, then bring the model online. - **What It Does** - + 1. Re‑runs lint and tests (quick safety net). 2. Prefect flow `serve_best` downloads the best model artifact from MLflow. 3. Builds a FastAPI app and launches Uvicorn on **port 9000**. - **What to Explore** + - Swagger UI at `http://127.0.0.1:9000/docs`: live documentation, try a prediction. - Prefect UI: a small flow made of download, build app and serve. - MLflow UI: confirm the run ID of the served model matches the develop stage. + ### 5.4 Querying the Best Model Once the **main branch workflow** has deployed the FastAPI service, you can send a prediction request directly from the terminal: @@ -283,10 +290,10 @@ Expected JSON response: --- -## 6 Monitoring & Auto‑Retraining  +## 6 Monitoring & Auto‑Retraining -- **Command** -In your terminal, once the conda environment is active, run: +- **Command** +In your terminal, once the conda environment is active, run: ```sh python -m src.pipeline_monitoring monitoring_best @@ -297,13 +304,14 @@ In your terminal, once the conda environment is active, run: - **What the Command Simulates** A **scheduled batch job** (cron in Prefect) that runs nightly, comparing that day’s data against a reference baseline. - **What It Does** - + 1. Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. 2. Evidently creates an HTML + JSON drift report. 3. Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. 4. All actions—report generation and optional retraining—are logged in Prefect and MLflow. - **What to Explore** + - Drift Report: open `artifacts/drift_report.html`; discuss which features drifted. - Prefect UI: see conditional branching—one path ends, the other chains into a training flow. - MLflow UI: a new run appears **only** when drift threshold is exceeded, proving closed‑loop automation. @@ -329,5 +337,8 @@ Together these elements demonstrate an end‑to‑end, production‑style MLOps The key takeaway: **tooling synergy** matters more than individual components. By combining focused, purpose‑built tools, we achieve reproducibility, observability and automation without heavyweight infrastructure. +--- + ## 8 Next steps + TBD From 01ff67dfc6aa2f37d875ad4c9e759228f9b30614 Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:36:20 +0200 Subject: [PATCH 20/70] fix readme lint --- Academy/AI/2-ML-Model-Lifecycle/README.md | 29 +++++++++++------------ 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 943d68f..e2a81c1 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -1,4 +1,3 @@ - # Lab: From Experiments to Production with Wine Classification Welcome to this live lab! @@ -182,9 +181,9 @@ Open in the browser Prefect and MLflow at: - **Command** In your terminal, once the conda environment is active, run: -```sh - make push-feature -``` + ```sh + make push-feature + ``` - **Goal** Explore the parameter space quickly and cheaply. No model is intended for production at this stage. @@ -209,9 +208,9 @@ In your terminal, once the conda environment is active, run: - **Command** In your terminal, once the conda environment is active, run: -```sh - make push-develop -``` + ```sh + make push-develop + ``` - **Goal** Promote the best experimental configuration, validate code quality, and log a **deployable model artifact**. @@ -235,9 +234,9 @@ In your terminal, once the conda environment is active, run: - **Command** In your terminal, once the conda environment is active, run: -```sh - make push-main -``` + ```sh + make push-main + ``` - **Goal** Deploy the champion model as an HTTP service usable by downstream teams. @@ -292,12 +291,12 @@ Expected JSON response: ## 6 Monitoring & Auto‑Retraining -- **Command** -In your terminal, once the conda environment is active, run: +- **Command** +In your terminal, once the conda environment is active, run: -```sh - python -m src.pipeline_monitoring monitoring_best -``` + ```sh + python -m src.pipeline_monitoring monitoring_best + ``` - **Goal** Detect distributional drift and kick off retraining only when needed. From 07b915344fb6f25070ae04609d97c9a42380cfef Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:38:56 +0200 Subject: [PATCH 21/70] fix readme lint --- Academy/AI/2-ML-Model-Lifecycle/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index e2a81c1..5cef2d9 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -179,7 +179,7 @@ Open in the browser Prefect and MLflow at: ### 5.1 Feature Branch Workflow - **Command** -In your terminal, once the conda environment is active, run: + In your terminal, once the conda environment is active, run: ```sh make push-feature @@ -206,7 +206,7 @@ In your terminal, once the conda environment is active, run: ### 5.2 Develop Branch Workflow  - **Command** -In your terminal, once the conda environment is active, run: + In your terminal, once the conda environment is active, run: ```sh make push-develop @@ -232,7 +232,7 @@ In your terminal, once the conda environment is active, run: ### 5.3 Master Branch Workflow  `make push-master` - **Command** -In your terminal, once the conda environment is active, run: + In your terminal, once the conda environment is active, run: ```sh make push-main @@ -292,7 +292,7 @@ Expected JSON response: ## 6 Monitoring & Auto‑Retraining - **Command** -In your terminal, once the conda environment is active, run: + In your terminal, once the conda environment is active, run: ```sh python -m src.pipeline_monitoring monitoring_best From 362093902045e7ae08b1b590f957caca1ccbc725 Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 8 May 2025 22:17:40 +0200 Subject: [PATCH 22/70] add imgs of workflows --- Academy/AI/2-ML-Model-Lifecycle/README.md | 70 ++++++++++++++---- .../AI/2-ML-Model-Lifecycle/imgs/lab-full.png | Bin 0 -> 1050461 bytes .../2-ML-Model-Lifecycle/imgs/lab-part1.png | Bin 0 -> 1044777 bytes .../2-ML-Model-Lifecycle/imgs/lab-part2.png | Bin 0 -> 1043204 bytes .../2-ML-Model-Lifecycle/imgs/lab-part3.png | Bin 0 -> 1042782 bytes .../2-ML-Model-Lifecycle/imgs/lab-part4.png | Bin 0 -> 1045520 bytes 6 files changed, 55 insertions(+), 15 deletions(-) create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-full.png create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-part1.png create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-part2.png create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-part3.png create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-part4.png diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 5cef2d9..5eca5b9 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -82,11 +82,13 @@ Evidently then compares this drifted batch to the original reference set, detect --- -## 3 Tooling Overview – Concept + Our Usage +## 3 Tooling Overview + +### 3.1 Tools, concepts and our usage | Tool | Key Concept | How We Use It in the Lab | |------|-------------|--------------------------| -| [**Makefile**](https://www.gnu.org/software/make/manual/make.html) | Declarative build/task runner | Simulates Git push events and invokes Nox sessions (`push-feature`, `push-develop`, `push-master`). | +| [**Makefile**](https://www.gnu.org/software/make/manual/make.html) | Declarative build/task runner | Simulates Git push events and invokes Nox sessions (`push-feature`, `push-develop`, `push-main`). | | [**Nox**](https://nox.thea.codes/en/stable/) | Python automation with virtual/conda envs | Acts like GitHub Actions runners; executes lint, tests and Prefect flows in a reproducible and isolated way. | | [**Prefect**](https://docs.prefect.io/v3/get-started) | Workflow orchestration + observability | Wraps training, serving and monitoring as flows; offers scheduling, retries and a UI at [http://127.0.0.1:4200](http://127.0.0.1:4200). | | [**MLflow**](https://mlflow.org/) | Experiment tracking & model registry | Logs parameters, metrics and model artefacts; source of truth for the “best model”. UI at [http://127.0.0.1:5000](http://127.0.0.1:5000). | @@ -94,6 +96,35 @@ Evidently then compares this drifted batch to the original reference set, detect | [**FastAPI**](https://fastapi.tiangolo.com/) | High‑performance Python web API | Exposes the model at `/predict` with auto‑generated docs at [http://127.0.0.1:9000](http://127.0.0.1:9000). | | [**Evidently**](https://docs.evidentlyai.com/introduction) | Data drift & model monitoring | Generates HTML/JSON drift reports; triggers retraining when drift is detected. | +### 3.2 Why We Simulate GitHub Actions Locally + +[End-to-End Workflow](imgs/lab-full.png) + +In production we would rely on **real Git events and GitHub Actions**: + +| Real event | Real GH Action (example) | Purpose | +| ----------------------------------------- | --------------------------------------------- | ----------------------------------- | +| `git push feature/experiments` | `ci-feature.yml` → sweep experiment | Run many trials, log only metrics | +| `git merge feature/experiments (into develop)` | `ci-develop.yml` → lint + tests + train best | Promote best params, prepare model | +| `git merge develop (into main)` | `ci-main.yml` → lint + tests + deploy best | Bring model online | + +For a laboratory setting those actions pose two practical problems: + +1. **Concurrency & permissions** – the whole group cannot push to `develop` or `main` at the same time without overwriting one another. +2. **Infrastructure dependencies** – real GH Actions would need publicly reachable Prefect and MLflow endpoints; everyone would have to expose tunnels or deploy cloud services. + +To keep the experience **100 % local**, we introduce a one-to-one mapping: + +| Simulated command | What it stands for | +| --------------------------------------------------- | ------------------------------------------------------ | +| `make push-feature` | `git push feature/experiments` trigger GH Action `ci-feature.yml` | +| `make push-develop` | `git merge feature/experiments (into develop)` trigger GH Action `ci-develop.yml` | +| `make push-main` | `git merge develop (into main)` trigger GH Action `ci-main.yml` | +| `python -m src.pipeline_monitoring monitoring_best` | Nightly cron job in GH Actions or Prefect Cloud | + +Under the hood **Nox** replaces the GitHub runner: +`make push-*` commands launch the appropriate nox sessions (lint / tests / Prefect flows) inside a shared Conda environment, reproducing exactly what would happen in cloud CI—but without leaving the laptop. + --- ## 4 Environment Setup @@ -176,8 +207,17 @@ Open in the browser Prefect and MLflow at: ## 5 Branch Workflows +### 5.0 What we will do + +A production-style GitHub Actions workflow we want to **experience locally**. +In the laboratory we replay each numbered block with Makefile + Nox + Prefect, avoiding concurrent pushes and external infrastructure. + +[End-to-End Workflow](imgs/lab-full.png) + ### 5.1 Feature Branch Workflow +[Feature Workflow](imgs/lab-part1.png) + - **Command** In your terminal, once the conda environment is active, run: @@ -194,7 +234,7 @@ Open in the browser Prefect and MLflow at: - **What It Does** - A Nox session calls Prefect flow `train_experiment`. - - Nine RandomForest training tasks run in parallel (3  `n_estimators` × 3  `max_depth`). + - 9 RandomForest training tasks run in parallel (3  `n_estimators` × 3  `max_depth`). - Each task logs **parameters + accuracy** to MLflow, but **does not store a model file**. - Prefect captures task logs and execution graph. @@ -205,6 +245,8 @@ Open in the browser Prefect and MLflow at: ### 5.2 Develop Branch Workflow  +[Develop Workflow](imgs/lab-part2.png) + - **Command** In your terminal, once the conda environment is active, run: @@ -229,7 +271,9 @@ Open in the browser Prefect and MLflow at: - MLflow UI: a new run with an **artifact path**, this is the candidate for production. - Prefect UI: see the “find best params” task feeding the “train” task. -### 5.3 Master Branch Workflow  `make push-master` +### 5.3 Main Branch Workflow + +[Main Workflow](imgs/lab-part3.png) - **Command** In your terminal, once the conda environment is active, run: @@ -291,6 +335,8 @@ Expected JSON response: ## 6 Monitoring & Auto‑Retraining +[Feature Workflow](imgs/lab-part4.png) + - **Command** In your terminal, once the conda environment is active, run: @@ -304,10 +350,10 @@ Expected JSON response: A **scheduled batch job** (cron in Prefect) that runs nightly, comparing that day’s data against a reference baseline. - **What It Does** - 1. Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. - 2. Evidently creates an HTML + JSON drift report. - 3. Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. - 4. All actions—report generation and optional retraining—are logged in Prefect and MLflow. + - Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. + - Evidently creates an HTML + JSON drift report. + - Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. + - All actions—report generation and optional retraining—are logged in Prefect and MLflow. - **What to Explore** @@ -321,7 +367,7 @@ Expected JSON response: Over the course of this lab we have: -- **Simulated a full CI/CD loop locally** +- **Simulated a full CI/CD/CT loop locally** using Makefile to trigger branch‑style workflows and Nox as a lightweight stand‑in for GitHub Actions. - **Captured the complete model lineage** in MLflow, from exploratory runs to the promoted, deployable artifact. @@ -335,9 +381,3 @@ Over the course of this lab we have: Together these elements demonstrate an end‑to‑end, production‑style MLOps workflow that fits entirely on a developer laptop—yet scales conceptually to cloud or on‑prem environments. The key takeaway: **tooling synergy** matters more than individual components. 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zk1vu+=0K%tb-=@!zZ;rYH8*AHo>jCq?pn2e@@TY#N#Kx+wc4H0U#@CjHn=x=8hM?+ zWF9`4*oRB$p_RxB>eo*(`+Rq6vUU|0%Ds8Qtm2@|FWY)mD{ADyetS;vepdBy87?jk z`~nD79oQ*o2y`nW_ph=wC(#j z$ULr-TC?9JxPAbQ!=S$K2@nFb!qtt3mh$^)`@=e`S8@7nSls&65#Z&m+;wru;@H@K zh&#~(=Tt7FYJtGQ?to4AnE-z~eYrZ2Nn{begBSsIR~BW=CIWU4??=hSF6|3DNI)5m zS*vq_*6N;!zk~EyuybjVKwL38;=?i-z?EoJbAmYEW16=%{c_TO?|r*C)+OQFxik@# z1)-S?)W!^TO}pZE*>d@J;~vm0?(8n=uxE|N0XUsIa8Bjtt-wc%AaQ$N$zt!b#b5Gs zKq=tuy6aZo9~3|fx>%HeA;6LcpDR)nh`Xna%@EU_h7D!~C|rh01+C$i3^qM9lAvG$7qjI$s+X^f`BSIHC)4&r^WD)Jj0m658bFb#& z3=TLEP~W?<3(U17!vE^72)Uamc3x~MfO9-?kXR>VNW?GHQvqq{_ujmVI;StE8mI!T z?L{trWn4yPPHE7a?wyaJ8R`ogAfg0(Jol|OY;8Z!|#W`{1d({Zq~5sHLxMJM-47?0Q;A5!W#h_^Iw~oFFsE0 z5-6YLJIfYd{@ Date: Thu, 8 May 2025 22:33:35 +0200 Subject: [PATCH 23/70] mv img of workflow --- Academy/AI/2-ML-Model-Lifecycle/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 5eca5b9..cbbfc3a 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -84,6 +84,8 @@ Evidently then compares this drifted batch to the original reference set, detect ## 3 Tooling Overview +[End-to-End Workflow](imgs/lab-full.png) + ### 3.1 Tools, concepts and our usage | Tool | Key Concept | How We Use It in the Lab | @@ -98,8 +100,6 @@ Evidently then compares this drifted batch to the original reference set, detect ### 3.2 Why We Simulate GitHub Actions Locally -[End-to-End Workflow](imgs/lab-full.png) - In production we would rely on **real Git events and GitHub Actions**: | Real event | Real GH Action (example) | Purpose | From adc6fe6493bab6ae96843d77e4a215f049cd3c2e Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 4 Aug 2025 21:08:56 +0200 Subject: [PATCH 24/70] add notebooks --- Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb | 423 ++++++++++++++++++ .../AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb | 239 ++++++++++ 2 files changed, 662 insertions(+) create mode 100644 Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb create mode 100644 Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb new file mode 100644 index 0000000..332158a --- /dev/null +++ b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "ug_3UHSCaaZI" + }, + "source": [ + "## What is Hugging Face and the Hugging Face Hub ?\n", + "\n", + "Hugging Face is an open-source-first AI company that curates Transformers, Diffusers, and other widely used libraries.\n", + "The Hub (https://huggingface.co/) is a **GitHub for models & datasets**:\n", + "- Free hosting for models, datasets, Spaces (demo apps).\n", + "- Built-in versioning, README rendering, and model cards.\n", + "- REST / Python SDK (huggingface_hub) for push-pull, inference endpoints, and gated access control.\n", + "\n", + "In practice you clone/push a repo exactly like Git, but via hf_hub_download() or model.push_to_hub()." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-hTQLYPZdUZp" + }, + "outputs": [], + "source": [ + "!pip install -q huggingface_hub" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UX-GYw-WbGrP" + }, + "source": [ + "\n", + "## What are providers on HF Inference Client and what is novita?\n", + "\n", + "The InferenceClient can route a request to different providers (back-ends):\n", + "- huggingface (default): the standard Inference API hosted by HF.\n", + "- novita (a third-party back-end): offers chat-optimized latency/pricing; API surface matches OpenAI-style /chat/completions.\n", + "\n", + "You switch simply by\n", + "\n", + "```python\n", + "client = InferenceClient(provider=\"novita\", api_key=HF_TOKEN)\n", + "```\n", + "so the same client code can talk to multiple vendors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mZG3akxmdqpT", + "outputId": "456d745e-1ae4-49e7-8940-7a8e613e721f" + }, + "outputs": [], + "source": [ + "import os, getpass\n", + "from huggingface_hub import InferenceClient\n", + "\n", + "# Inserisci il tuo token Hugging Face personale\n", + "os.environ[\"HF_TOKEN\"] = getpass.getpass(\"👉 Inserisci il tuo HF token: \")\n", + "\n", + "# Usa provider \"novita\" per il modello Mistral chat\n", + "client = InferenceClient(provider=\"novita\", api_key=os.environ[\"HF_TOKEN\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-D2_PSUlep8I" + }, + "source": [ + "## Model spotlight - mistralai/Mistral-7B-Instruct-v0.3\n", + "| - | Pros | Cons |\n", + "| ------------------ | -------------------------------------------------------------------------------- | --------------------------------------------------------------------------- |\n", + "| **Quality** | Very strong chat & code for a 7 B model; outperforms LLaMA-7B/13B in many evals. | Still below GPT-4 / Claude-Opus tier on reasoning & long-context. |\n", + "| **Licence** | Apache-2.0 – commercial use allowed, no policy hoops. | Weight access sometimes gated; you need HF/token or local download. |\n", + "| **Speed / Memory** | Fits into 14 GB GPU (8-bit) → cheap to host; responds \\~50-70 tok/s on T4. | Context window 8 K; cannot process very long docs like GPT-4o-128K. |\n", + "| **LoRA** | Adapter \\~120 MB; fine-tune on a Colab T4 in <30 min. | PEFT on Mistral needs to target correct linear layers (`q_proj`, `v_proj`). |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qogkSPAbbiD2" + }, + "source": [ + "## Prompt Engineering - Zero-shot\n", + "\n", + "Ask the model to perform a task without examples: “Translate the sentence to Italian: 'I love coffee'”.\n", + "\n", + "Zero-shot works because large LMs have seen countless instructions during pre-training. It's the fastest way to test a capability." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vd3cTO7LemPx", + "outputId": "f971023b-1d60-4c6d-a6da-437b083c30c2" + }, + "outputs": [], + "source": [ + "prompt = \"A customer is requesting a refund because a product was delivered late. Write a professional reply.\"\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": prompt}\n", + " ],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_UF5mFW1cBNm" + }, + "source": [ + "## Prompt Engineering - Few-shot\n", + "\n", + "Provide 1-3 exemples so the model infers pattern, tone, structure.\n", + "\n", + "Example: \n", + "Q1: Formal -> Casual \n", + "A1: Please contact me. -> Hit me up! \n", + "\n", + "The next transformation aligns with the examples far better than zero-shot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CeOQuFfRdrga", + "outputId": "126939e8-9c72-48fd-a94b-00b5de91ab5f" + }, + "outputs": [], + "source": [ + "prompt = '''Example 1:\n", + "Customer: I received my order late. I'd like a refund.\n", + "Reply: We're sorry for the delay. Your refund will be processed within 3 business days.\n", + "\n", + "Example 2:\n", + "Customer: My package arrived after the estimated delivery date. I want a refund.\n", + "Reply: We apologize for the inconvenience. The refund has been approved and will be issued shortly.\n", + "\n", + "Now write a reply to:\n", + "A customer is requesting a refund because a product was delivered late.\n", + "'''\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ExIj1lZyc9jE" + }, + "source": [ + "## Chain-of-Thought Prompting\n", + "\n", + "Add “Think step by step” (or an explicit reasoning prompt). \n", + "The model reveals intermediate reasoning, often raising factual accuracy and allowing you to inspect/error-correct the chain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-TgOb4owgnHn", + "outputId": "7c8a24dc-f8f3-4de0-ac6c-fef324f80022" + }, + "outputs": [], + "source": [ + "prompt = \"A customer is requesting a refund because a product was delivered late. Think step by step and write a professional reply.\"\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oWUOoci-dQLx" + }, + "source": [ + "## Role Prompting\n", + "\n", + "Pre-frame the assistant persona:\n", + "\"You are an empathetic customer-support agent…\". \n", + "Sets vocabulary, tone, even policy constraints without touching the model weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n5jnEUKwgoVM", + "outputId": "10bc4383-505a-474c-80b9-af01e73c52be" + }, + "outputs": [], + "source": [ + "prompt = \"You are a customer support agent. A customer is asking for a refund due to a late delivery. Write a helpful and empathetic response.\"\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I8fryyF4dgJ2" + }, + "source": [ + "## Prompt Patterns - Delimiters\n", + "\n", + "Wrap user context in clear markers (\"\"\" or ###), e.g.\n", + "\n", + "```txt\n", + "\"\"\"CONTEXT\n", + "long text …\n", + "\"\"\"\n", + "Summarize the context in one bullet.\n", + "```\n", + "\n", + "Prevents the model from mixing instructions with payload; boosts reliability in multi-part prompts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l9doquBSg2jV", + "outputId": "6d34e5a7-0075-40bd-d360-daca4b5b0a07" + }, + "outputs": [], + "source": [ + "prompt = '''Write a professional reply with the following structure:\n", + "1. Greeting\n", + "2. Apology for the delay\n", + "3. Refund details\n", + "4. Closing remark\n", + "\n", + "Customer: I received the product late and would like a refund.'''\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S1ooDMboeG6k" + }, + "source": [ + "## LoRA-style Fine-tuning (simulation vs real)\n", + "\n", + "Simulation: bake policy or domain hints inside the prompt ([POLICY] …). \n", + "Real LoRA: train tiny adapter matrices (≪1 % params) -> insert at Q/V/(K,O) projections. Needs only minutes & single GPU; adapter file <100 MB." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "koM2FhTJg2-q", + "outputId": "26c6f6ba-4ee1-4976-b04a-ed7cdd23e6e8" + }, + "outputs": [], + "source": [ + "# Prompt standard\n", + "prompt = \"A customer is requesting a refund because a product was delivered late. Write a professional reply.\"\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(\"Standard Prompt:\\n\", completion.choices[0].message[\"content\"])\n", + "\n", + "# Prompt 'personalizzato' (simulazione LoRA con istruzioni nel prompt)\n", + "prompt_custom_1 = '''[INTERNAL POLICY: Respond with empathy, offer refund without requiring customer to return the item, and apologize sincerely. Use concise and positive tone.]\n", + "\n", + "A customer is requesting a refund because a product was delivered late. Write a professional reply.'''\n", + "\n", + "completion_custom_1 = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt_custom_1}],\n", + ")\n", + "print(\"Prompt personalizzato:\\n\", completion_custom_1.choices[0].message[\"content\"])\n", + "\n", + "# Prompt 'personalizzato' (simulazione LoRA con istruzioni nel prompt)\n", + "prompt_custom_2 = '''[INTERNAL POLICY: Do not offer refunds, instead offer 15% discount code.]\n", + "\n", + "A customer is requesting a refund because a product was delivered late. Write a professional reply.'''\n", + "\n", + "completion_custom_2 = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt_custom_2}],\n", + ")\n", + "print(\"Prompt personalizzato:\\n\", completion_custom_2.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L6PVrBLGebEB" + }, + "source": [ + "## Prompt for Structured Output (JSON)\n", + "\n", + "Explicitly instruct format + give a skeleton:\n", + "\n", + "\n", + "\n", + "```\n", + "Return ONLY valid JSON:\n", + "{\"greeting\":\"\", \"apology\":\"\", \"resolution\":\"\", \"action_type\":\"\", \"needs_follow_up\":false}\n", + "```\n", + "\n", + "Model tends to obey if the request is precise and examples are consistent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OdBNug_3g3OX", + "outputId": "bcb45f0e-6893-4fba-dada-6e160db982b3" + }, + "outputs": [], + "source": [ + "prompt = '''\n", + "You are a customer support assistant. A customer is requesting a refund because a product arrived late.\n", + "\n", + "Return the response in JSON format with the following fields:\n", + "- greeting\n", + "- apology\n", + "- refund_policy\n", + "- closing\n", + "\n", + "Example format:\n", + "{\n", + " \"greeting\": \"...\",\n", + " \"apology\": \"...\",\n", + " \"refund_policy\": \"...\",\n", + " \"closing\": \"...\"\n", + "}\n", + "'''\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.10.10 ('.venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.10" + }, + "vscode": { + "interpreter": { + "hash": "12a8c86496d70dc5ec458316f0fdc66c09147f19c1386e8b96b40af59bd1e3d4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb new file mode 100644 index 0000000..dc6b886 --- /dev/null +++ b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kWEJ1_vBLlja" + }, + "outputs": [], + "source": [ + "!rm -rf t5-lora-out\n", + "!rm -rf t5-small-lora-adapter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sIvNNZPcg3T-" + }, + "outputs": [], + "source": [ + "!pip install -q transformers datasets peft accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xm3Hrq4o6bGd" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from datasets import Dataset\n", + "df = pd.read_csv(\"customer_support_lora_dataset_250.csv\")\n", + "ds = Dataset.from_pandas(df)\n", + "print(ds[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "X4z3IZjFrwMG" + }, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer, DataCollatorForSeq2Seq\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n", + "\n", + "def preprocess(ex):\n", + " model_inputs = tokenizer(ex[\"input_text\"], max_length=128, truncation=True)\n", + " with tokenizer.as_target_tokenizer():\n", + " labels = tokenizer(ex[\"target_text\"], max_length=128, truncation=True)\n", + " model_inputs[\"labels\"] = labels[\"input_ids\"]\n", + " return model_inputs\n", + "\n", + "ds_tok = ds.map(preprocess, remove_columns=ds.column_names)\n", + "print(ds_tok[0].keys(), \"→ lengths:\", len(ds_tok[0][\"input_ids\"]), len(ds_tok[0][\"labels\"]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qJwCKCFFrKgI" + }, + "outputs": [], + "source": [ + "from transformers import AutoModelForSeq2SeqLM\n", + "from peft import LoraConfig, get_peft_model, TaskType\n", + "\n", + "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", + "\n", + "lora_cfg = LoraConfig(\n", + " task_type = TaskType.SEQ_2_SEQ_LM,\n", + " r = 8,\n", + " lora_alpha = 16,\n", + " target_modules = [\"q\", \"v\"],\n", + " lora_dropout = 0.05,\n", + " bias = \"none\"\n", + ")\n", + "\n", + "peft_model = get_peft_model(base_model, lora_cfg)\n", + "peft_model.print_trainable_parameters()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "_zAx2Eq2raxS" + }, + "outputs": [], + "source": [ + "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq\n", + "\n", + "training_args = Seq2SeqTrainingArguments(\n", + " output_dir = \"./t5-lora-out\",\n", + " per_device_train_batch_size = 16,\n", + " num_train_epochs = 30,\n", + " learning_rate = 5e-4,\n", + " logging_steps = 5,\n", + " save_strategy = \"no\",\n", + " report_to = \"none\",\n", + ")\n", + "\n", + "trainer = Seq2SeqTrainer(\n", + " model = peft_model,\n", + " args = training_args,\n", + " train_dataset = ds_tok,\n", + " data_collator = DataCollatorForSeq2Seq(tokenizer, model=peft_model),\n", + ")\n", + "\n", + "trainer.train()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbw5BG817UvV" + }, + "outputs": [], + "source": [ + "trainer.model.save_pretrained(\"t5-small-lora-adapter\") \n", + "tokenizer.save_pretrained(\"t5-small-lora-adapter\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eG0hnG43qlAM" + }, + "outputs": [], + "source": [ + "from peft import PeftConfig, PeftModel\n", + "from transformers import AutoModelForSeq2SeqLM\n", + "\n", + "cfg = PeftConfig.from_pretrained(\"t5-small-lora-adapter\")\n", + "base_model = AutoModelForSeq2SeqLM.from_pretrained(cfg.base_model_name_or_path)\n", + "model_lora = PeftModel.from_pretrained(base_model, \"t5-small-lora-adapter\")\n", + "\n", + "prompt = \"generate reply: My order arrived late. I want a refund.\"\n", + "inputs = tokenizer(prompt, return_tensors=\"pt\")\n", + "outputs = model_lora.generate(**inputs, max_new_tokens=80)\n", + "\n", + "print(\"→\", tokenizer.decode(outputs[0], skip_special_tokens=True))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A0j9NQUt7e6F" + }, + "outputs": [], + "source": [ + "\n", + "# Compare vanilla T5-small with LoRA fine‑tuned adapter on structured JSON output\n", + "import json, pandas as pd\n", + "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", + "from peft import PeftConfig, PeftModel\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n", + "\n", + "# Base (pre‑trained) model\n", + "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", + "\n", + "# LoRA‑adapted model – make sure this path matches the one used in the training cell\n", + "adapter_path = \"t5-small-lora-adapter\"\n", + "cfg = PeftConfig.from_pretrained(adapter_path)\n", + "ft_model = PeftModel.from_pretrained(\n", + " AutoModelForSeq2SeqLM.from_pretrained(cfg.base_model_name_or_path),\n", + " adapter_path\n", + ")\n", + "\n", + "def generate(model, prompt):\n", + " ids = tokenizer(prompt, return_tensors=\"pt\")\n", + " out = model.generate(**ids, max_new_tokens=120)\n", + " return tokenizer.decode(out[0], skip_special_tokens=True)\n", + "\n", + "test_prompts = [\n", + " \"generate structured_reply: My order arrived late. I want a refund.\",\n", + " \"generate structured_reply: The product I received is damaged..\",\n", + " \"generate structured_reply: I correctly received my order. Thanks!\"\n", + "]\n", + "test_prompts = [\n", + " \"generate structured_reply: My order arrived late. I want a refund.\",\n", + " \"generate structured_reply: The product I received is damaged. What can I do?\",\n", + " \"generate structured_reply: I received the wrong item in my order.\",\n", + " \"generate structured_reply: How can I return an item I purchased last week?\",\n", + " \"generate structured_reply: I never received my order.\",\n", + " \"generate structured_reply: Why was I charged twice for my order?\",\n", + " \"generate structured_reply: I need help tracking my shipment.\",\n", + " \"generate structured_reply: Can I exchange my item for a different size?\"\n", + "]\n", + "\n", + "records = []\n", + "for p in test_prompts:\n", + " records.append({\n", + " \"prompt\": p,\n", + " \"T5-base\": generate(base_model, p),\n", + " \"LoRA\": generate(ft_model, p)\n", + " })\n", + "\n", + "df = pd.DataFrame(records)\n", + "print(df.to_markdown(index=False))\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.10.10 ('.venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.10" + }, + "vscode": { + "interpreter": { + "hash": "12a8c86496d70dc5ec458316f0fdc66c09147f19c1386e8b96b40af59bd1e3d4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 6eb5741b078357659ad143c8868902488da8a12b Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 4 Aug 2025 23:18:35 +0200 Subject: [PATCH 25/70] add readme and contents --- Academy/AI/3-LLM/README.md | 79 ++++++ .../customer_support_lora_dataset_250.csv | 251 ++++++++++++++++++ Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb | 114 ++++++-- .../AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb | 236 ++++++++++++++-- 4 files changed, 630 insertions(+), 50 deletions(-) create mode 100644 Academy/AI/3-LLM/README.md create mode 100644 Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv diff --git a/Academy/AI/3-LLM/README.md b/Academy/AI/3-LLM/README.md new file mode 100644 index 0000000..6bf83bd --- /dev/null +++ b/Academy/AI/3-LLM/README.md @@ -0,0 +1,79 @@ +# Lab: Prompt‑Engineering and LoRA Fine‑Tuning + +Welcome to this live lab! +In the previous lab session we discussed how to: + +1. **Train a classification model** on a real dataset using `Scikit-learn`. +2. **Manage and track your experiments** with `MLflow`. +3. **Build CI/CD-like workflows** using `Makefile` and `nox`. +4. **Build scalable and orchestrated ML pipelines** using `Prefect`. +5. **Monitor data drift over time** and trigger retraining with `Evidently`. + +In this session, you will run two Jupyter notebooks that demonstrate: + +- Practical prompt‑engineering patterns in `notebook 1_LabLLM.ipynb`, the craft of writing clear, explicit instructions for an LLM. +- A LoRA fine‑tuning workflow on model T5‑small in `notebook 2_LabLLM_LoRA.ipynb`, a lightweight fine-tuning technique for transformers. + +We'll leverage two big providers for this lab: + +- **Google Colab**: a free, browser-based Jupyter environment hosted by Google. Colab gives you a temporary Linux VM with Python, CUDA drivers, and optional GPU/TPU. You open a .ipynb notebook, run cells just like in Jupyter, and share links with classmates—no local setup required. + +- **Hugging Face**: A community and tooling hub for modern AI. +It offers: + + - Model Hub: 500 k-plus pre-trained models (transformers, diffusers, etc.). + - Datasets Hub: curated datasets in one-line API. + - Inference API / Endpoints: hosted model inference in the cloud. + - Libraries: transformers, datasets, peft, and more. + + Creating a (free) HF account lets you: + + - Download models. + - Push your own models/adapters/data. + - Generate an access token that notebooks use to call hosted endpoints securely. + +--- + +## Repository contents + +| Path/Name | What it is and why it matters | +| --------------------------------------- | ---------------------------------------------- | +| `notebooks/1_LabLLM.ipynb` | Prompt‑engineering walkthrough (Mistral‑7B) | +| `notebooks/2_LabLLM_LoRA.ipynb` | LoRA adapter training on a 250‑row support set | +| `files/customer_support_lora_dataset_250.csv` | Tiny dataset used in Notebook 2 | +| `README` (this file) | Quickstart and background | + +## Prerequisites + +| Tool / Account | Why you need it | Sign‑up link | +| ------------------------ | -------------------------------------------- | ------------------------------------------------------------------------ | +| **Google account** | Required to open Colab notebooks | [https://accounts.google.com/signup](https://accounts.google.com/signup) | +| **Hugging Face account** | Lets you call hosted models & store adapters | [https://huggingface.co/join](https://huggingface.co/join) | + +### 1 Create your HF account and token + +1. Go to [https://huggingface.co/join](https://huggingface.co/join) and sign up (email and password). +2. Verify your email address (check spam folder too). +3. Navigate to **Settings > Access Tokens > New Token**. +4. Name it e.g. `ai‑academy‑lab` and select **"Read"** scope (fine for this lesson). +5. Copy the token. **You will paste it into the notebook when prompted**. +6. Treat the token like a password and do **not** share or commit it to GitHub. + +### 2 Recommended Colab runtime settings + +- **GPU:** In Colab, go to **Runtime > Change runtime type > Hardware accelerator > GPU**. +- **RAM:** The default *Standard* tier is enough. No paid upgrade needed. + +--- + +## Run the notebooks in Colab + +Open Google Colab and import, everything at root level, the content of the folders notebooks and files. +From now you can continue on the notebooks. + +--- + +## Suggested readings and references + +- [OpenAI Cookbook – GPT‑4 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide): a new prompting guide that lays out a practical structure for building powerful prompts, especially with GPT-4.1. It’s short, clear, and highly effective for anyone working with agents, structured outputs, tool use, or reasoning-heavy tasks. +- [Google Prompt Engineering pdf](https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view): whether you're technical or non-technical, this might be one of the most useful prompt engineering resources out there right now. Google just published a 68-page whitepaper focused on Prompt Engineering (focused on API users), and it goes deep on structure, formatting, config settings, and real examples. diff --git a/Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv b/Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv new file mode 100644 index 0000000..3d10f64 --- /dev/null +++ b/Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv @@ -0,0 +1,251 @@ +input_text,target_text +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I never received my order.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I never received my order.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I never received my order.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb index 332158a..1463611 100644 --- a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb +++ b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb @@ -1,12 +1,37 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prompt-Engineering Lab: Customer-Support Replies with **Mistral-7B-Instruct**\n", + "\n", + "In this lab you will experiment with six classic **prompt patterns**:\n", + "\n", + "1. **Zero-shot**: no examples, just the task. \n", + "2. **Few-shot**: show 1-2 examples first. \n", + "3. **Chain-of-Thought**: ask the model to reason step by step. \n", + "4. **Role / Persona**: tell the model \"You are a ...\". \n", + "5. **Structured output**: force JSON or bullet format. \n", + "6. **System / Policy instructions**: prepend internal guidelines. \n", + "\n", + "By the end you’ll understand how tiny changes in wording can dramatically change an LLM’s answer.\n", + "\n", + "## What is Prompt-Engineering?\n", + "Because large language models don’t have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.is called prompt engineering. Good prompts boost accuracy and consistency without touching the model’s weights." + ] + }, { "cell_type": "markdown", "metadata": { "id": "ug_3UHSCaaZI" }, "source": [ - "## What is Hugging Face and the Hugging Face Hub ?\n", + "## 0 Install Hugging Face client library\n", + "\n", + "We only need **`huggingface_hub`** because we call the hosted Inference API. No heavyweight Transformers install is required.\n", + "\n", + "### What is Hugging Face and the Hugging Face Hub ?\n", "\n", "Hugging Face is an open-source-first AI company that curates Transformers, Diffusers, and other widely used libraries.\n", "The Hub (https://huggingface.co/) is a **GitHub for models & datasets**:\n", @@ -34,12 +59,15 @@ "id": "UX-GYw-WbGrP" }, "source": [ + "## 1 Authenticate and create an `InferenceClient`\n", "\n", - "## What are providers on HF Inference Client and what is novita?\n", + "You need an **access token** to use the Mistral-7B endpoint hosted by provider **\"novita\"** on Hugging Face.\n", + "\n", + "### What are providers on HF Inference Client and what is novita?\n", "\n", "The InferenceClient can route a request to different providers (back-ends):\n", "- huggingface (default): the standard Inference API hosted by HF.\n", - "- novita (a third-party back-end): offers chat-optimized latency/pricing; API surface matches OpenAI-style /chat/completions.\n", + "- novita (a third-party back-end): offers chat-optimized latency/pricing. API surface matches OpenAI-style /chat/completions.\n", "\n", "You switch simply by\n", "\n", @@ -64,10 +92,10 @@ "import os, getpass\n", "from huggingface_hub import InferenceClient\n", "\n", - "# Inserisci il tuo token Hugging Face personale\n", - "os.environ[\"HF_TOKEN\"] = getpass.getpass(\"👉 Inserisci il tuo HF token: \")\n", + "# Paste your personal HF token when prompted\n", + "os.environ[\"HF_TOKEN\"] = getpass.getpass(\"Enter your HF token: \")\n", "\n", - "# Usa provider \"novita\" per il modello Mistral chat\n", + "# Create the client (model is chosen later in each call)\n", "client = InferenceClient(provider=\"novita\", api_key=os.environ[\"HF_TOKEN\"])" ] }, @@ -80,10 +108,10 @@ "## Model spotlight - mistralai/Mistral-7B-Instruct-v0.3\n", "| - | Pros | Cons |\n", "| ------------------ | -------------------------------------------------------------------------------- | --------------------------------------------------------------------------- |\n", - "| **Quality** | Very strong chat & code for a 7 B model; outperforms LLaMA-7B/13B in many evals. | Still below GPT-4 / Claude-Opus tier on reasoning & long-context. |\n", - "| **Licence** | Apache-2.0 – commercial use allowed, no policy hoops. | Weight access sometimes gated; you need HF/token or local download. |\n", - "| **Speed / Memory** | Fits into 14 GB GPU (8-bit) → cheap to host; responds \\~50-70 tok/s on T4. | Context window 8 K; cannot process very long docs like GPT-4o-128K. |\n", - "| **LoRA** | Adapter \\~120 MB; fine-tune on a Colab T4 in <30 min. | PEFT on Mistral needs to target correct linear layers (`q_proj`, `v_proj`). |\n" + "| **Quality** | Very strong chat and code for a 7 B model. Outperforms LLaMA-7B/13B in many evals. | Still below GPT-4 / Claude-Opus tier on reasoning and long-context. |\n", + "| **Licence** | Apache-2.0, commercial use allowed. | Weight access sometimes gated. You need HF/token or local download. |\n", + "| **Speed / Memory** | Fits into 14 GB GPU (8-bit) so it is cheap to host. Responds \\~50-70 tok/s on T4. | Context window 8 K. Cannot process very long docs like GPT-4o-128K. |\n", + "| **LoRA** | Adapter \\~120 MB. Can be fine-tuned on a Colab T4 in <30 min. | PEFT on Mistral needs to target correct linear layers (`q_proj`, `v_proj`). |\n" ] }, { @@ -92,7 +120,7 @@ "id": "qogkSPAbbiD2" }, "source": [ - "## Prompt Engineering - Zero-shot\n", + "## 2 Zero-shot prompt\n", "\n", "Ask the model to perform a task without examples: “Translate the sentence to Italian: 'I love coffee'”.\n", "\n", @@ -112,12 +140,14 @@ "outputs": [], "source": [ "prompt = \"A customer is requesting a refund because a product was delivered late. Write a professional reply.\"\n", + "\n", "completion = client.chat.completions.create(\n", " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[\n", " {\"role\": \"user\", \"content\": prompt}\n", " ],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -127,9 +157,9 @@ "id": "_UF5mFW1cBNm" }, "source": [ - "## Prompt Engineering - Few-shot\n", + "## 3 Few-shot prompt\n", "\n", - "Provide 1-3 exemples so the model infers pattern, tone, structure.\n", + "Provide 1-3 examples so the model infers pattern, tone, structure.\n", "\n", "Example: \n", "Q1: Formal -> Casual \n", @@ -166,6 +196,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -175,7 +206,7 @@ "id": "ExIj1lZyc9jE" }, "source": [ - "## Chain-of-Thought Prompting\n", + "## 4 Chain-of-Thought prompt\n", "\n", "Add “Think step by step” (or an explicit reasoning prompt). \n", "The model reveals intermediate reasoning, often raising factual accuracy and allowing you to inspect/error-correct the chain." @@ -199,6 +230,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -208,7 +240,7 @@ "id": "oWUOoci-dQLx" }, "source": [ - "## Role Prompting\n", + "## 5 Role / Persona prompt\n", "\n", "Pre-frame the assistant persona:\n", "\"You are an empathetic customer-support agent…\". \n", @@ -233,6 +265,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -242,7 +275,7 @@ "id": "I8fryyF4dgJ2" }, "source": [ - "## Prompt Patterns - Delimiters\n", + "## 6 Structured Output\n", "\n", "Wrap user context in clear markers (\"\"\" or ###), e.g.\n", "\n", @@ -253,7 +286,7 @@ "Summarize the context in one bullet.\n", "```\n", "\n", - "Prevents the model from mixing instructions with payload; boosts reliability in multi-part prompts." + "Prevents the model from mixing instructions with payload and boosts reliability in multi-part prompts." ] }, { @@ -280,6 +313,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -289,10 +323,11 @@ "id": "S1ooDMboeG6k" }, "source": [ - "## LoRA-style Fine-tuning (simulation vs real)\n", + "## 7 System / Policy instructions (simulating LoRA-style policies)\n", + "\n", + "Simulation: bake policy or domain hints inside the prompt ([POLICY] ...). Notice how the reply changes when the policy changes.\n", "\n", - "Simulation: bake policy or domain hints inside the prompt ([POLICY] …). \n", - "Real LoRA: train tiny adapter matrices (≪1 % params) -> insert at Q/V/(K,O) projections. Needs only minutes & single GPU; adapter file <100 MB." + "Real LoRA: train tiny adapter matrices (<<1 % params) and insert at Q/V/(K,O) projections of self-attention matrices. Needs only minutes, single GPU and the adapter file is <100 MB." ] }, { @@ -307,16 +342,17 @@ }, "outputs": [], "source": [ - "# Prompt standard\n", + "# Standard prompt\n", "prompt = \"A customer is requesting a refund because a product was delivered late. Write a professional reply.\"\n", "\n", "completion = client.chat.completions.create(\n", " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(\"Standard Prompt:\\n\", completion.choices[0].message[\"content\"])\n", "\n", - "# Prompt 'personalizzato' (simulazione LoRA con istruzioni nel prompt)\n", + "# Policy A: full refund, no return required\n", "prompt_custom_1 = '''[INTERNAL POLICY: Respond with empathy, offer refund without requiring customer to return the item, and apologize sincerely. Use concise and positive tone.]\n", "\n", "A customer is requesting a refund because a product was delivered late. Write a professional reply.'''\n", @@ -325,9 +361,9 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt_custom_1}],\n", ")\n", - "print(\"Prompt personalizzato:\\n\", completion_custom_1.choices[0].message[\"content\"])\n", + "print(\"Policy 1:\\n\", completion_custom_1.choices[0].message[\"content\"])\n", "\n", - "# Prompt 'personalizzato' (simulazione LoRA con istruzioni nel prompt)\n", + "# Policy B: no refund, offer discount code\n", "prompt_custom_2 = '''[INTERNAL POLICY: Do not offer refunds, instead offer 15% discount code.]\n", "\n", "A customer is requesting a refund because a product was delivered late. Write a professional reply.'''\n", @@ -336,7 +372,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt_custom_2}],\n", ")\n", - "print(\"Prompt personalizzato:\\n\", completion_custom_2.choices[0].message[\"content\"])" + "print(\"Policy 2:\\n\", completion_custom_2.choices[0].message[\"content\"])" ] }, { @@ -345,11 +381,9 @@ "id": "L6PVrBLGebEB" }, "source": [ - "## Prompt for Structured Output (JSON)\n", - "\n", - "Explicitly instruct format + give a skeleton:\n", - "\n", + "## 8 Advanced: JSON-formatted reply\n", "\n", + "Explicitly combining role, task, format and give a skeleton:\n", "\n", "```\n", "Return ONLY valid JSON:\n", @@ -395,6 +429,28 @@ ")\n", "print(completion.choices[0].message[\"content\"])" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9 Take-aways\n", + "\n", + "- **Prompt engineering for rapid iteration**: you can prototype different behaviours in minutes, no fine-tuning needed. \n", + "- **System vs. User prompts**: internal policies (system) can override the tone and policy of the final answer. \n", + "- **Structured output**: JSON or numbered lists make post-processing trivial. \n", + "\n", + "### Next experiments\n", + "\n", + "1. Add temperature/top-p arguments to explore creativity vs. safety of the model. \n", + "2. Test the same prompts on a different model (e.g., **Llama-3-8B-Instruct**) to compare style and latency. \n", + "3. Combine with *LoRA-style adapters* (see Notebook 2) for policy adherence without needing bulky prompts. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb index dc6b886..2ae4494 100644 --- a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb +++ b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb @@ -1,5 +1,28 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LoRA Fine-Tuning Lab: T5-small Customer-Support Adapter\n", + "\n", + "Welcome! \n", + "This hands-on notebook will guide you—step by step—through fine-tuning a **T5-small** model with **LoRA (Low-Rank Adaptation)** so it can answer customer-support questions in a consistent, structured style.\n", + "\n", + "## What is LoRA (Low-Rank Adaptation)\n", + "\n", + "Instead of updating all hundreds of millions of parameters, LoRA freezes the original model and inserts two tiny matrices into selected linear layers (often the Q and V projections). Training adjusts only those low-rank “adapter” weights. So you need far less GPU memory, reach good quality with small datasets, and ship adapters (~10 MB) instead of full checkpoints (~2 GB)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0 Environment Clean-up\n", + "Before starting, you may want to delete any previous artefacts (checkpoints, logs, etc.) so the run is fresh and reproducible. \n", + "Feel free to skip this cell if you have nothing to clean." + ] + }, { "cell_type": "code", "execution_count": null, @@ -8,10 +31,25 @@ }, "outputs": [], "source": [ + "# Remove previous training artefacts—run only if you need a fresh start\n", "!rm -rf t5-lora-out\n", "!rm -rf t5-small-lora-adapter" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Install Dependencies\n", + "We rely on the **Hugging Face Transformers** ecosystem plus two helper libraries:\n", + "- **`transformers`**: model and trainer APIs.\n", + "- **`datasets`**: efficient data loading from HF.\n", + "- **`peft`** – adds LoRA and other parameter-efficient methods. \n", + "- **`accelerate`** – handles device placement (CPU / single-GPU / multi-GPU) transparently.\n", + "\n", + "Installation is one time." + ] + }, { "cell_type": "code", "execution_count": null, @@ -20,9 +58,26 @@ }, "outputs": [], "source": [ + "# Transformers, Datasets, PEFT, and Accelerate (quiet install)\n", "!pip install -q transformers datasets peft accelerate" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 Load CSV Dataset and transform it to HF Dataset\n", + "\n", + "Our CSV file has two columns:\n", + "\n", + "| Column name | What it contains |\n", + "|---------------|--------------------------------------------------|\n", + "| `input_text` | A raw customer request or complaint |\n", + "| `target_text` | The ideal structured reply we want the model to generate |\n", + "\n", + "We'll turn the CSV into a **HF Dataset** object so it plays nicely with the Trainer API." + ] + }, { "cell_type": "code", "execution_count": null, @@ -33,9 +88,42 @@ "source": [ "import pandas as pd\n", "from datasets import Dataset\n", + "\n", + "# Read the 250-row customer-support file\n", "df = pd.read_csv(\"customer_support_lora_dataset_250.csv\")\n", "ds = Dataset.from_pandas(df)\n", - "print(ds[0])" + "\n", + "print(\"Sample row:\\n\", ds[0]) # quick sanity check" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 Tokenisation and Label Preparation\n", + "\n", + "Transformer models can’t read raw text, they need **token IDs**.\n", + "For sequence-to-sequence models like T5 we must prepare **two** sequences:\n", + "\n", + "1. **Source** – the customer request (`input_text`) \n", + "2. **Target** – the desired reply (`target_text`) \n", + "\n", + "### Key details:\n", + "\n", + "- We call `tokenizer.as_target_tokenizer()` so the decoder uses its own special prefix tokens. \n", + "- We truncate to 128 tokens to keep batches small on modest GPUs. \n", + "\n", + "### What exactly are \"tokens\" (the 128-token limit)? \n", + "A token is not a word or a single character.\n", + "Transformers work on sub-word units produced by a tokenizer (for T5 that’s a SentencePiece model with a 32 k-item vocabulary). The rules are learned from large corpora and try to strike a balance:\n", + "| Example text | Tokens generated | Notes |\n", + "| ------------- | ---------------------- | ------------------------------------- |\n", + "| `tracking` | `▁track`, `ing` | the leading “▁” marks a word start |\n", + "| `refund` | `▁refund` | common words are often a single token |\n", + "| `extra-large` | `▁extra`, `-`, `large` | punctuation becomes its own token |\n", + "\n", + "Because tokens can be full words or fragments, the length in tokens is usually 1.3-1.6x shorter than counting raw characters but longer than counting full words.\n", + "A 128-token limit therefore fits roughly 75–100 English words (fewer if the text contains many rare names, URLs, or emojis that split into multiple tokens)." ] }, { @@ -50,15 +138,55 @@ "\n", "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n", "\n", - "def preprocess(ex):\n", - " model_inputs = tokenizer(ex[\"input_text\"], max_length=128, truncation=True)\n", + "def preprocess(example):\n", + " # Encode source\n", + " model_inputs = tokenizer(example[\"input_text\"], max_length=128, truncation=True)\n", + " # Encode target as labels\n", " with tokenizer.as_target_tokenizer():\n", - " labels = tokenizer(ex[\"target_text\"], max_length=128, truncation=True)\n", + " labels = tokenizer(example[\"target_text\"], max_length=128, truncation=True)\n", " model_inputs[\"labels\"] = labels[\"input_ids\"]\n", " return model_inputs\n", "\n", "ds_tok = ds.map(preprocess, remove_columns=ds.column_names)\n", - "print(ds_tok[0].keys(), \"→ lengths:\", len(ds_tok[0][\"input_ids\"]), len(ds_tok[0][\"labels\"]))\n" + "\n", + "\n", + "print(\"Keys:\", ds_tok[0].keys(),\n", + " \"| source len:\", len(ds_tok[0][\"input_ids\"]),\n", + " \"| target len:\", len(ds_tok[0][\"labels\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 Build Base Model and LoRA Configuration\n", + "\n", + "Here we load the vanilla **T5-small** (60 M parameters) and wrap it with a `peft.LoraConfig`.\n", + "\n", + "### Key hyper-parameters:\n", + "\n", + "| Parameter | Role | Here |\n", + "|--------------|---------------------------------------------------------------|------|\n", + "| `r` | Rank of the low-rank matrices (higher = more capacity) | 8 |\n", + "| `lora_alpha` | Scaling factor for the adapter’s update | 16 |\n", + "| `target_modules` | Which weight matrices get adapters (we pick **q** & **v**) | [\"q\",\"v\"] |\n", + "| `lora_dropout` | Regularisation inside adapters | 0.05 |\n", + "\n", + "### Why place LoRA adapters on q and v? What about the others?\n", + "\n", + "| Symbol | Full name | Role in self-attention |\n", + "| ------ | ------------------------------- | ------------------------------ |\n", + "| **Q** | **Query** projection | asks “what am I looking for?” |\n", + "| **K** | **Key** projection | represents “what do I have?” |\n", + "| **V** | **Value** projection | holds the information to mix |\n", + "| **O** | **Output** (final linear layer) | re-mixes heads after attention |\n", + "\n", + "A complete attention block has four projection matrices per head. Putting LoRA on all four gives maximum flexibility but also multiplies train-time memory. \n", + "Empirical sweet-spot: Research (LoRA paper §5 and several follow-ups) showed that adapting Q + V captures most task-specific gains while keeping parameter count and GPU RAM minimal. The intuition:\n", + "- Queries (Q) change how each token attends to others.\n", + "- Values (V) change what content is blended once attention scores are computed.\n", + "\n", + "Keys and the output layer matter too, but adjusting them yields diminishing returns for many language-generation tasks." ] }, { @@ -75,16 +203,32 @@ "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", "\n", "lora_cfg = LoraConfig(\n", - " task_type = TaskType.SEQ_2_SEQ_LM,\n", - " r = 8,\n", - " lora_alpha = 16,\n", - " target_modules = [\"q\", \"v\"],\n", + " task_type = TaskType.SEQ_2_SEQ_LM, # generation task\n", + " r = 8, # rank of the LoRA matrices\n", + " lora_alpha = 16, # scaling\n", + " target_modules = [\"q\", \"v\"], # project only query & value matrices\n", " lora_dropout = 0.05,\n", " bias = \"none\"\n", ")\n", "\n", "peft_model = get_peft_model(base_model, lora_cfg)\n", - "peft_model.print_trainable_parameters()\n" + "peft_model.print_trainable_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 Training Arguments and Trainer Loop\n", + "\n", + "Hugging Face `Seq2SeqTrainer` takes care of the full training loop (forward, back-prop, gradient clipping, etc.).\n", + "\n", + "Important flags we set:\n", + "\n", + "- **`per_device_train_batch_size` = 16**, fits on a 12 GB GPU. \n", + "- **`num_train_epochs` = 30**, small dataset needs more passes. \n", + "- **`learning_rate` = 5e-4**, slightly higher than full-fine-tuning, because we’re optimising far fewer weights. \n", + "- **`save_strategy` = \"no\"**, skip checkpoints to save disk. You can change it to `\"epoch\"` if you want them. " ] }, { @@ -115,7 +259,16 @@ " data_collator = DataCollatorForSeq2Seq(tokenizer, model=peft_model),\n", ")\n", "\n", - "trainer.train()\n" + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6 Save Adapter and Tokenizer\n", + "\n", + "LoRA lets us store **only** the lightweight adapter, ~9 MB in this case. The base T5-small weights are **not duplicated**." ] }, { @@ -127,7 +280,16 @@ "outputs": [], "source": [ "trainer.model.save_pretrained(\"t5-small-lora-adapter\") \n", - "tokenizer.save_pretrained(\"t5-small-lora-adapter\")\n" + "tokenizer.save_pretrained(\"t5-small-lora-adapter\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7 Load LoRA-Adapted Model for Inference\n", + "\n", + "We merge the adapter with the frozen base model at load time, then generate a reply for a sample complaint." ] }, { @@ -141,15 +303,33 @@ "from peft import PeftConfig, PeftModel\n", "from transformers import AutoModelForSeq2SeqLM\n", "\n", + "# Load base model\n", "cfg = PeftConfig.from_pretrained(\"t5-small-lora-adapter\")\n", "base_model = AutoModelForSeq2SeqLM.from_pretrained(cfg.base_model_name_or_path)\n", + "# Load LoRA adapter\n", "model_lora = PeftModel.from_pretrained(base_model, \"t5-small-lora-adapter\")\n", "\n", + "# Test input\n", "prompt = \"generate reply: My order arrived late. I want a refund.\"\n", + "# Tokenize the test input\n", "inputs = tokenizer(prompt, return_tensors=\"pt\")\n", + "# Generate reply\n", "outputs = model_lora.generate(**inputs, max_new_tokens=80)\n", "\n", - "print(\"→\", tokenizer.decode(outputs[0], skip_special_tokens=True))\n" + "print(\"LoRA reply:\\n\", tokenizer.decode(outputs[0], skip_special_tokens=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8 Side-by-Side Evaluation (Base vs LoRA)\n", + "\n", + "Let’s run eight realistic prompts through both the vanilla T5-small and our LoRA-adapted version, then print the outputs in a table for quick eyeballing. \n", + "You should notice LoRA replies are:\n", + "- More structured (e.g., include apology and next steps) \n", + "- Shorter and on brand \n", + "- Consistent JSON or bullet style, depending on your `target_text` examples " ] }, { @@ -160,7 +340,6 @@ }, "outputs": [], "source": [ - "\n", "# Compare vanilla T5-small with LoRA fine‑tuned adapter on structured JSON output\n", "import json, pandas as pd\n", "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", @@ -171,7 +350,7 @@ "# Base (pre‑trained) model\n", "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", "\n", - "# LoRA‑adapted model – make sure this path matches the one used in the training cell\n", + "# LoRA‑adapted model, make sure this path matches the one used in the training cell\n", "adapter_path = \"t5-small-lora-adapter\"\n", "cfg = PeftConfig.from_pretrained(adapter_path)\n", "ft_model = PeftModel.from_pretrained(\n", @@ -186,11 +365,6 @@ "\n", "test_prompts = [\n", " \"generate structured_reply: My order arrived late. I want a refund.\",\n", - " \"generate structured_reply: The product I received is damaged..\",\n", - " \"generate structured_reply: I correctly received my order. Thanks!\"\n", - "]\n", - "test_prompts = [\n", - " \"generate structured_reply: My order arrived late. I want a refund.\",\n", " \"generate structured_reply: The product I received is damaged. What can I do?\",\n", " \"generate structured_reply: I received the wrong item in my order.\",\n", " \"generate structured_reply: How can I return an item I purchased last week?\",\n", @@ -209,8 +383,28 @@ " })\n", "\n", "df = pd.DataFrame(records)\n", - "print(df.to_markdown(index=False))\n" + "print(df.to_markdown(index=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9 Next Steps \n", + "\n", + "1. **Quantisation**: combine LoRA with 8-bit weights using `bitsandbytes` library to shrink disk size and speed up inference. \n", + "2. **Hyper-parameter search**: try different ranks (`r`) and target modules (add **k** and **o** matrices) for possibly better accuracy. \n", + "3. **Objective metrics**: integrate BLEU, ROUGE-L, or a custom JSON validator to track quality over epochs. \n", + "4. **Deployment**: merge base + adapter and serve via FastAPI, Streamlit, or Hugging Face Inference Endpoints. \n", + "5. **Prompt scaffolding**: prepend `\"generate structured_reply:\"` automatically so end-users don’t need to remember it. \n", + "\n", + "Happy fine-tuning!" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { From 8f81d6569245814d5fd53a0c7d90090337167999 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 4 Aug 2025 23:22:45 +0200 Subject: [PATCH 26/70] fix readme --- Academy/AI/3-LLM/README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/Academy/AI/3-LLM/README.md b/Academy/AI/3-LLM/README.md index 6bf83bd..4dad1b2 100644 --- a/Academy/AI/3-LLM/README.md +++ b/Academy/AI/3-LLM/README.md @@ -19,15 +19,16 @@ We'll leverage two big providers for this lab: - **Google Colab**: a free, browser-based Jupyter environment hosted by Google. Colab gives you a temporary Linux VM with Python, CUDA drivers, and optional GPU/TPU. You open a .ipynb notebook, run cells just like in Jupyter, and share links with classmates—no local setup required. - **Hugging Face**: A community and tooling hub for modern AI. -It offers: - + + It offers: + - Model Hub: 500 k-plus pre-trained models (transformers, diffusers, etc.). - Datasets Hub: curated datasets in one-line API. - Inference API / Endpoints: hosted model inference in the cloud. - Libraries: transformers, datasets, peft, and more. Creating a (free) HF account lets you: - + - Download models. - Push your own models/adapters/data. - Generate an access token that notebooks use to call hosted endpoints securely. @@ -75,5 +76,5 @@ From now you can continue on the notebooks. ## Suggested readings and references -- [OpenAI Cookbook – GPT‑4 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide): a new prompting guide that lays out a practical structure for building powerful prompts, especially with GPT-4.1. It’s short, clear, and highly effective for anyone working with agents, structured outputs, tool use, or reasoning-heavy tasks. -- [Google Prompt Engineering pdf](https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view): whether you're technical or non-technical, this might be one of the most useful prompt engineering resources out there right now. Google just published a 68-page whitepaper focused on Prompt Engineering (focused on API users), and it goes deep on structure, formatting, config settings, and real examples. +- [OpenAI Cookbook – GPT‑4 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide): a new prompting guide that lays out a practical structure for building powerful prompts, especially with GPT-4.1. It’s short, clear, and highly effective for anyone working with agents, structured outputs, tool use, or reasoning-heavy tasks. +- [Google Prompt Engineering pdf](https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view): whether you're technical or non-technical, this might be one of the most useful prompt engineering resources out there right now. Google just published a 68-page whitepaper focused on Prompt Engineering (focused on API users), and it goes deep on structure, formatting, config settings, and real examples. From fd10ec3124c38500e718e440cfa6443fadf1d207 Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 6 Aug 2025 17:53:45 +0200 Subject: [PATCH 27/70] fix notebooks --- Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb | 94 +++++------ .../AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb | 156 +++++++++++++----- 2 files changed, 156 insertions(+), 94 deletions(-) diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb index 1463611..04bbb67 100644 --- a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb +++ b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb @@ -2,7 +2,9 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "us7zHCUszttj" + }, "source": [ "# Prompt-Engineering Lab: Customer-Support Replies with **Mistral-7B-Instruct**\n", "\n", @@ -12,13 +14,14 @@ "2. **Few-shot**: show 1-2 examples first. \n", "3. **Chain-of-Thought**: ask the model to reason step by step. \n", "4. **Role / Persona**: tell the model \"You are a ...\". \n", - "5. **Structured output**: force JSON or bullet format. \n", + "5. **Structured delimiters**: clearly define context or information. \n", "6. **System / Policy instructions**: prepend internal guidelines. \n", + "7. **Structured JSON-formatted**: force JSON or bullet format. \n", "\n", - "By the end you’ll understand how tiny changes in wording can dramatically change an LLM’s answer.\n", + "By the end you'll understand how tiny changes in wording can dramatically change an LLM's answer.\n", "\n", "## What is Prompt-Engineering?\n", - "Because large language models don’t have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.is called prompt engineering. Good prompts boost accuracy and consistency without touching the model’s weights." + "Because large language models don't have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.is called prompt engineering. Good prompts boost accuracy and consistency without touching the model's weights." ] }, { @@ -81,11 +84,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mZG3akxmdqpT", - "outputId": "456d745e-1ae4-49e7-8940-7a8e613e721f" + "id": "mZG3akxmdqpT" }, "outputs": [], "source": [ @@ -131,11 +130,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vd3cTO7LemPx", - "outputId": "f971023b-1d60-4c6d-a6da-437b083c30c2" + "id": "vd3cTO7LemPx" }, "outputs": [], "source": [ @@ -172,11 +167,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CeOQuFfRdrga", - "outputId": "126939e8-9c72-48fd-a94b-00b5de91ab5f" + "id": "CeOQuFfRdrga" }, "outputs": [], "source": [ @@ -208,7 +199,7 @@ "source": [ "## 4 Chain-of-Thought prompt\n", "\n", - "Add “Think step by step” (or an explicit reasoning prompt). \n", + "Add \"Think step by step\" (or an explicit reasoning prompt). \n", "The model reveals intermediate reasoning, often raising factual accuracy and allowing you to inspect/error-correct the chain." ] }, @@ -216,15 +207,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-TgOb4owgnHn", - "outputId": "7c8a24dc-f8f3-4de0-ac6c-fef324f80022" + "id": "-TgOb4owgnHn" }, "outputs": [], "source": [ - "prompt = \"A customer is requesting a refund because a product was delivered late. Think step by step and write a professional reply.\"\n", + "prompt = \"A customer is requesting a refund because a product was delivered late. Think step by step and explain me your reasonings to write a professional reply.\"\n", "\n", "completion = client.chat.completions.create(\n", " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", @@ -251,11 +238,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n5jnEUKwgoVM", - "outputId": "10bc4383-505a-474c-80b9-af01e73c52be" + "id": "n5jnEUKwgoVM" }, "outputs": [], "source": [ @@ -275,7 +258,7 @@ "id": "I8fryyF4dgJ2" }, "source": [ - "## 6 Structured Output\n", + "## 6 Structured delimiters\n", "\n", "Wrap user context in clear markers (\"\"\" or ###), e.g.\n", "\n", @@ -293,21 +276,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "l9doquBSg2jV", - "outputId": "6d34e5a7-0075-40bd-d360-daca4b5b0a07" + "id": "l9doquBSg2jV" }, "outputs": [], "source": [ - "prompt = '''Write a professional reply with the following structure:\n", + "prompt = '''\n", + "### STRUCTURE\n", "1. Greeting\n", "2. Apology for the delay\n", "3. Refund details\n", "4. Closing remark\n", - "\n", - "Customer: I received the product late and would like a refund.'''\n", + "###\n", + "### CUSTOMER EMAIL\n", + "I received the product late and would like a refund.\n", + "###\n", + "Write a professional reply to customer email using our structure.\n", + "'''\n", "\n", "completion = client.chat.completions.create(\n", " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", @@ -334,11 +318,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "koM2FhTJg2-q", - "outputId": "26c6f6ba-4ee1-4976-b04a-ed7cdd23e6e8" + "id": "koM2FhTJg2-q" }, "outputs": [], "source": [ @@ -350,7 +330,7 @@ " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", "\n", - "print(\"Standard Prompt:\\n\", completion.choices[0].message[\"content\"])\n", + "print(\"Standard Prompt:\\n\", completion.choices[0].message[\"content\"], \"\\n---\")\n", "\n", "# Policy A: full refund, no return required\n", "prompt_custom_1 = '''[INTERNAL POLICY: Respond with empathy, offer refund without requiring customer to return the item, and apologize sincerely. Use concise and positive tone.]\n", @@ -361,7 +341,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt_custom_1}],\n", ")\n", - "print(\"Policy 1:\\n\", completion_custom_1.choices[0].message[\"content\"])\n", + "print(\"Policy 1:\\n\", completion_custom_1.choices[0].message[\"content\"], \"\\n---\")\n", "\n", "# Policy B: no refund, offer discount code\n", "prompt_custom_2 = '''[INTERNAL POLICY: Do not offer refunds, instead offer 15% discount code.]\n", @@ -372,7 +352,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt_custom_2}],\n", ")\n", - "print(\"Policy 2:\\n\", completion_custom_2.choices[0].message[\"content\"])" + "print(\"Policy 2:\\n\", completion_custom_2.choices[0].message[\"content\"], \"\\n---\")" ] }, { @@ -381,7 +361,7 @@ "id": "L6PVrBLGebEB" }, "source": [ - "## 8 Advanced: JSON-formatted reply\n", + "## 8 Structured JSON-formatted\n", "\n", "Explicitly combining role, task, format and give a skeleton:\n", "\n", @@ -397,11 +377,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OdBNug_3g3OX", - "outputId": "bcb45f0e-6893-4fba-dada-6e160db982b3" + "id": "OdBNug_3g3OX" }, "outputs": [], "source": [ @@ -432,7 +408,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "N2Go8Hszztto" + }, "source": [ "## 9 Take-aways\n", "\n", @@ -449,7 +427,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "7yg1gPSZztto" + }, "source": [] } ], @@ -476,4 +456,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb index 2ae4494..611f1f2 100644 --- a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb +++ b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb @@ -2,21 +2,35 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "BVqac1pg809I" + }, "source": [ "# LoRA Fine-Tuning Lab: T5-small Customer-Support Adapter\n", "\n", "Welcome! \n", - "This hands-on notebook will guide you—step by step—through fine-tuning a **T5-small** model with **LoRA (Low-Rank Adaptation)** so it can answer customer-support questions in a consistent, structured style.\n", + "This hands-on notebook will guide you, step by step, through fine-tuning a **T5-small** model with **LoRA (Low-Rank Adaptation)** so it can answer customer-support questions in a consistent, structured style. \n", + "Indeed, out-of-the-box **T5-small** has never seen our customer-support style or policy. \n", + "If you prompt it with “My order arrived late, I want a refund,” it answers vaguely, or not at all. \n", + "\n", + "In this notebook we will:\n", + "\n", + "1. **Measure the baseline** and see how poorly vanilla T5 handles eight real support prompts. \n", + "2. **Attach a tiny LoRA adapter** (\\~ 9 MB, 8-rank) and fine-tune it on **just 250 examples**. \n", + "3. **Re-test the same prompts** to verify that the adapted model now produces concise, policy-compliant replies. \n", + "\n", + "**Key takeaway:** with LoRA we upgrade a generic language model into a task-specialist in ~10 minutes on a free Colab GPU, without touching the original 60 M parameters.\n", "\n", "## What is LoRA (Low-Rank Adaptation)\n", "\n", - "Instead of updating all hundreds of millions of parameters, LoRA freezes the original model and inserts two tiny matrices into selected linear layers (often the Q and V projections). Training adjusts only those low-rank “adapter” weights. So you need far less GPU memory, reach good quality with small datasets, and ship adapters (~10 MB) instead of full checkpoints (~2 GB)." + "Instead of updating all hundreds of millions of parameters, LoRA freezes the original model and inserts two tiny matrices into selected linear layers (often the Q and V projections). Training adjusts only those low-rank “adapter” weights. So you need far less GPU memory, reach good quality with small datasets, and ship adapters (\\~10 MB) instead of full checkpoints (\\~2 GB)." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "XvH1ePiH809K" + }, "source": [ "## 0 Environment Clean-up\n", "Before starting, you may want to delete any previous artefacts (checkpoints, logs, etc.) so the run is fresh and reproducible. \n", @@ -38,7 +52,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "UMkIRIWx809M" + }, "source": [ "## 1 Install Dependencies\n", "We rely on the **Hugging Face Transformers** ecosystem plus two helper libraries:\n", @@ -64,10 +80,47 @@ }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "## 2 Load CSV Dataset and transform it to HF Dataset\n", + "## 2 Baseline Check: How well does vanilla T5-small handle our task?\n", + "\n", + "Before we train anything, let's ask the out-of-the-box model to draft a refund reply. \n", + "Spoiler: its answer will be generic, overly long, or simply unrelated because T5-small has never been told what our support policy or tone should be.\n" + ], + "metadata": { + "id": "ponHU0RFA8Ms" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n", + "model_base = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", + "\n", + "prompt = \"reply to this customer's email: My order arrived late and I want a refund.\"\n", + "inputs = tokenizer(prompt, return_tensors=\"pt\")\n", + "\n", + "# Greedy decode to keep things deterministic and short\n", + "outputs = model_base.generate(**inputs, max_new_tokens=120)\n", + "print(\"Vanilla T5-small says:\\n\")\n", + "print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n" + ], + "metadata": { + "id": "Qjw3r_bMA631" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tkYjmNZ7809N" + }, + "source": [ + "## 3 Load CSV Dataset and transform it to HF Dataset\n", "\n", + "Upload to Colab filesystem the file of our dataset: `files/customer_support_lora_dataset_250`. \n", "Our CSV file has two columns:\n", "\n", "| Column name | What it contains |\n", @@ -93,29 +146,33 @@ "df = pd.read_csv(\"customer_support_lora_dataset_250.csv\")\n", "ds = Dataset.from_pandas(df)\n", "\n", - "print(\"Sample row:\\n\", ds[0]) # quick sanity check" + "print(\"Sample row:\") # quick sanity check\n", + "ds[0]" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "2HlULnbn809P" + }, "source": [ "## 3 Tokenisation and Label Preparation\n", "\n", - "Transformer models can’t read raw text, they need **token IDs**.\n", + "Transformer models can't read raw text, they need **token IDs**.\n", "For sequence-to-sequence models like T5 we must prepare **two** sequences:\n", "\n", - "1. **Source** – the customer request (`input_text`) \n", - "2. **Target** – the desired reply (`target_text`) \n", + "1. **Source** - the customer request (`input_text`) \n", + "2. **Target** - the desired reply (`target_text`) \n", "\n", "### Key details:\n", "\n", "- We call `tokenizer.as_target_tokenizer()` so the decoder uses its own special prefix tokens. \n", - "- We truncate to 128 tokens to keep batches small on modest GPUs. \n", + "- We truncate to 128 tokens to keep batches small on modest GPUs.\n", "\n", "### What exactly are \"tokens\" (the 128-token limit)? \n", "A token is not a word or a single character.\n", - "Transformers work on sub-word units produced by a tokenizer (for T5 that’s a SentencePiece model with a 32 k-item vocabulary). The rules are learned from large corpora and try to strike a balance:\n", + "Transformers work on sub-word units produced by a tokenizer (for T5 that's a SentencePiece model with a 32 k-item vocabulary). The rules are learned from large corpora and try to strike a balance:\n", + "\n", "| Example text | Tokens generated | Notes |\n", "| ------------- | ---------------------- | ------------------------------------- |\n", "| `tracking` | `▁track`, `ing` | the leading “▁” marks a word start |\n", @@ -123,9 +180,20 @@ "| `extra-large` | `▁extra`, `-`, `large` | punctuation becomes its own token |\n", "\n", "Because tokens can be full words or fragments, the length in tokens is usually 1.3-1.6x shorter than counting raw characters but longer than counting full words.\n", - "A 128-token limit therefore fits roughly 75–100 English words (fewer if the text contains many rare names, URLs, or emojis that split into multiple tokens)." + "A 128-token limit therefore fits roughly 75-100 English words (fewer if the text contains many rare names, URLs, or emojis that split into multiple tokens)." ] }, + { + "cell_type": "code", + "source": [ + "ds[0][\"input_text\"]" + ], + "metadata": { + "id": "tRmH0hZpCK0p" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "execution_count": null, @@ -149,15 +217,17 @@ "\n", "ds_tok = ds.map(preprocess, remove_columns=ds.column_names)\n", "\n", - "\n", - "print(\"Keys:\", ds_tok[0].keys(),\n", - " \"| source len:\", len(ds_tok[0][\"input_ids\"]),\n", - " \"| target len:\", len(ds_tok[0][\"labels\"]))" + "print(\"Input: \", ds[0][\"input_text\"])\n", + "print(\"Input tokens: \", len(ds_tok[0][\"input_ids\"]))\n", + "print(\"Target: \", ds[0][\"target_text\"])\n", + "print(\"Target tokens: \", len(ds_tok[0][\"labels\"]))\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "LU43xk3P809R" + }, "source": [ "## 4 Build Base Model and LoRA Configuration\n", "\n", @@ -217,7 +287,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "ezlVpv2R809S" + }, "source": [ "## 5 Training Arguments and Trainer Loop\n", "\n", @@ -264,7 +336,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "WD8UKp4g809T" + }, "source": [ "## 6 Save Adapter and Tokenizer\n", "\n", @@ -279,13 +353,15 @@ }, "outputs": [], "source": [ - "trainer.model.save_pretrained(\"t5-small-lora-adapter\") \n", + "trainer.model.save_pretrained(\"t5-small-lora-adapter\")\n", "tokenizer.save_pretrained(\"t5-small-lora-adapter\")" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "H8U36q-C809T" + }, "source": [ "## 7 Load LoRA-Adapted Model for Inference\n", "\n", @@ -321,7 +397,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "RI_MC1cU809U" + }, "source": [ "## 8 Side-by-Side Evaluation (Base vs LoRA)\n", "\n", @@ -364,14 +442,14 @@ " return tokenizer.decode(out[0], skip_special_tokens=True)\n", "\n", "test_prompts = [\n", - " \"generate structured_reply: My order arrived late. I want a refund.\",\n", - " \"generate structured_reply: The product I received is damaged. What can I do?\",\n", - " \"generate structured_reply: I received the wrong item in my order.\",\n", - " \"generate structured_reply: How can I return an item I purchased last week?\",\n", - " \"generate structured_reply: I never received my order.\",\n", - " \"generate structured_reply: Why was I charged twice for my order?\",\n", - " \"generate structured_reply: I need help tracking my shipment.\",\n", - " \"generate structured_reply: Can I exchange my item for a different size?\"\n", + " \"Compose a response to this customer email: My order arrived late. I want a refund.\",\n", + " \"Draft a reply to this customer message: The product I received is damaged. What can I do?\",\n", + " \"Write a response to this email from a client: I received the wrong item in my order.\",\n", + " \"Create a reply for this customer's email: How can I return an item I purchased last week?\",\n", + " \"Formulate a response to the customer's email: I never received my order.\",\n", + " \"Respond to this message from the customer: Why was I charged twice for my order?\",\n", + " \"Prepare a reply to this client email: I need help tracking my shipment.\",\n", + " \"Construct a response for the customer's message: Can I exchange my item for a different size?\"\n", "]\n", "\n", "records = []\n", @@ -388,9 +466,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "csHeHzww809V" + }, "source": [ - "## 9 Next Steps \n", + "## 9 Next Steps\n", "\n", "1. **Quantisation**: combine LoRA with 8-bit weights using `bitsandbytes` library to shrink disk size and speed up inference. \n", "2. **Hyper-parameter search**: try different ranks (`r`) and target modules (add **k** and **o** matrices) for possibly better accuracy. \n", @@ -403,7 +483,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "TG70O8z6809V" + }, "source": [] } ], @@ -430,4 +512,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 22c391b583aeca53c8b88eca2866ee0809024716 Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 7 Aug 2025 16:21:07 +0200 Subject: [PATCH 28/70] fix notebooks --- Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb | 4 +-- .../AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb | 35 +++++++------------ 2 files changed, 14 insertions(+), 25 deletions(-) diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb index 04bbb67..9936556 100644 --- a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb +++ b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb @@ -21,7 +21,7 @@ "By the end you'll understand how tiny changes in wording can dramatically change an LLM's answer.\n", "\n", "## What is Prompt-Engineering?\n", - "Because large language models don't have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.is called prompt engineering. Good prompts boost accuracy and consistency without touching the model's weights." + "Because large language models don't have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.) is called prompt engineering. Good prompts boost accuracy and consistency without touching the model's weights." ] }, { @@ -456,4 +456,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb index 611f1f2..33913c6 100644 --- a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb +++ b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb @@ -80,18 +80,23 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "ponHU0RFA8Ms" + }, "source": [ "## 2 Baseline Check: How well does vanilla T5-small handle our task?\n", "\n", "Before we train anything, let's ask the out-of-the-box model to draft a refund reply. \n", "Spoiler: its answer will be generic, overly long, or simply unrelated because T5-small has never been told what our support policy or tone should be.\n" - ], - "metadata": { - "id": "ponHU0RFA8Ms" - } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Qjw3r_bMA631" + }, + "outputs": [], "source": [ "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", "\n", @@ -105,12 +110,7 @@ "outputs = model_base.generate(**inputs, max_new_tokens=120)\n", "print(\"Vanilla T5-small says:\\n\")\n", "print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n" - ], - "metadata": { - "id": "Qjw3r_bMA631" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", @@ -183,17 +183,6 @@ "A 128-token limit therefore fits roughly 75-100 English words (fewer if the text contains many rare names, URLs, or emojis that split into multiple tokens)." ] }, - { - "cell_type": "code", - "source": [ - "ds[0][\"input_text\"]" - ], - "metadata": { - "id": "tRmH0hZpCK0p" - }, - "execution_count": null, - "outputs": [] - }, { "cell_type": "code", "execution_count": null, @@ -237,7 +226,7 @@ "\n", "| Parameter | Role | Here |\n", "|--------------|---------------------------------------------------------------|------|\n", - "| `r` | Rank of the low-rank matrices (higher = more capacity) | 8 |\n", + "| `r` | Rank of the low-rank matrices (higher = more learning capacity) | 8 |\n", "| `lora_alpha` | Scaling factor for the adapter’s update | 16 |\n", "| `target_modules` | Which weight matrices get adapters (we pick **q** & **v**) | [\"q\",\"v\"] |\n", "| `lora_dropout` | Regularisation inside adapters | 0.05 |\n", @@ -512,4 +501,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From a9608cbbc2eb32ed850fc41398610093836b6342 Mon Sep 17 00:00:00 2001 From: rndg Date: Fri, 8 Aug 2025 09:22:09 +0200 Subject: [PATCH 29/70] update readme --- Academy/AI/3-LLM/README.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/Academy/AI/3-LLM/README.md b/Academy/AI/3-LLM/README.md index 4dad1b2..9c5808e 100644 --- a/Academy/AI/3-LLM/README.md +++ b/Academy/AI/3-LLM/README.md @@ -69,6 +69,29 @@ We'll leverage two big providers for this lab: ## Run the notebooks in Colab +### 1 Clone the Repository + +To start, clone the lab repository by running the following command in the terminal: + +```sh + git clone https://github.com/kiratech/labs.git +``` + +### 2 Checkout the Lab Branch + +After cloning the repository, checkout the `academy-ai` branch: + +```sh + git checkout academy-ai +``` + +Then, navigate to the project folder: + +```sh + cd labs/Academy/AI/3-LLM +``` + +This folder contains resources related to this lab. Open Google Colab and import, everything at root level, the content of the folders notebooks and files. From now you can continue on the notebooks. From 042e9fce57078f7f3296b2af7673942058f84298 Mon Sep 17 00:00:00 2001 From: Ale3e Date: Mon, 3 Mar 2025 10:19:20 +0100 Subject: [PATCH 30/70] First lab --- AI/Academy/1-Intro-To-ML-And-MLOps/lab.md | 258 ++++++++++++++++++ .../src/solution.ipynb | 230 ++++++++++++++++ 2 files changed, 488 insertions(+) create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/lab.md create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md new file mode 100644 index 0000000..357fafb --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md @@ -0,0 +1,258 @@ +# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow + +In questo laboratorio, affrontiamo un percorso step-by-step: + +1. **Caricare e preparare un dataset meteo** (con dati di temperatura e umidità) per prevedere se pioverà o meno. +2. **Addestrare un modello di Machine Learning** con Scikit-learn. +3. **Valutare il modello** e comprendere i risultati. +4. **Estendere il progetto** integrando **MLflow** per tracciare metriche, parametri e versioni del modello. + +La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow. + +--- + +## Parte 1: Dai dati al modello ML (Supervised Learning con Scikit-learn) + +### Obiettivo +- Creare un semplice **modello di classificazione** per predire la pioggia basandoci su dati di temperatura e umidità. + +### 1. Preparazione del Dataset + +Per semplicità, qui useremo un dataset di esempio: +[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv) + +```python +import pandas as pd + +# URL del dataset (sostituiscilo con il tuo link) +url = "https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv" + +# Carica il dataset +df = pd.read_csv(url) + +# Mostra le prime righe +print(df.head()) +``` + +--- + +### 2. Esplorazione e pulizia dei Dati +1. Controlliamo se ci sono valori mancanti +2. Trasformiamo la colonna `Label` in una variabile numerica (0 = NoRain, 1 = Rain) +3. Selezione delle feature + + +```python +# 1. Rimozione dei valori mancanti +df = df.dropna() + +# 2. Conversione della colonna 'RainToday' in valori numerici +df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0) + +# 3. Selezione delle feature +features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am'] + +X = df[features] +y = df['RainToday'] +``` +--- +### 3. Suddivisione del Dataset in Training e Test + +Prima di addestrare il modello, dividiamo il dataset in: +- **X** (features) → Temperatura, Umidità +- **y** (target) → Label (Rain/NoRain) + +```python +from sklearn.model_selection import train_test_split + +# Suddivisione dei dati (80% training, 20% testing) +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + +``` + +--- +### 4. Creazione e Addestramento del Modello + +Utilizziamo un classificatore semplice, ad esempio **RandomForestClassifier**. + +```python +from sklearn.ensemble import RandomForestClassifier + +# Creazione e addestramento del modello +model = RandomForestClassifier(n_estimators=100, random_state=42) +model.fit(X_train, y_train) + +``` + +--- + +### 5. Valutazione del Modello + +Calcoliamo l’accuratezza e visualizziamo la matrice di confusione. + +```python +from sklearn.metrics import accuracy_score, classification_report + +# Predizioni sul set di test +y_pred = model.predict(X_test) + +# Calcolo dell'accuratezza +accuracy = accuracy_score(y_test, y_pred) +print(f"Accuratezza del modello: {accuracy:.2f}") + +# Report di classificazione +print(classification_report(y_test, y_pred)) +``` + +--- + +### Conclusione (Parte 1) +In questa prima parte, abbiamo: +1. Caricato un dataset relativo alle condizioni meteo +2. Pulito i dati e trasformato la variabile target +3. Suddiviso il dataset in training e test +4. Creato un modello di classificazione con RandomForest +5. Valutato le prestazioni con accuratezza e matrice di confusione + +--- +## Tech: Installazione e Configurazione di MLflow in Locale con Docker + +### Obiettivo +- Configurare un'istanza MLflow in locale per registrare esperimenti. + +### 1. Avviare MLflow con Docker + +Esegui il seguente comando per avviare un'istanza locale di MLflow: + +```bash +docker pull ghcr.io/mlflow/mlflow + +docker run -p 5001:5000 --name mlflow-server \ + -v $(pwd)/mlruns:/mlruns \ + ghcr.io/mlflow/mlflow:latest server \ + --backend-store-uri sqlite:///mlflow.db \ + --default-artifact-root /mlruns \ + --host 0.0.0.0 +``` + +- **`-p 5001:5000`**: Espone MLflow sulla porta 5001 +- **`-v $(pwd)/mlruns:/mlruns`**: Monta la cartella locale per salvare gli esperimenti +- **`--backend-store-uri sqlite:///mlflow.db`**: Utilizza un database SQLite per il tracking + +Una volta avviato, MLflow sarà disponibile all’indirizzo: + +``` +http://127.0.0.1:5001 +``` + +Ora possiamo passare alla terza parte e integrare MLflow nel nostro codice! + +--- +## Parte 2: Integrazione con MLflow + +Adesso, estendiamo il codice esistente per **tracciare i nostri esperimenti** con **MLflow**. Questo ci permetterà di: +- Loggare i nostri parametri (es. `n_estimators`) +- Salvare l’accuratezza e altre metriche +- Salvare il modello e ricaricarlo facilmente + +### Obiettivo +- Integrare MLflow nel codice esistente per versionare e tracciare le metriche del modello. + +### 1. Installazione e Setup MLflow + +Installa MLflow: + +```bash +pip install mlflow +``` + +```python +import mlflow +import mlflow.sklearn + +# Impostiamo un nome per l'esperimento +mlflow.set_tracking_uri("http://127.0.0.1:5001") +mlflow.set_experiment("weather_classification_experiment") +``` + +--- + +### 2. Logging dei Parametri e Metriche + +Possiamo loggare: +- Parametri → `n_estimators` e altre impostazioni +- Metriche → accuratezza +- Modello → versione del modello addestrato + + +```python +# Usando il modello creato nella Parte 1 +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score + +n_estimators = 100 + +# Creiamo una nuova run MLflow +with mlflow.start_run(): + # Log Param + mlflow.log_param("n_estimators", n_estimators) + + # Creazione del modello + rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42) + rf_model.fit(X_train, y_train) + + # Calcolo metriche + y_pred = rf_model.predict(X_test) + accuracy = accuracy_score(y_test, y_pred) + mlflow.log_metric("accuracy", accuracy) + + # Salviamo il modello + mlflow.sklearn.log_model(rf_model, "random_forest_model") + + print(f"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}") +``` + +--- + +### 3. Visualizzare e Confrontare i Risultati + +MLflow fornisce un’interfaccia web per esplorare tutte le run ed esperimenti. + +```bash +http://127.0.0.1:5001 +``` + +Accedi all’URL indicato nel terminale. Nella sezione **Experiments** potrai vedere: +- Parametri loggati +- Metriche +- Modelli salvati + +### 4. Caricare un Modello Salvato con MLflow + +Possiamo anche recuperare un modello da MLflow per fare previsioni future. + +```python +import mlflow.sklearn +from sklearn.metrics import accuracy_score + +# Inserisci un run_id reale che trovi nell'interfaccia MLflow +RUN_ID = "" + +loaded_model = mlflow.sklearn.load_model(f"runs:/{RUN_ID}/random_forest_model") + +# Verifichiamo l'accuratezza +y_loaded_pred = loaded_model.predict(X_test) +acc_loaded = accuracy_score(y_test, y_loaded_pred) +print(f"Accuratezza del modello caricato: {acc_loaded:.2f}") +``` + +## Conclusioni +In questo laboratorio abbiamo: +1. **Creato un modello di classificazione con Scikit-learn** su un dataset meteo. +2. **Aggiunto MLflow** per tracciare parametri, metriche e gestire le versioni del modello. +3. **Visualizzato i risultati** con l’interfaccia MLflow UI e ricaricato il modello salvato. + + **Prossimi Passi** +- Provare iperparametri diversi (`n_estimators`, `max_depth`, ecc.) e confrontare i risultati su MLflow. +- Integrare un sistema di **continuous integration/continuous deployment (CI/CD)** per distribuire automaticamente il modello. +- Monitorare in produzione il drift del modello (eventuali cali di accuratezza). diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb new file mode 100644 index 0000000..b1584fc --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuratezza del modello: 0.82\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.93 0.89 2649\n", + " 1 0.61 0.42 0.50 721\n", + "\n", + " accuracy 0.82 3370\n", + " macro avg 0.73 0.67 0.69 3370\n", + "weighted avg 0.80 0.82 0.81 3370\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# URL del dataset (sostituiscilo con il tuo link)\n", + "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", + "\n", + "# Carica il dataset\n", + "df = pd.read_csv(url)\n", + "\n", + "\n", + "# 1. Rimozione dei valori mancanti\n", + "df = df.dropna()\n", + "\n", + "# 2. Conversione della colonna 'Rain' in valori numerici\n", + "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", + "\n", + "# 3. Selezione delle caratteristiche e della variabile target\n", + "features = ['MinTemp', 'MaxTemp', 'Humidity9am', 'Humidity3pm']\n", + "X = df[features]\n", + "y = df['RainToday']\n", + "\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Suddivisione dei dati (80% training, 20% testing)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Creazione e addestramento del modello\n", + "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "model.fit(X_train, y_train)\n", + "\n", + "from sklearn.metrics import accuracy_score, classification_report\n", + "\n", + "# Predizioni sul set di test\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Calcolo dell'accuratezza\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", + "\n", + "# Report di classificazione\n", + "print(classification_report(y_test, y_pred))\n", + "\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "# Calcolare la matrice di confusione\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Creiamo una heatmap con Seaborn\n", + "plt.figure(figsize=(6, 4))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Rain\", \"Rain\"], yticklabels=[\"No Rain\", \"Rain\"])\n", + "\n", + "# Aggiungiamo i titoli\n", + "plt.xlabel(\"Predicted Label\")\n", + "plt.ylabel(\"True Label\")\n", + "plt.title(\"Confusion Matrix\")\n", + "\n", + "# Mostriamo il grafico\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025/02/25 15:17:44 INFO mlflow.tracking.fluent: Experiment with name 'weather_classification_experiment' does not exist. Creating a new experiment.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import mlflow\n", + "import mlflow.sklearn\n", + "\n", + "# Impostiamo un nome per l'esperimento\n", + "mlflow.set_tracking_uri(\"http://127.0.0.1:5001\")\n", + "mlflow.set_experiment(\"weather_classification_experiment\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025/02/25 15:17:52 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n", + "2025/02/25 15:17:52 INFO mlflow.tracking._tracking_service.client: 🏃 View run shivering-yak-384 at: http://127.0.0.1:5001/#/experiments/1/runs/e1a69ed25ddc49609674c7df7375d642.\n", + "2025/02/25 15:17:52 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5001/#/experiments/1.\n" + ] + }, + { + "ename": "OSError", + "evalue": "[Errno 30] Read-only file system: '/mlruns'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 24\u001b[0m\n\u001b[1;32m 21\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m, accuracy)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Salviamo il modello\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msklearn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrf_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrandom_forest_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEsperimento concluso. Accuratezza registrata: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maccuracy\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/sklearn/__init__.py:413\u001b[0m, in \u001b[0;36mlog_model\u001b[0;34m(sk_model, artifact_path, conda_env, code_paths, serialization_format, registered_model_name, signature, input_example, await_registration_for, pip_requirements, extra_pip_requirements, pyfunc_predict_fn, metadata)\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;129m@format_docstring\u001b[39m(LOG_MODEL_PARAM_DOCS\u001b[38;5;241m.\u001b[39mformat(package_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscikit-learn\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_model\u001b[39m(\n\u001b[1;32m 336\u001b[0m sk_model,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 348\u001b[0m metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 349\u001b[0m ):\n\u001b[1;32m 350\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 351\u001b[0m \u001b[38;5;124;03m Log a scikit-learn model as an MLflow artifact for the current run. Produces an MLflow Model\u001b[39;00m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;124;03m containing the following flavors:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 411\u001b[0m \n\u001b[1;32m 412\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43martifact_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[43mflavor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msklearn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43msk_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msk_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mconda_env\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconda_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mcode_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcode_paths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43mserialization_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserialization_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m \u001b[49m\u001b[43mregistered_model_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mregistered_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 421\u001b[0m \u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 422\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_example\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_example\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[43m \u001b[49m\u001b[43mawait_registration_for\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mawait_registration_for\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 424\u001b[0m \u001b[43m \u001b[49m\u001b[43mpip_requirements\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpip_requirements\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 425\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_pip_requirements\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_pip_requirements\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 426\u001b[0m \u001b[43m \u001b[49m\u001b[43mpyfunc_predict_fn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpyfunc_predict_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 427\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 428\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/models/model.py:744\u001b[0m, in \u001b[0;36mModel.log\u001b[0;34m(cls, artifact_path, flavor, registered_model_name, await_registration_for, metadata, run_id, resources, **kwargs)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m tracking_uri \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabricks\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m get_uri_scheme(tracking_uri) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabricks\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 743\u001b[0m _logger\u001b[38;5;241m.\u001b[39mwarning(_LOG_MODEL_MISSING_SIGNATURE_WARNING)\n\u001b[0;32m--> 744\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtracking\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfluent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlflow_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43martifact_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 746\u001b[0m \u001b[38;5;66;03m# if the model_config kwarg is passed in, then log the model config as an params\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_config \u001b[38;5;241m:=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_config\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/fluent.py:1170\u001b[0m, in \u001b[0;36mlog_artifacts\u001b[0;34m(local_dir, artifact_path, run_id)\u001b[0m\n\u001b[1;32m 1136\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;124;03mLog all the contents of a local directory as artifacts of the run. If no run is active,\u001b[39;00m\n\u001b[1;32m 1138\u001b[0m \u001b[38;5;124;03mthis method will create a new active run.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1167\u001b[0m \u001b[38;5;124;03m mlflow.log_artifacts(tmp_dir, artifact_path=\"states\")\u001b[39;00m\n\u001b[1;32m 1168\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1169\u001b[0m run_id \u001b[38;5;241m=\u001b[39m run_id \u001b[38;5;129;01mor\u001b[39;00m _get_or_start_run()\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mrun_id\n\u001b[0;32m-> 1170\u001b[0m \u001b[43mMlflowClient\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/client.py:1977\u001b[0m, in \u001b[0;36mMlflowClient.log_artifacts\u001b[0;34m(self, run_id, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 1930\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_artifacts\u001b[39m(\n\u001b[1;32m 1931\u001b[0m \u001b[38;5;28mself\u001b[39m, run_id: \u001b[38;5;28mstr\u001b[39m, local_dir: \u001b[38;5;28mstr\u001b[39m, artifact_path: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1932\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Write a directory of files to the remote ``artifact_uri``.\u001b[39;00m\n\u001b[1;32m 1934\u001b[0m \n\u001b[1;32m 1935\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1975\u001b[0m \n\u001b[1;32m 1976\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1977\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tracking_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/_tracking_service/client.py:874\u001b[0m, in \u001b[0;36mTrackingServiceClient.log_artifacts\u001b[0;34m(self, run_id, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_artifacts\u001b[39m(\u001b[38;5;28mself\u001b[39m, run_id, local_dir, artifact_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Write a directory of files to the remote ``artifact_uri``.\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \n\u001b[1;32m 868\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 872\u001b[0m \n\u001b[1;32m 873\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 874\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_artifact_repo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/store/artifact/local_artifact_repo.py:66\u001b[0m, in \u001b[0;36mLocalArtifactRepository.log_artifacts\u001b[0;34m(self, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 62\u001b[0m artifact_dir \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 63\u001b[0m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39martifact_dir, artifact_path) \u001b[38;5;28;01mif\u001b[39;00m artifact_path \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39martifact_dir\n\u001b[1;32m 64\u001b[0m )\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(artifact_dir):\n\u001b[0;32m---> 66\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43martifact_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m shutil\u001b[38;5;241m.\u001b[39mcopytree(src\u001b[38;5;241m=\u001b[39mlocal_dir, dst\u001b[38;5;241m=\u001b[39martifact_dir, dirs_exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/utils/file_utils.py:211\u001b[0m, in \u001b[0;36mmkdir\u001b[0;34m(root, name)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m!=\u001b[39m errno\u001b[38;5;241m.\u001b[39mEEXIST \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(target):\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m target\n", + "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/utils/file_utils.py:208\u001b[0m, in \u001b[0;36mmkdir\u001b[0;34m(root, name)\u001b[0m\n\u001b[1;32m 206\u001b[0m target \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(root, name) \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m root\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m!=\u001b[39m errno\u001b[38;5;241m.\u001b[39mEEXIST \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(target):\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + " \u001b[0;31m[... skipping similar frames: makedirs at line 213 (1 times)]\u001b[0m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", + "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:223\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\n", + "\u001b[0;31mOSError\u001b[0m: [Errno 30] Read-only file system: '/mlruns'" + ] + } + ], + "source": [ + "# Usando il modello creato nella Parte 1\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "n_estimators = 100\n", + "\n", + "# Creiamo una nuova run MLflow\n", + "with mlflow.start_run():\n", + " # Log Param\n", + " mlflow.log_param(\"n_estimators\", n_estimators)\n", + "\n", + " # Creazione del modello\n", + " rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n", + " rf_model.fit(X_train, y_train)\n", + "\n", + " # Calcolo metriche\n", + " y_pred = rf_model.predict(X_test)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + " \n", + " # Salviamo il modello\n", + " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", + "\n", + " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "mlflow_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 2e733245387e02c90a29e07921879547ed4298ff Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Mar 2025 14:59:27 +0100 Subject: [PATCH 31/70] add lab python and refactor solution --- AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore | 178 ++++++ AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 530 ++++++++++++++++++ AI/Academy/1-Intro-To-ML-And-MLOps/lab.md | 258 --------- .../src/requirements.txt | 153 +++++ .../src/solution.ipynb | 152 ++--- 5 files changed, 899 insertions(+), 372 deletions(-) create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb delete mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/lab.md create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore b/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore new file mode 100644 index 0000000..5dd41ee --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore @@ -0,0 +1,178 @@ +# local mlflow +mlartifacts/* +mlruns/* + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc \ No newline at end of file diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb new file mode 100644 index 0000000..65cae9f --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -0,0 +1,530 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow\n", + "\n", + "In questo laboratorio, affrontiamo un percorso step-by-step:\n", + "\n", + "1. **Caricare e preparare un dataset meteo** (con dati di temperatura e umidità) per prevedere se pioverà o meno.\n", + "2. **Addestrare un modello di Machine Learning** con Scikit-learn.\n", + "3. **Valutare il modello** e comprendere i risultati.\n", + "4. **Estendere il progetto** integrando **MLflow** per tracciare metriche, parametri e versioni del modello.\n", + "\n", + "La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parte 0: Setup dell'Environment per Python\n", + "\n", + "#### **Requisiti**\n", + "\n", + "Questo laboratorio assume che Python sia già installato, la repository [kiratech/labs](https://github.com/kiratech/labs.git) sia accessibile e che Git sia configurato correttamente sul computer locale.\n", + "\n", + "### 1. Clonare la Repository\n", + "Per iniziare, clona la repository del laboratorio eseguendo il seguente comando:\n", + "\n", + "```sh\n", + "git clone https://github.com/kiratech/labs.git\n", + "```\n", + "\n", + "### 2. Fare il Checkout del Branch Lab\n", + "\n", + "Dopo aver clonato la repository, entra nella cartella del progetto:\n", + "\n", + "```sh\n", + "cd labs\n", + "```\n", + "\n", + "Quindi, fai il checkout del branch `lab`:\n", + "\n", + "```sh\n", + "git checkout academy-ai\n", + "```\n", + "In questa cartella sono presenti le risorse relative ai laboratori con tema AI.\n", + "\n", + "### 3. Spostarsi nella Cartella 1-Intro-To-ML-And-MLOps\n", + "\n", + "Naviga nella cartella del primo laboratorio:\n", + "\n", + "```sh\n", + "cd Academy/1-Intro-To-ML-And-MLOps\n", + "```\n", + "\n", + "### 4. Creare un Virtual Environment\n", + "\n", + "Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema.\n", + "\n", + "Crea un ambiente virtuale:\n", + "\n", + "```sh\n", + "python -m venv venv\n", + "```\n", + "\n", + "Attiva l'Ambiente Virtuale\n", + "\n", + "- **Su macOS e Linux:**\n", + " \n", + " ```sh\n", + " source venv/bin/activate\n", + " ```\n", + " \n", + "- **Su Windows:**\n", + " \n", + " ```sh\n", + " venv\\Scripts\\activate\n", + " ```\n", + "\n", + "Dovresti vedere il prefisso `(venv)` nel terminale, indicando che l'ambiente virtuale è attivo.\n", + "\n", + "### 5. Installare i pacchetti da `requirements.txt`\n", + "\n", + "Tramite il file `requirements.txt`, possiamo installare tutte le dipendenze di questo laboratorio, in particolare **scikit-learn** e **MLflow**, eseguendo il comando:\n", + "\n", + "```sh\n", + "pip install -r src/requirements.txt\n", + "```\n", + "\n", + "Verifica che i pacchetti siano stati installati correttamente con:\n", + "\n", + "```sh\n", + "pip list\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parte 1: Dai dati al modello ML (Supervised Learning con Scikit-learn)\n", + "\n", + "**Obiettivo**\n", + "- Creare un semplice **modello di classificazione** per predire la pioggia basandoci su dati di temperatura e umidità.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Preparazione del Dataset\n", + "\n", + "Per semplicità, qui useremo un dataset di esempio:\n", + "[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# URL del dataset (sostituiscilo con il tuo link)\n", + "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", + "\n", + "# Carica il dataset\n", + "df = pd.read_csv(url)\n", + "\n", + "# Mostra le prime righe\n", + "df.head(5)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Esplorazione e pulizia dei Dati\n", + "1. Controlliamo se ci sono valori mancanti\n", + "2. Trasformiamo la colonna `Label` in una variabile binaria (0 = NoRain, 1 = Rain)\n", + "3. Selezione delle feature " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Rimozione dei valori mancanti\n", + "df = df.dropna()\n", + "\n", + "# 2. Conversione della colonna 'RainToday' in valori numerici\n", + "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", + "\n", + "# 3. Selezione delle feature \n", + "features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am']\n", + "\n", + "X = df[features]\n", + "y = df['RainToday']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Suddivisione del Dataset in Training e Test\n", + "\n", + "Prima di addestrare il modello, dividiamo il dataset in:\n", + "- **X** (features) → Temperatura, Umidità\n", + "- **y** (target) → Label (Rain/NoRain)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Suddivisione dei dati (80% training, 20% testing)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "print(f\"Dimensioni del dataset di training: {len(X_train)}\")\n", + "print(f\"Dimensioni del dataset di test: {len(X_test)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Creazione e Addestramento del Modello\n", + "\n", + "Utilizziamo un classificatore semplice, ad esempio [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "# Creazione e addestramento del modello\n", + "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", + "model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5. Valutazione del Modello\n", + "\n", + "Calcoliamo l’accuratezza del modello e altre metriche. Inoltre, visualizziamo la matrice di confusione ovvero un metodo visivo per valutare le prestazioni di un modello." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import accuracy_score, classification_report\n", + "\n", + "# Predizioni sul set di test\n", + "y_pred = model.predict(X_test)\n", + "\n", + "# Calcolo dell'accuratezza\n", + "accuracy = accuracy_score(y_test, y_pred)\n", + "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", + "\n", + "# Report di classificazione\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "# Creiamo una heatmap con Seaborn\n", + "target_names = [\"No Rain\", \"Rain\"]\n", + "cm = confusion_matrix(y_test, y_pred)\n", + "plt.figure(figsize=(6, 4))\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", + "\n", + "# Aggiungiamo i titoli\n", + "plt.xlabel(\"Predicted Label\")\n", + "plt.ylabel(\"True Label\")\n", + "plt.title(\"Confusion Matrix\")\n", + "\n", + "# Mostriamo il grafico\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusione (Parte 1)\n", + "In questa prima parte, abbiamo:\n", + "1. Caricato un dataset relativo alle condizioni meteo\n", + "2. Pulito i dati e trasformato la variabile target\n", + "3. Suddiviso il dataset in training e test\n", + "4. Creato un modello di classificazione con RandomForest\n", + "5. Valutato le prestazioni con accuratezza e matrice di confusione" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tech: Installazione e Configurazione di MLflow\n", + "### Obiettivo\n", + "- Configurare un'istanza MLflow in locale per registrare esperimenti.\n", + "### 1. Avviare MLflow con Docker\n", + "\n", + "Esegui il seguente comando per avviare un'istanza locale di MLflow:\n", + "```bash\n", + "mlflow ui\n", + "```\n", + "In questo modo MLflow espone l'interfaccia grafica di MLflow all'indirizzo:\n", + "```\n", + "http://127.0.0.1:5000\n", + "```\n", + "Questa configurazione salva artefatti ed esperimenti in locale. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MLflow in Produzione\n", + "Nel contesto produttivo e presso i clienti, non si esegue MLflow in locale. \n", + "Generalmente si scelgono soluzioni che permettono un'architettura più robusta e scalabile, evitando problemi di gestione manuale degli esperimenti e della persistenza dei dati.\n", + "Le soluzioni comuni includono:\n", + "- **Docker Compose**: Un file docker-compose.yml viene utilizzato per avviare MLflow con un database backend e uno storage remoto. \n", + "Una soluzione basata su questo metodo può essere trovata nella repository interna [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main).\n", + "- **Kubernetes**: MLflow viene distribuito su un cluster Kubernetes, garantendo scalabilità e gestione centralizzata. \n", + "\n", + "Entrambe le opzioni si basano su una soluzione multi-container dove sono presenti un database persistente e un storage S3, in genere MinIO o soluzioni cloud. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Parte 2: Integrazione con MLflow\n", + "\n", + "Adesso, estendiamo il codice esistente per **tracciare i nostri esperimenti** con **MLflow**. Questo ci permetterà di:\n", + "- Loggare i nostri parametri (es. `n_estimators`)\n", + "- Salvare l’accuratezza e altre metriche\n", + "- Salvare il modello e ricaricarlo facilmente\n", + "\n", + "### Obiettivo\n", + "- Integrare MLflow nel codice esistente per versionare e tracciare le metriche del modello.\n", + "\n", + "### 1. Setup MLflow\n", + "Impostiamo le variabili di MLflow per questo esperimento:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow\n", + "import mlflow.sklearn\n", + "\n", + "# Impostiamo un nome per l'esperimento\n", + "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n", + "mlflow.set_experiment(\"weather_classification_experiment\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Verifichiamo che dalla nostra interfaccia grafica il nuovo esperimento sia visibile." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Logging dei Parametri e Metriche\n", + "\n", + "Possiamo loggare:\n", + "- Parametri → `n_estimators` e altre impostazioni\n", + "- Metriche → accuratezza\n", + "- Modello → versione del modello addestrato\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Usando il modello creato nella Parte 1\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "n_estimators = 100\n", + "\n", + "# Creiamo una nuova run MLflow\n", + "with mlflow.start_run():\n", + " # Log Param\n", + " mlflow.log_param(\"n_estimators\", n_estimators)\n", + "\n", + " # Creazione del modello\n", + " rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n", + " rf_model.fit(X_train, y_train)\n", + "\n", + " # Calcolo metriche\n", + " y_pred = rf_model.predict(X_test)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + "\n", + " # Salviamo il modello\n", + " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", + "\n", + " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Visualizzare e Confrontare i Risultati\n", + "\n", + "MLflow fornisce un’interfaccia web per esplorare tutte le run ed esperimenti.\n", + "\n", + "```bash\n", + "http://127.0.0.1:5000\n", + "```\n", + "\n", + "Accedi all’URL indicato nel terminale. Nella sezione **Experiments** potrai vedere:\n", + "- Parametri loggati\n", + "- Metriche\n", + "- Modelli salvati" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4. Caricare un Modello Salvato con MLflow\n", + "\n", + "Copiando l'id della run dell'esperimento eseguito, possiamo recuperare un modello da MLflow per fare previsioni future." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import mlflow.sklearn\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Inserisci un run_id reale che trovi nell'interfaccia MLflow\n", + "RUN_ID = \"\"\n", + "\n", + "loaded_model = mlflow.sklearn.load_model(f\"runs:/{RUN_ID}/random_forest_model\")\n", + "\n", + "# Verifichiamo l'accuratezza\n", + "y_loaded_pred = loaded_model.predict(X_test)\n", + "acc_loaded = accuracy_score(y_test, y_loaded_pred)\n", + "print(f\"Accuratezza del modello caricato: {acc_loaded:.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "È possibile notare che con lo stesso dataset di test, il modello ha precisamente lo stessa accuratezza." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusioni\n", + "In questo laboratorio abbiamo:\n", + "1. **Creato un modello di classificazione con Scikit-learn** su un dataset meteo.\n", + "2. **Aggiunto MLflow** per tracciare parametri, metriche e gestire le versioni del modello.\n", + "3. **Visualizzato i risultati** con l’interfaccia MLflow UI e ricaricato il modello salvato.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prossimi Passi\n", + "- Provare iperparametri diversi (`n_estimators`, `max_depth`, ecc.) e confrontare i risultati su MLflow.\n", + "- Integrare un sistema di **continuous integration/continuous deployment (CI/CD)** per distribuire automaticamente il modello.\n", + "- Monitorare in produzione il drift del modello (eventuali cali di accuratezza)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.10 ('venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a8e57e261110579b348b220fe1c71aea6cd1a8cd238281550a2a6c068185ef06" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md deleted file mode 100644 index 357fafb..0000000 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.md +++ /dev/null @@ -1,258 +0,0 @@ -# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow - -In questo laboratorio, affrontiamo un percorso step-by-step: - -1. **Caricare e preparare un dataset meteo** (con dati di temperatura e umidità) per prevedere se pioverà o meno. -2. **Addestrare un modello di Machine Learning** con Scikit-learn. -3. **Valutare il modello** e comprendere i risultati. -4. **Estendere il progetto** integrando **MLflow** per tracciare metriche, parametri e versioni del modello. - -La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow. - ---- - -## Parte 1: Dai dati al modello ML (Supervised Learning con Scikit-learn) - -### Obiettivo -- Creare un semplice **modello di classificazione** per predire la pioggia basandoci su dati di temperatura e umidità. - -### 1. Preparazione del Dataset - -Per semplicità, qui useremo un dataset di esempio: -[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv) - -```python -import pandas as pd - -# URL del dataset (sostituiscilo con il tuo link) -url = "https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv" - -# Carica il dataset -df = pd.read_csv(url) - -# Mostra le prime righe -print(df.head()) -``` - ---- - -### 2. Esplorazione e pulizia dei Dati -1. Controlliamo se ci sono valori mancanti -2. Trasformiamo la colonna `Label` in una variabile numerica (0 = NoRain, 1 = Rain) -3. Selezione delle feature - - -```python -# 1. Rimozione dei valori mancanti -df = df.dropna() - -# 2. Conversione della colonna 'RainToday' in valori numerici -df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0) - -# 3. Selezione delle feature -features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am'] - -X = df[features] -y = df['RainToday'] -``` ---- -### 3. Suddivisione del Dataset in Training e Test - -Prima di addestrare il modello, dividiamo il dataset in: -- **X** (features) → Temperatura, Umidità -- **y** (target) → Label (Rain/NoRain) - -```python -from sklearn.model_selection import train_test_split - -# Suddivisione dei dati (80% training, 20% testing) -X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - -``` - ---- -### 4. Creazione e Addestramento del Modello - -Utilizziamo un classificatore semplice, ad esempio **RandomForestClassifier**. - -```python -from sklearn.ensemble import RandomForestClassifier - -# Creazione e addestramento del modello -model = RandomForestClassifier(n_estimators=100, random_state=42) -model.fit(X_train, y_train) - -``` - ---- - -### 5. Valutazione del Modello - -Calcoliamo l’accuratezza e visualizziamo la matrice di confusione. - -```python -from sklearn.metrics import accuracy_score, classification_report - -# Predizioni sul set di test -y_pred = model.predict(X_test) - -# Calcolo dell'accuratezza -accuracy = accuracy_score(y_test, y_pred) -print(f"Accuratezza del modello: {accuracy:.2f}") - -# Report di classificazione -print(classification_report(y_test, y_pred)) -``` - ---- - -### Conclusione (Parte 1) -In questa prima parte, abbiamo: -1. Caricato un dataset relativo alle condizioni meteo -2. Pulito i dati e trasformato la variabile target -3. Suddiviso il dataset in training e test -4. Creato un modello di classificazione con RandomForest -5. Valutato le prestazioni con accuratezza e matrice di confusione - ---- -## Tech: Installazione e Configurazione di MLflow in Locale con Docker - -### Obiettivo -- Configurare un'istanza MLflow in locale per registrare esperimenti. - -### 1. Avviare MLflow con Docker - -Esegui il seguente comando per avviare un'istanza locale di MLflow: - -```bash -docker pull ghcr.io/mlflow/mlflow - -docker run -p 5001:5000 --name mlflow-server \ - -v $(pwd)/mlruns:/mlruns \ - ghcr.io/mlflow/mlflow:latest server \ - --backend-store-uri sqlite:///mlflow.db \ - --default-artifact-root /mlruns \ - --host 0.0.0.0 -``` - -- **`-p 5001:5000`**: Espone MLflow sulla porta 5001 -- **`-v $(pwd)/mlruns:/mlruns`**: Monta la cartella locale per salvare gli esperimenti -- **`--backend-store-uri sqlite:///mlflow.db`**: Utilizza un database SQLite per il tracking - -Una volta avviato, MLflow sarà disponibile all’indirizzo: - -``` -http://127.0.0.1:5001 -``` - -Ora possiamo passare alla terza parte e integrare MLflow nel nostro codice! - ---- -## Parte 2: Integrazione con MLflow - -Adesso, estendiamo il codice esistente per **tracciare i nostri esperimenti** con **MLflow**. Questo ci permetterà di: -- Loggare i nostri parametri (es. `n_estimators`) -- Salvare l’accuratezza e altre metriche -- Salvare il modello e ricaricarlo facilmente - -### Obiettivo -- Integrare MLflow nel codice esistente per versionare e tracciare le metriche del modello. - -### 1. Installazione e Setup MLflow - -Installa MLflow: - -```bash -pip install mlflow -``` - -```python -import mlflow -import mlflow.sklearn - -# Impostiamo un nome per l'esperimento -mlflow.set_tracking_uri("http://127.0.0.1:5001") -mlflow.set_experiment("weather_classification_experiment") -``` - ---- - -### 2. Logging dei Parametri e Metriche - -Possiamo loggare: -- Parametri → `n_estimators` e altre impostazioni -- Metriche → accuratezza -- Modello → versione del modello addestrato - - -```python -# Usando il modello creato nella Parte 1 -from sklearn.ensemble import RandomForestClassifier -from sklearn.metrics import accuracy_score - -n_estimators = 100 - -# Creiamo una nuova run MLflow -with mlflow.start_run(): - # Log Param - mlflow.log_param("n_estimators", n_estimators) - - # Creazione del modello - rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42) - rf_model.fit(X_train, y_train) - - # Calcolo metriche - y_pred = rf_model.predict(X_test) - accuracy = accuracy_score(y_test, y_pred) - mlflow.log_metric("accuracy", accuracy) - - # Salviamo il modello - mlflow.sklearn.log_model(rf_model, "random_forest_model") - - print(f"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}") -``` - ---- - -### 3. Visualizzare e Confrontare i Risultati - -MLflow fornisce un’interfaccia web per esplorare tutte le run ed esperimenti. - -```bash -http://127.0.0.1:5001 -``` - -Accedi all’URL indicato nel terminale. Nella sezione **Experiments** potrai vedere: -- Parametri loggati -- Metriche -- Modelli salvati - -### 4. Caricare un Modello Salvato con MLflow - -Possiamo anche recuperare un modello da MLflow per fare previsioni future. - -```python -import mlflow.sklearn -from sklearn.metrics import accuracy_score - -# Inserisci un run_id reale che trovi nell'interfaccia MLflow -RUN_ID = "" - -loaded_model = mlflow.sklearn.load_model(f"runs:/{RUN_ID}/random_forest_model") - -# Verifichiamo l'accuratezza -y_loaded_pred = loaded_model.predict(X_test) -acc_loaded = accuracy_score(y_test, y_loaded_pred) -print(f"Accuratezza del modello caricato: {acc_loaded:.2f}") -``` - -## Conclusioni -In questo laboratorio abbiamo: -1. **Creato un modello di classificazione con Scikit-learn** su un dataset meteo. -2. **Aggiunto MLflow** per tracciare parametri, metriche e gestire le versioni del modello. -3. **Visualizzato i risultati** con l’interfaccia MLflow UI e ricaricato il modello salvato. - - **Prossimi Passi** -- Provare iperparametri diversi (`n_estimators`, `max_depth`, ecc.) e confrontare i risultati su MLflow. -- Integrare un sistema di **continuous integration/continuous deployment (CI/CD)** per distribuire automaticamente il modello. -- Monitorare in produzione il drift del modello (eventuali cali di accuratezza). diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt b/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt new file mode 100644 index 0000000..2580f85 --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt @@ -0,0 +1,153 @@ +alembic==1.14.1 +annotated-types==0.7.0 +anyio==4.8.0 +appnope==0.1.4 +argon2-cffi==23.1.0 +argon2-cffi-bindings==21.2.0 +arrow==1.3.0 +asttokens==3.0.0 +async-lru==2.0.4 +attrs==25.1.0 +babel==2.17.0 +beautifulsoup4==4.13.3 +bleach==6.2.0 +blinker==1.9.0 +cachetools==5.5.2 +certifi==2025.1.31 +cffi==1.17.1 +charset-normalizer==3.4.1 +click==8.1.8 +cloudpickle==3.1.1 +comm==0.2.2 +contourpy==1.3.1 +cycler==0.12.1 +databricks-sdk==0.44.1 +debugpy==1.8.12 +decorator==5.2.1 +defusedxml==0.7.1 +Deprecated==1.2.18 +docker==7.1.0 +exceptiongroup==1.2.2 +executing==2.2.0 +fastjsonschema==2.21.1 +Flask==3.1.0 +fonttools==4.56.0 +fqdn==1.5.1 +gitdb==4.0.12 +GitPython==3.1.44 +google-auth==2.38.0 +graphene==3.4.3 +graphql-core==3.2.6 +graphql-relay==3.2.0 +gunicorn==23.0.0 +h11==0.14.0 +httpcore==1.0.7 +httpx==0.28.1 +idna==3.10 +importlib_metadata==8.5.0 +ipykernel==6.29.5 +ipython==8.33.0 +ipywidgets==8.1.5 +isoduration==20.11.0 +itsdangerous==2.2.0 +jedi==0.19.2 +Jinja2==3.1.5 +joblib==1.4.2 +json5==0.10.0 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2024.10.1 +jupyter==1.1.1 +jupyter-console==6.6.3 +jupyter-events==0.12.0 +jupyter-lsp==2.2.5 +jupyter_client==8.6.3 +jupyter_core==5.7.2 +jupyter_server==2.15.0 +jupyter_server_terminals==0.5.3 +jupyterlab==4.3.5 +jupyterlab_pygments==0.3.0 +jupyterlab_server==2.27.3 +jupyterlab_widgets==3.0.13 +kiwisolver==1.4.8 +Mako==1.3.9 +Markdown==3.7 +MarkupSafe==3.0.2 +matplotlib==3.10.1 +matplotlib-inline==0.1.7 +mistune==3.1.2 +mlflow==2.20.3 +mlflow-skinny==2.20.3 +nbclient==0.10.2 +nbconvert==7.16.6 +nbformat==5.10.4 +nest-asyncio==1.6.0 +notebook==7.3.2 +notebook_shim==0.2.4 +numpy==2.2.3 +opentelemetry-api==1.30.0 +opentelemetry-sdk==1.30.0 +opentelemetry-semantic-conventions==0.51b0 +overrides==7.7.0 +packaging==24.2 +pandas==2.2.3 +pandocfilters==1.5.1 +parso==0.8.4 +pexpect==4.9.0 +pillow==11.1.0 +platformdirs==4.3.6 +prometheus_client==0.21.1 +prompt_toolkit==3.0.50 +protobuf==5.29.3 +psutil==7.0.0 +ptyprocess==0.7.0 +pure_eval==0.2.3 +pyarrow==19.0.1 +pyasn1==0.6.1 +pyasn1_modules==0.4.1 +pycparser==2.22 +pydantic==2.10.6 +pydantic_core==2.27.2 +Pygments==2.19.1 +pyparsing==3.2.1 +python-dateutil==2.9.0.post0 +python-json-logger==3.2.1 +pytz==2025.1 +PyYAML==6.0.2 +pyzmq==26.2.1 +referencing==0.36.2 +requests==2.32.3 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rpds-py==0.23.1 +rsa==4.9 +scikit-learn==1.6.1 +scipy==1.15.2 +seaborn==0.13.2 +Send2Trash==1.8.3 +six==1.17.0 +smmap==5.0.2 +sniffio==1.3.1 +soupsieve==2.6 +SQLAlchemy==2.0.38 +sqlparse==0.5.3 +stack-data==0.6.3 +terminado==0.18.1 +threadpoolctl==3.5.0 +tinycss2==1.4.0 +tomli==2.2.1 +tornado==6.4.2 +traitlets==5.14.3 +types-python-dateutil==2.9.0.20241206 +typing_extensions==4.12.2 +tzdata==2025.1 +uri-template==1.3.0 +urllib3==2.3.0 +wcwidth==0.2.13 +webcolors==24.11.1 +webencodings==0.5.1 +websocket-client==1.8.0 +Werkzeug==3.1.3 +widgetsnbextension==4.0.13 +wrapt==1.17.2 +zipp==3.21.0 diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb index b1584fc..848696a 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb @@ -2,173 +2,81 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuratezza del modello: 0.82\n", - " precision recall f1-score support\n", - "\n", - " 0 0.85 0.93 0.89 2649\n", - " 1 0.61 0.42 0.50 721\n", - "\n", - " accuracy 0.82 3370\n", - " macro avg 0.73 0.67 0.69 3370\n", - "weighted avg 0.80 0.82 0.81 3370\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", "# URL del dataset (sostituiscilo con il tuo link)\n", "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", - "\n", "# Carica il dataset\n", "df = pd.read_csv(url)\n", "\n", - "\n", "# 1. Rimozione dei valori mancanti\n", "df = df.dropna()\n", - "\n", - "# 2. Conversione della colonna 'Rain' in valori numerici\n", + "# 2. Conversione della colonna 'RainToday' in valori numerici\n", "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", - "\n", - "# 3. Selezione delle caratteristiche e della variabile target\n", - "features = ['MinTemp', 'MaxTemp', 'Humidity9am', 'Humidity3pm']\n", + "# 3. Selezione delle feature \n", + "features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am']\n", "X = df[features]\n", "y = df['RainToday']\n", "\n", - "\n", "from sklearn.model_selection import train_test_split\n", - "\n", "# Suddivisione dei dati (80% training, 20% testing)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "print(f\"Dimensioni del dataset di training: {len(X_train)}\")\n", + "print(f\"Dimensioni del dataset di test: {len(X_test)}\")\n", "\n", "from sklearn.ensemble import RandomForestClassifier\n", - "\n", "# Creazione e addestramento del modello\n", "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", "model.fit(X_train, y_train)\n", "\n", "from sklearn.metrics import accuracy_score, classification_report\n", - "\n", "# Predizioni sul set di test\n", "y_pred = model.predict(X_test)\n", - "\n", "# Calcolo dell'accuratezza\n", "accuracy = accuracy_score(y_test, y_pred)\n", "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", - "\n", "# Report di classificazione\n", "print(classification_report(y_test, y_pred))\n", "\n", - "from sklearn.metrics import confusion_matrix\n", - "\n", - "# Calcolare la matrice di confusione\n", - "cm = confusion_matrix(y_test, y_pred)\n", - "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", - "\n", + "from sklearn.metrics import confusion_matrix\n", "# Creiamo una heatmap con Seaborn\n", + "target_names = [\"No Rain\", \"Rain\"]\n", + "cm = confusion_matrix(y_test, y_pred)\n", "plt.figure(figsize=(6, 4))\n", - "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"No Rain\", \"Rain\"], yticklabels=[\"No Rain\", \"Rain\"])\n", - "\n", + "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", "# Aggiungiamo i titoli\n", "plt.xlabel(\"Predicted Label\")\n", "plt.ylabel(\"True Label\")\n", "plt.title(\"Confusion Matrix\")\n", - "\n", "# Mostriamo il grafico\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025/02/25 15:17:44 INFO mlflow.tracking.fluent: Experiment with name 'weather_classification_experiment' does not exist. Creating a new experiment.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import mlflow\n", "import mlflow.sklearn\n", "\n", "# Impostiamo un nome per l'esperimento\n", - "mlflow.set_tracking_uri(\"http://127.0.0.1:5001\")\n", + "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n", "mlflow.set_experiment(\"weather_classification_experiment\")" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025/02/25 15:17:52 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\n", - "2025/02/25 15:17:52 INFO mlflow.tracking._tracking_service.client: 🏃 View run shivering-yak-384 at: http://127.0.0.1:5001/#/experiments/1/runs/e1a69ed25ddc49609674c7df7375d642.\n", - "2025/02/25 15:17:52 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://127.0.0.1:5001/#/experiments/1.\n" - ] - }, - { - "ename": "OSError", - "evalue": "[Errno 30] Read-only file system: '/mlruns'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 24\u001b[0m\n\u001b[1;32m 21\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m\"\u001b[39m, accuracy)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Salviamo il modello\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msklearn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrf_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrandom_forest_model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEsperimento concluso. Accuratezza registrata: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00maccuracy\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/sklearn/__init__.py:413\u001b[0m, in \u001b[0;36mlog_model\u001b[0;34m(sk_model, artifact_path, conda_env, code_paths, serialization_format, registered_model_name, signature, input_example, await_registration_for, pip_requirements, extra_pip_requirements, pyfunc_predict_fn, metadata)\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;129m@format_docstring\u001b[39m(LOG_MODEL_PARAM_DOCS\u001b[38;5;241m.\u001b[39mformat(package_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscikit-learn\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 335\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_model\u001b[39m(\n\u001b[1;32m 336\u001b[0m sk_model,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 348\u001b[0m metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 349\u001b[0m ):\n\u001b[1;32m 350\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 351\u001b[0m \u001b[38;5;124;03m Log a scikit-learn model as an MLflow artifact for the current run. Produces an MLflow Model\u001b[39;00m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;124;03m containing the following flavors:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 411\u001b[0m \n\u001b[1;32m 412\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mModel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 414\u001b[0m \u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43martifact_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 415\u001b[0m \u001b[43m \u001b[49m\u001b[43mflavor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msklearn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 416\u001b[0m \u001b[43m \u001b[49m\u001b[43msk_model\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msk_model\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 417\u001b[0m \u001b[43m \u001b[49m\u001b[43mconda_env\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconda_env\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mcode_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcode_paths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43mserialization_format\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mserialization_format\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m \u001b[49m\u001b[43mregistered_model_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mregistered_model_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 421\u001b[0m \u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 422\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_example\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_example\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[43m \u001b[49m\u001b[43mawait_registration_for\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mawait_registration_for\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 424\u001b[0m \u001b[43m \u001b[49m\u001b[43mpip_requirements\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpip_requirements\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 425\u001b[0m \u001b[43m \u001b[49m\u001b[43mextra_pip_requirements\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_pip_requirements\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 426\u001b[0m \u001b[43m \u001b[49m\u001b[43mpyfunc_predict_fn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpyfunc_predict_fn\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 427\u001b[0m \u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 428\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/models/model.py:744\u001b[0m, in \u001b[0;36mModel.log\u001b[0;34m(cls, artifact_path, flavor, registered_model_name, await_registration_for, metadata, run_id, resources, **kwargs)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m tracking_uri \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabricks\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m get_uri_scheme(tracking_uri) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdatabricks\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 743\u001b[0m _logger\u001b[38;5;241m.\u001b[39mwarning(_LOG_MODEL_MISSING_SIGNATURE_WARNING)\n\u001b[0;32m--> 744\u001b[0m \u001b[43mmlflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtracking\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfluent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmlflow_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43martifact_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 746\u001b[0m \u001b[38;5;66;03m# if the model_config kwarg is passed in, then log the model config as an params\u001b[39;00m\n\u001b[1;32m 747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model_config \u001b[38;5;241m:=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_config\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/fluent.py:1170\u001b[0m, in \u001b[0;36mlog_artifacts\u001b[0;34m(local_dir, artifact_path, run_id)\u001b[0m\n\u001b[1;32m 1136\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1137\u001b[0m \u001b[38;5;124;03mLog all the contents of a local directory as artifacts of the run. If no run is active,\u001b[39;00m\n\u001b[1;32m 1138\u001b[0m \u001b[38;5;124;03mthis method will create a new active run.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1167\u001b[0m \u001b[38;5;124;03m mlflow.log_artifacts(tmp_dir, artifact_path=\"states\")\u001b[39;00m\n\u001b[1;32m 1168\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1169\u001b[0m run_id \u001b[38;5;241m=\u001b[39m run_id \u001b[38;5;129;01mor\u001b[39;00m _get_or_start_run()\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mrun_id\n\u001b[0;32m-> 1170\u001b[0m \u001b[43mMlflowClient\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/client.py:1977\u001b[0m, in \u001b[0;36mMlflowClient.log_artifacts\u001b[0;34m(self, run_id, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 1930\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_artifacts\u001b[39m(\n\u001b[1;32m 1931\u001b[0m \u001b[38;5;28mself\u001b[39m, run_id: \u001b[38;5;28mstr\u001b[39m, local_dir: \u001b[38;5;28mstr\u001b[39m, artifact_path: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1932\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1933\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Write a directory of files to the remote ``artifact_uri``.\u001b[39;00m\n\u001b[1;32m 1934\u001b[0m \n\u001b[1;32m 1935\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1975\u001b[0m \n\u001b[1;32m 1976\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1977\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_tracking_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/tracking/_tracking_service/client.py:874\u001b[0m, in \u001b[0;36mTrackingServiceClient.log_artifacts\u001b[0;34m(self, run_id, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mlog_artifacts\u001b[39m(\u001b[38;5;28mself\u001b[39m, run_id, local_dir, artifact_path\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 866\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Write a directory of files to the remote ``artifact_uri``.\u001b[39;00m\n\u001b[1;32m 867\u001b[0m \n\u001b[1;32m 868\u001b[0m \u001b[38;5;124;03m Args:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 872\u001b[0m \n\u001b[1;32m 873\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 874\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_artifact_repo\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrun_id\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_artifacts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifact_path\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/store/artifact/local_artifact_repo.py:66\u001b[0m, in \u001b[0;36mLocalArtifactRepository.log_artifacts\u001b[0;34m(self, local_dir, artifact_path)\u001b[0m\n\u001b[1;32m 62\u001b[0m artifact_dir \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 63\u001b[0m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39martifact_dir, artifact_path) \u001b[38;5;28;01mif\u001b[39;00m artifact_path \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39martifact_dir\n\u001b[1;32m 64\u001b[0m )\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(artifact_dir):\n\u001b[0;32m---> 66\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43martifact_dir\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 67\u001b[0m shutil\u001b[38;5;241m.\u001b[39mcopytree(src\u001b[38;5;241m=\u001b[39mlocal_dir, dst\u001b[38;5;241m=\u001b[39martifact_dir, dirs_exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/utils/file_utils.py:211\u001b[0m, in \u001b[0;36mmkdir\u001b[0;34m(root, name)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m!=\u001b[39m errno\u001b[38;5;241m.\u001b[39mEEXIST \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(target):\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m target\n", - "File \u001b[0;32m~/Desktop/Accademy/labs/labs/mlflow_env/lib/python3.8/site-packages/mlflow/utils/file_utils.py:208\u001b[0m, in \u001b[0;36mmkdir\u001b[0;34m(root, name)\u001b[0m\n\u001b[1;32m 206\u001b[0m target \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(root, name) \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m root\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 209\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m e\u001b[38;5;241m.\u001b[39merrno \u001b[38;5;241m!=\u001b[39m errno\u001b[38;5;241m.\u001b[39mEEXIST \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(target):\n", - "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - " \u001b[0;31m[... skipping similar frames: makedirs at line 213 (1 times)]\u001b[0m\n", - "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:213\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m head \u001b[38;5;129;01mand\u001b[39;00m tail \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39mexists(head):\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 213\u001b[0m \u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhead\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexist_ok\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexist_ok\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# Defeats race condition when another thread created the path\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n", - "File \u001b[0;32m~/opt/anaconda3/lib/python3.8/os.py:223\u001b[0m, in \u001b[0;36mmakedirs\u001b[0;34m(name, mode, exist_ok)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m 222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 223\u001b[0m \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m 225\u001b[0m \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[1;32m 226\u001b[0m \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[1;32m 227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\n", - "\u001b[0;31mOSError\u001b[0m: [Errno 30] Read-only file system: '/mlruns'" - ] - } - ], + "outputs": [], "source": [ "# Usando il modello creato nella Parte 1\n", "from sklearn.ensemble import RandomForestClassifier\n", @@ -189,9 +97,7 @@ " y_pred = rf_model.predict(X_test)\n", " accuracy = accuracy_score(y_test, y_pred)\n", " mlflow.log_metric(\"accuracy\", accuracy)\n", - " mlflow.log_metric(\"accuracy\", accuracy)\n", - " mlflow.log_metric(\"accuracy\", accuracy)\n", - " \n", + "\n", " # Salviamo il modello\n", " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", "\n", @@ -203,12 +109,25 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "import mlflow.sklearn\n", + "from sklearn.metrics import accuracy_score\n", + "\n", + "# Inserisci un run_id reale che trovi nell'interfaccia MLflow\n", + "RUN_ID = \"\"\n", + "\n", + "loaded_model = mlflow.sklearn.load_model(f\"runs:/{RUN_ID}/random_forest_model\")\n", + "\n", + "# Verifichiamo l'accuratezza\n", + "y_loaded_pred = loaded_model.predict(X_test)\n", + "acc_loaded = accuracy_score(y_test, y_loaded_pred)\n", + "print(f\"Accuratezza del modello caricato: {acc_loaded:.2f}\")" + ] } ], "metadata": { "kernelspec": { - "display_name": "mlflow_env", + "display_name": "Python 3.10.10 ('venv': venv)", "language": "python", "name": "python3" }, @@ -222,7 +141,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.10.10" + }, + "vscode": { + "interpreter": { + "hash": "a8e57e261110579b348b220fe1c71aea6cd1a8cd238281550a2a6c068185ef06" + } } }, "nbformat": 4, From e25f42b041c4c7023bdfab60675b00f94cae2c0e Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Mar 2025 18:29:44 +0100 Subject: [PATCH 32/70] refactor lab --- AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index 65cae9f..43647cd 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -60,14 +60,21 @@ "Naviga nella cartella del primo laboratorio:\n", "\n", "```sh\n", - "cd Academy/1-Intro-To-ML-And-MLOps\n", + "cd AI/Academy/1-Intro-To-ML-And-MLOps\n", "```\n", "\n", - "### 4. Creare un Virtual Environment\n", + "### 4. Apri il progetto in VSCode\n", + "\n", + "A questo punto apri VSCode dall'explorer oppure lanciando il comando:\n", + "```sh\n", + "code .\n", + "```\n", + "\n", + "### 5. Creare un Virtual Environment\n", "\n", "Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema.\n", "\n", - "Crea un ambiente virtuale:\n", + "Tramite il terminale di VSCode, crea un ambiente virtuale:\n", "\n", "```sh\n", "python -m venv venv\n", @@ -89,7 +96,7 @@ "\n", "Dovresti vedere il prefisso `(venv)` nel terminale, indicando che l'ambiente virtuale è attivo.\n", "\n", - "### 5. Installare i pacchetti da `requirements.txt`\n", + "### 6. Installare i pacchetti da `requirements.txt`\n", "\n", "Tramite il file `requirements.txt`, possiamo installare tutte le dipendenze di questo laboratorio, in particolare **scikit-learn** e **MLflow**, eseguendo il comando:\n", "\n", @@ -101,7 +108,8 @@ "\n", "```sh\n", "pip list\n", - "```" + "```\n", + "A questo punto si può continuare sul file `lab.ipynb`." ] }, { From 953eb482cac236b13c28ac1f6a78633fdb7d3db2 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Mar 2025 18:40:15 +0100 Subject: [PATCH 33/70] refactor lab --- AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 701 ++++++++++++++++++- 1 file changed, 687 insertions(+), 14 deletions(-) diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index 43647cd..3edcfb2 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -141,9 +141,216 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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row IDLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9am...WindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainToday
0Row0Albury12.925.70.0NaNNaNWSW46.0W...26.038.030.01007.61008.7NaN2.021.023.2No
1Row1Albury9.228.00.0NaNNaNNE24.0SE...9.045.016.01017.61012.8NaNNaN18.126.5No
2Row2Albury14.325.00.0NaNNaNW50.0SW...24.049.019.01009.61008.21.0NaN18.124.6No
3Row3Albury9.731.90.0NaNNaNNNW80.0SE...28.042.09.01008.91003.6NaNNaN18.330.2No
4Row4Albury15.918.615.6NaNNaNW61.0NNW...28.076.093.0994.3993.08.08.017.415.8Yes
\n", + "

5 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " row ID Location MinTemp MaxTemp Rainfall Evaporation Sunshine \\\n", + "0 Row0 Albury 12.9 25.7 0.0 NaN NaN \n", + "1 Row1 Albury 9.2 28.0 0.0 NaN NaN \n", + "2 Row2 Albury 14.3 25.0 0.0 NaN NaN \n", + "3 Row3 Albury 9.7 31.9 0.0 NaN NaN \n", + "4 Row4 Albury 15.9 18.6 15.6 NaN NaN \n", + "\n", + " WindGustDir WindGustSpeed WindDir9am ... WindSpeed3pm Humidity9am \\\n", + "0 WSW 46.0 W ... 26.0 38.0 \n", + "1 NE 24.0 SE ... 9.0 45.0 \n", + "2 W 50.0 SW ... 24.0 49.0 \n", + "3 NNW 80.0 SE ... 28.0 42.0 \n", + "4 W 61.0 NNW ... 28.0 76.0 \n", + "\n", + " Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am \\\n", + "0 30.0 1007.6 1008.7 NaN 2.0 21.0 \n", + "1 16.0 1017.6 1012.8 NaN NaN 18.1 \n", + "2 19.0 1009.6 1008.2 1.0 NaN 18.1 \n", + "3 9.0 1008.9 1003.6 NaN NaN 18.3 \n", + "4 93.0 994.3 993.0 8.0 8.0 17.4 \n", + "\n", + " Temp3pm RainToday \n", + "0 23.2 No \n", + "1 26.5 No \n", + "2 24.6 No \n", + "3 30.2 No \n", + "4 15.8 Yes \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd\n", "\n", @@ -170,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -200,9 +407,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensioni del dataset di training: 13476\n", + "Dimensioni del dataset di test: 3370\n" + ] + } + ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -223,9 +439,438 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "RandomForestClassifier(random_state=42)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", @@ -245,9 +890,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuratezza del modello: 0.82\n", + " precision recall f1-score support\n", + "\n", + " 0 0.85 0.93 0.89 2649\n", + " 1 0.61 0.42 0.50 721\n", + "\n", + " accuracy 0.82 3370\n", + " macro avg 0.73 0.67 0.69 3370\n", + "weighted avg 0.80 0.82 0.81 3370\n", + "\n" + ] + } + ], "source": [ "from sklearn.metrics import accuracy_score, classification_report\n", "\n", @@ -264,9 +926,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", @@ -314,7 +987,7 @@ "## Tech: Installazione e Configurazione di MLflow\n", "### Obiettivo\n", "- Configurare un'istanza MLflow in locale per registrare esperimenti.\n", - "### 1. Avviare MLflow con Docker\n", + "### 1. Avviare MLflow\n", "\n", "Esegui il seguente comando per avviare un'istanza locale di MLflow:\n", "```bash\n", @@ -339,7 +1012,7 @@ "Una soluzione basata su questo metodo può essere trovata nella repository interna [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main).\n", "- **Kubernetes**: MLflow viene distribuito su un cluster Kubernetes, garantendo scalabilità e gestione centralizzata. \n", "\n", - "Entrambe le opzioni si basano su una soluzione multi-container dove sono presenti un database persistente e un storage S3, in genere MinIO o soluzioni cloud. \n" + "Entrambe le opzioni si basano su una soluzione multi-container dove sono presenti un database persistente e un storage S3, in genere soluzioni cloud o MinIO. \n" ] }, { @@ -483,7 +1156,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "È possibile notare che con lo stesso dataset di test, il modello ha precisamente lo stessa accuratezza." + "È possibile notare che con lo stesso dataset di test, il modello ha precisamente la stessa accuratezza." ] }, { From e2af60377b98ce43bce7dbe2f62edcc5b777431a Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Mar 2025 18:40:31 +0100 Subject: [PATCH 34/70] refactor lab --- AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 695 +------------------ 1 file changed, 11 insertions(+), 684 deletions(-) diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index 3edcfb2..aac03a9 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -141,216 +141,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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row IDLocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9am...WindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainToday
0Row0Albury12.925.70.0NaNNaNWSW46.0W...26.038.030.01007.61008.7NaN2.021.023.2No
1Row1Albury9.228.00.0NaNNaNNE24.0SE...9.045.016.01017.61012.8NaNNaN18.126.5No
2Row2Albury14.325.00.0NaNNaNW50.0SW...24.049.019.01009.61008.21.0NaN18.124.6No
3Row3Albury9.731.90.0NaNNaNNNW80.0SE...28.042.09.01008.91003.6NaNNaN18.330.2No
4Row4Albury15.918.615.6NaNNaNW61.0NNW...28.076.093.0994.3993.08.08.017.415.8Yes
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5 rows × 22 columns

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" - ], - "text/plain": [ - " row ID Location MinTemp MaxTemp Rainfall Evaporation Sunshine \\\n", - "0 Row0 Albury 12.9 25.7 0.0 NaN NaN \n", - "1 Row1 Albury 9.2 28.0 0.0 NaN NaN \n", - "2 Row2 Albury 14.3 25.0 0.0 NaN NaN \n", - "3 Row3 Albury 9.7 31.9 0.0 NaN NaN \n", - "4 Row4 Albury 15.9 18.6 15.6 NaN NaN \n", - "\n", - " WindGustDir WindGustSpeed WindDir9am ... WindSpeed3pm Humidity9am \\\n", - "0 WSW 46.0 W ... 26.0 38.0 \n", - "1 NE 24.0 SE ... 9.0 45.0 \n", - "2 W 50.0 SW ... 24.0 49.0 \n", - "3 NNW 80.0 SE ... 28.0 42.0 \n", - "4 W 61.0 NNW ... 28.0 76.0 \n", - "\n", - " Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am \\\n", - "0 30.0 1007.6 1008.7 NaN 2.0 21.0 \n", - "1 16.0 1017.6 1012.8 NaN NaN 18.1 \n", - "2 19.0 1009.6 1008.2 1.0 NaN 18.1 \n", - "3 9.0 1008.9 1003.6 NaN NaN 18.3 \n", - "4 93.0 994.3 993.0 8.0 8.0 17.4 \n", - "\n", - " Temp3pm RainToday \n", - "0 23.2 No \n", - "1 26.5 No \n", - "2 24.6 No \n", - "3 30.2 No \n", - "4 15.8 Yes \n", - "\n", - "[5 rows x 22 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -377,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -407,18 +200,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dimensioni del dataset di training: 13476\n", - "Dimensioni del dataset di test: 3370\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", @@ -439,438 +223,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
RandomForestClassifier(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "RandomForestClassifier(random_state=42)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", @@ -890,26 +245,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Accuratezza del modello: 0.82\n", - " precision recall f1-score support\n", - "\n", - " 0 0.85 0.93 0.89 2649\n", - " 1 0.61 0.42 0.50 721\n", - "\n", - " accuracy 0.82 3370\n", - " macro avg 0.73 0.67 0.69 3370\n", - "weighted avg 0.80 0.82 0.81 3370\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from sklearn.metrics import accuracy_score, classification_report\n", "\n", @@ -926,20 +264,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", From 0ae8b14359434e03f26e5fcfdab9d5d3b871012a Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 6 Mar 2025 10:20:50 +0100 Subject: [PATCH 35/70] refactor lab and repo --- AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore | 1 + AI/Academy/1-Intro-To-ML-And-MLOps/README.md | 98 ++++ AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 435 +++++++++--------- 3 files changed, 329 insertions(+), 205 deletions(-) create mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/README.md diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore b/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore index 5dd41ee..1c5d00a 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/.gitignore @@ -1,6 +1,7 @@ # local mlflow mlartifacts/* mlruns/* +dev/* # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md new file mode 100644 index 0000000..e921163 --- /dev/null +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md @@ -0,0 +1,98 @@ +# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow + +Benvenuto in questo laboratorio! Qui imparerai a: + +1. **Caricare e preparare un dataset meteo**, con dati di temperatura e umidità. +2. **Addestrare un modello di Machine Learning** con Scikit-learn per prevedere la pioggia. +3. **Valutare il modello** e comprendere i risultati. +4. **Integrare MLflow** per tracciare metriche, parametri e versioni del modello. + +Seguiremo un approccio guidato, con spiegazioni dettagliate ad ogni passaggio. +La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow. + +--- + +## Parte 0: Setup dell'Environment per Python + +Prima di iniziare, dobbiamo assicurarci di avere tutto il necessario per eseguire correttamente il laboratorio. + +#### **Requisiti** + +Questo laboratorio assume che **Python** e **miniconda** siano già installati, la repository [kiratech/labs](https://github.com/kiratech/labs.git) sia accessibile e che Git sia configurato correttamente sul computer locale. + +### 1. Clonare la Repository +Per iniziare, clona la repository del laboratorio eseguendo il seguente comando nel terminale: +```sh + git clone https://github.com/kiratech/labs.git +``` + +### 2. Fare il Checkout del Branch Lab + +Dopo aver clonato la repository, entra nella cartella del progetto: + +```sh + cd labs +``` + +Quindi, fai il checkout del branch `lab`: + +```sh + git checkout academy-ai +``` +In questa cartella sono presenti le risorse relative ai laboratori con tema AI. + +### 3. Spostarsi nella Cartella 1-Intro-To-ML-And-MLOps + +Naviga nella cartella del primo laboratorio: + +```sh + cd AI/Academy/1-Intro-To-ML-And-MLOps +``` + +### 4. Apri il progetto in VSCode + +A questo punto apri VSCode dall'explorer oppure lanciando il comando: +```sh + code . +``` + +### 5. Creare un Virtual Environment + +Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema. + +Tramite il terminale di **VSCode**, crea un ambiente virtuale: + +```sh + conda create --name lab_env python=3.12 pip -y +``` + +Attiva l'Ambiente Virtuale +```sh + conda activate lab_env +``` + +Dovresti vedere il prefisso `(lab_env)` nel terminale, indicando che l'ambiente virtuale è attivo. + +### 6. Installare i Pacchetti Necessari + +Oltre ai pacchetti presenti di default nell'environment, è possibile che servano librerie aggiuntive per il laboratorio. +Prima di installarle, è sempre buona pratica aggiornare `pip` per evitare problemi di compatibilità: +```sh + pip install --upgrade pip +``` + +Ora installiamo alcuni pacchetti fondamentali per l'analisi dei dati e il machine learning: +```sh + pip install scikit-learn pandas seaborn mlflow ipykernel +``` +- [**scikit-learn**](https://scikit-learn.org/stable/index.html): Libreria per il machine learning con strumenti di modellazione e valutazione. +- [**pandas**](https://pandas.pydata.org/): Framework per la manipolazione e l'analisi dei dati in Python. +- [**seaborn**](https://seaborn.pydata.org/): Libreria per la visualizzazione di dati basata su Matplotlib. +- [**mlflow**](https://mlflow.org/): Strumento per il tracciamento e la gestione di esperimenti di machine learning. + +Verifica che i pacchetti siano stati installati correttamente con: +```sh + conda list +``` + +A questo punto si può continuare sul file `lab.ipynb`. \ No newline at end of file diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index aac03a9..b5cfc00 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -6,13 +6,14 @@ "source": [ "# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow\n", "\n", - "In questo laboratorio, affrontiamo un percorso step-by-step:\n", + "Benvenuto in questo laboratorio! Qui imparerai a:\n", "\n", - "1. **Caricare e preparare un dataset meteo** (con dati di temperatura e umidità) per prevedere se pioverà o meno.\n", - "2. **Addestrare un modello di Machine Learning** con Scikit-learn.\n", + "1. **Caricare e preparare un dataset meteo**, con dati di temperatura e umidità.\n", + "2. **Addestrare un modello di Machine Learning** con Scikit-learn per prevedere la pioggia.\n", "3. **Valutare il modello** e comprendere i risultati.\n", - "4. **Estendere il progetto** integrando **MLflow** per tracciare metriche, parametri e versioni del modello.\n", + "4. **Integrare MLflow** per tracciare metriche, parametri e versioni del modello.\n", "\n", + "Seguiremo un approccio guidato, con spiegazioni dettagliate ad ogni passaggio. \n", "La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow." ] }, @@ -27,116 +28,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Parte 0: Setup dell'Environment per Python\n", + "## Parte 1: Dai Dati al Modello di Machine Learning (Supervised Learning con Scikit-learn) \n", "\n", - "#### **Requisiti**\n", - "\n", - "Questo laboratorio assume che Python sia già installato, la repository [kiratech/labs](https://github.com/kiratech/labs.git) sia accessibile e che Git sia configurato correttamente sul computer locale.\n", - "\n", - "### 1. Clonare la Repository\n", - "Per iniziare, clona la repository del laboratorio eseguendo il seguente comando:\n", - "\n", - "```sh\n", - "git clone https://github.com/kiratech/labs.git\n", - "```\n", - "\n", - "### 2. Fare il Checkout del Branch Lab\n", - "\n", - "Dopo aver clonato la repository, entra nella cartella del progetto:\n", - "\n", - "```sh\n", - "cd labs\n", - "```\n", - "\n", - "Quindi, fai il checkout del branch `lab`:\n", - "\n", - "```sh\n", - "git checkout academy-ai\n", - "```\n", - "In questa cartella sono presenti le risorse relative ai laboratori con tema AI.\n", - "\n", - "### 3. Spostarsi nella Cartella 1-Intro-To-ML-And-MLOps\n", - "\n", - "Naviga nella cartella del primo laboratorio:\n", - "\n", - "```sh\n", - "cd AI/Academy/1-Intro-To-ML-And-MLOps\n", - "```\n", - "\n", - "### 4. Apri il progetto in VSCode\n", - "\n", - "A questo punto apri VSCode dall'explorer oppure lanciando il comando:\n", - "```sh\n", - "code .\n", - "```\n", - "\n", - "### 5. Creare un Virtual Environment\n", - "\n", - "Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema.\n", - "\n", - "Tramite il terminale di VSCode, crea un ambiente virtuale:\n", - "\n", - "```sh\n", - "python -m venv venv\n", - "```\n", - "\n", - "Attiva l'Ambiente Virtuale\n", - "\n", - "- **Su macOS e Linux:**\n", - " \n", - " ```sh\n", - " source venv/bin/activate\n", - " ```\n", - " \n", - "- **Su Windows:**\n", - " \n", - " ```sh\n", - " venv\\Scripts\\activate\n", - " ```\n", - "\n", - "Dovresti vedere il prefisso `(venv)` nel terminale, indicando che l'ambiente virtuale è attivo.\n", - "\n", - "### 6. Installare i pacchetti da `requirements.txt`\n", - "\n", - "Tramite il file `requirements.txt`, possiamo installare tutte le dipendenze di questo laboratorio, in particolare **scikit-learn** e **MLflow**, eseguendo il comando:\n", - "\n", - "```sh\n", - "pip install -r src/requirements.txt\n", - "```\n", - "\n", - "Verifica che i pacchetti siano stati installati correttamente con:\n", - "\n", - "```sh\n", - "pip list\n", - "```\n", - "A questo punto si può continuare sul file `lab.ipynb`." + "### Obiettivo \n", + "Costruire un **modello di classificazione** che possa prevedere se pioverà, utilizzando come input i dati di **temperatura** e **umidità**. Il modello verrà addestrato con **Scikit-learn**, uno strumento potente per il Machine Learning in Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Parte 1: Dai dati al modello ML (Supervised Learning con Scikit-learn)\n", + "### 1. Preparazione del Dataset \n", "\n", - "**Obiettivo**\n", - "- Creare un semplice **modello di classificazione** per predire la pioggia basandoci su dati di temperatura e umidità.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1. Preparazione del Dataset\n", + "Prima di addestrare un modello di Machine Learning, è fondamentale pulire i dati, perché valori mancanti o errati possono compromettere le previsioni. Un dataset ben preparato permette al modello di imparare meglio e fornire risultati più accurati. \n", + "\n", + "Per questo laboratorio utilizzeremo un dataset di esempio: \n", + "[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv) \n", + "\n", + "Il dataset **Weather Test Data** contiene informazioni meteorologiche raccolte in diversi momenti. Ogni riga rappresenta un'osservazione con parametri come **temperatura**, **umidità**, **pressione atmosferica** e altre variabili meteo. \n", "\n", - "Per semplicità, qui useremo un dataset di esempio:\n", - "[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv)" + "L'obiettivo di questo dataset è analizzare i pattern climatici e utilizzarli per addestrare un modello di Machine Learning in grado di prevedere condizioni future, come la probabilità di pioggia o variazioni di temperatura." ] }, { @@ -147,10 +58,10 @@ "source": [ "import pandas as pd\n", "\n", - "# URL del dataset (sostituiscilo con il tuo link)\n", + "# URL del dataset \n", "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", "\n", - "# Carica il dataset\n", + "# Caricamento del dataset in un dataframe Pandas\n", "df = pd.read_csv(url)\n", "\n", "# Mostra le prime righe\n", @@ -162,10 +73,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Esplorazione e pulizia dei Dati\n", - "1. Controlliamo se ci sono valori mancanti\n", - "2. Trasformiamo la colonna `Label` in una variabile binaria (0 = NoRain, 1 = Rain)\n", - "3. Selezione delle feature " + "### 2. Esplorazione e Pulizia dei Dati \n", + "\n", + "Per garantire che il nostro modello funzioni correttamente, dobbiamo prima esaminare e preparare il dataset. Ecco i passaggi fondamentali: \n", + "\n", + "1. **Controllo dei dati mancanti**: verifichiamo se ci sono valori assenti, poiché potrebbero compromettere l'addestramento del modello. Se necessario, possiamo eliminarli o sostituirli con valori appropriati. \n", + "2. **Conversione della colonna `Label`**: trasformiamo le categorie testuali (*NoRain* e *Rain*) in valori numerici (0 per *NoRain*, 1 per *Rain*), in modo che il modello possa interpretarli correttamente. \n", + "3. **Selezione delle feature principali**: scegliamo solo le colonne più rilevanti (es. temperatura e umidità) per semplificare il modello e migliorare le sue prestazioni." ] }, { @@ -191,11 +105,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Suddivisione del Dataset in Training e Test\n", + "### 3. Suddivisione del Dataset in Training e Test \n", + "\n", + "Per addestrare e valutare correttamente il modello, dividiamo il dataset in due parti: \n", "\n", - "Prima di addestrare il modello, dividiamo il dataset in:\n", - "- **X** (features) → Temperatura, Umidità\n", - "- **y** (target) → Label (Rain/NoRain)" + "- **X (feature)**: contiene le informazioni che useremo per fare previsioni, come **temperatura** e **umidità**. \n", + "- **y (target)**: rappresenta la variabile che vogliamo prevedere, ovvero se pioverà (*Rain*) o meno (*NoRain*). \n", + "\n", + "Dividiamo i dati in **training set** (80%) e **test set** (20%) per i seguenti motivi: \n", + "\n", + "1. **Addestramento del modello** \n", + " - L'80% dei dati viene usato per insegnare al modello a riconoscere i pattern tra le feature e il target. \n", + "\n", + "2. **Valutazione del modello** \n", + " - Il restante 20% dei dati non viene usato nell'addestramento, ma serve per testare il modello su dati mai visti prima. \n", + " - Questo ci permette di capire se il modello è davvero in grado di fare previsioni accurate su nuovi dati. \n", + "\n", + "3. **Evitare overfitting** \n", + " - Se testassimo il modello sugli stessi dati con cui è stato addestrato, potremmo ottenere risultati ingannevolmente buoni, perché il modello li avrebbe semplicemente memorizzati. \n", + " - Usare dati di test separati aiuta a verificare se il modello può generalizzare le sue previsioni a nuovi dati reali. \n", + "\n", + "Questa suddivisione è un passaggio fondamentale per costruire un modello affidabile e capace di fare previsioni corrette su dati che non ha mai visto prima." ] }, { @@ -216,9 +146,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Creazione e Addestramento del Modello\n", + "### 4. Creazione e Addestramento del Modello \n", + "\n", + "Ora che abbiamo preparato i dati, possiamo costruire e addestrare un modello di Machine Learning. Per questo utilizzeremo un classificatore chiamato [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), una delle tecniche più utilizzate per problemi di classificazione. \n", + "\n", + "#### Perché usiamo **Random Forest**? \n", + "- È un modello basato su **alberi decisionali**, che suddivide i dati in più passaggi per prendere decisioni accurate. \n", + "- È **robusto** e gestisce bene sia dati numerici che categoriali. \n", + "- È meno sensibile ai dati rumorosi rispetto a un singolo albero decisionale, perché combina più alberi per migliorare la precisione. \n", "\n", - "Utilizziamo un classificatore semplice, ad esempio [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)." + "Il modello verrà addestrato utilizzando i dati di **temperatura** e **umidità** per prevedere se ci sarà **pioggia** o meno. Dopo l'addestramento, lo testeremo su dati nuovi per valutarne l'accuratezza." ] }, { @@ -238,9 +175,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 5. Valutazione del Modello\n", + "### 5. Valutazione del Modello \n", "\n", - "Calcoliamo l’accuratezza del modello e altre metriche. Inoltre, visualizziamo la matrice di confusione ovvero un metodo visivo per valutare le prestazioni di un modello." + "Dopo aver addestrato il modello, dobbiamo verificare quanto è accurato nelle previsioni. Per farlo, calcoliamo **l'accuratezza** e altre metriche di valutazione. \n", + "\n", + "#### Perché è importante valutare il modello? \n", + "Un modello di Machine Learning non è utile se non sappiamo quanto sia affidabile. La valutazione ci aiuta a capire: \n", + "- **Se il modello sta imparando correttamente dai dati** o se sta solo memorizzando le risposte (overfitting). \n", + "- **Se può essere utilizzato su dati nuovi** e fare previsioni realistiche. \n", + "\n", + "#### Matrice di Confusione \n", + "Oltre all'accuratezza, utilizzeremo la **matrice di confusione**, un metodo visivo che mostra dove il modello fa previsioni corrette e dove sbaglia. \n", + "- Ci aiuta a individuare **falsi positivi** e **falsi negativi**, che sono errori critici in molti scenari reali. \n", + "- È utile per migliorare il modello, ad esempio regolando soglie di decisione o bilanciando i dati di input. \n", + "\n", + "Con queste analisi possiamo capire se il nostro modello è pronto per essere utilizzato o se necessita di miglioramenti." ] }, { @@ -249,14 +198,16 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.metrics import accuracy_score, classification_report\n", + "from sklearn.metrics import accuracy_score, f1_score, classification_report\n", "\n", "# Predizioni sul set di test\n", "y_pred = model.predict(X_test)\n", "\n", - "# Calcolo dell'accuratezza\n", + "# Calcolo dell'accuratezza ed f1-score\n", "accuracy = accuracy_score(y_test, y_pred)\n", + "f1 = f1_score(y_test, y_pred, average='weighted')\n", "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", + "print(f\"F1-score del modello: {f1:.2f}\")\n", "\n", "# Report di classificazione\n", "print(classification_report(y_test, y_pred))" @@ -291,13 +242,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Conclusione (Parte 1)\n", - "In questa prima parte, abbiamo:\n", - "1. Caricato un dataset relativo alle condizioni meteo\n", - "2. Pulito i dati e trasformato la variabile target\n", - "3. Suddiviso il dataset in training e test\n", - "4. Creato un modello di classificazione con RandomForest\n", - "5. Valutato le prestazioni con accuratezza e matrice di confusione" + "### Conclusione (Parte 1) \n", + "\n", + "In questa prima parte, abbiamo seguito un processo step-by-step per costruire un modello di Machine Learning in grado di prevedere la pioggia. Ecco cosa abbiamo fatto: \n", + "\n", + "1. **Caricato il dataset meteo** per analizzare temperatura, umidità e altre variabili. \n", + "2. **Pulito e preparato i dati**, gestendo eventuali valori mancanti e trasformando la variabile target in un formato comprensibile al modello. \n", + "3. **Suddiviso il dataset** in dati di training (80%) e di test (20%) per addestrare e valutare il modello correttamente. \n", + "4. **Creato un modello di classificazione** utilizzando **RandomForestClassifier**, un algoritmo potente e robusto. \n", + "5. **Valutato le prestazioni del modello** calcolando l’accuratezza e analizzando la matrice di confusione per identificare eventuali errori. \n", + "\n", + "Ora che abbiamo costruito il modello base, nella prossima parte esploreremo come integrare **MLflow** per tracciare gli esperimenti e migliorare ulteriormente le prestazioni." ] }, { @@ -311,35 +266,55 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tech: Installazione e Configurazione di MLflow\n", - "### Obiettivo\n", - "- Configurare un'istanza MLflow in locale per registrare esperimenti.\n", - "### 1. Avviare MLflow\n", + "## Tech: Installazione e Configurazione di MLflow \n", + "\n", + "### Obiettivo \n", + "Configurare un'istanza locale di **MLflow** per registrare esperimenti, monitorare le metriche e gestire i modelli di Machine Learning in modo organizzato. \n", + "\n", + "### 1. Avviare MLflow \n", + "\n", + "Per avviare MLflow in locale, esegui il seguente comando nel terminale: \n", "\n", - "Esegui il seguente comando per avviare un'istanza locale di MLflow:\n", "```bash\n", - "mlflow ui\n", + " mlflow ui\n", "```\n", - "In questo modo MLflow espone l'interfaccia grafica di MLflow all'indirizzo:\n", + "\n", + "Dopo averlo avviato, l'interfaccia grafica sarà accessibile all'indirizzo: \n", + "\n", "```\n", - "http://127.0.0.1:5000\n", + " http://127.0.0.1:5000\n", "```\n", - "Questa configurazione salva artefatti ed esperimenti in locale. " + "\n", + "Questa configurazione permette di **salvare esperimenti e modelli localmente**, consentendo di tracciare le diverse versioni dei modelli, confrontare le metriche di valutazione e ottimizzare i processi di addestramento. \n", + "\n", + "Nelle prossime sezioni, vedremo come registrare parametri, metriche e modelli direttamente all'interno di MLflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### MLflow in Produzione\n", - "Nel contesto produttivo e presso i clienti, non si esegue MLflow in locale. \n", - "Generalmente si scelgono soluzioni che permettono un'architettura più robusta e scalabile, evitando problemi di gestione manuale degli esperimenti e della persistenza dei dati.\n", - "Le soluzioni comuni includono:\n", - "- **Docker Compose**: Un file docker-compose.yml viene utilizzato per avviare MLflow con un database backend e uno storage remoto. \n", - "Una soluzione basata su questo metodo può essere trovata nella repository interna [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main).\n", - "- **Kubernetes**: MLflow viene distribuito su un cluster Kubernetes, garantendo scalabilità e gestione centralizzata. \n", - "\n", - "Entrambe le opzioni si basano su una soluzione multi-container dove sono presenti un database persistente e un storage S3, in genere soluzioni cloud o MinIO. \n" + "### MLflow in Produzione \n", + "\n", + "In ambienti di produzione e presso i clienti, **MLflow non viene eseguito in locale**, ma viene integrato in un'infrastruttura più solida e scalabile. Questo evita problemi legati alla gestione manuale degli esperimenti e alla persistenza dei dati. \n", + "\n", + "Le soluzioni più comuni includono: \n", + "\n", + "- **Docker Compose** \n", + " - MLflow viene avviato utilizzando un file `docker-compose.yml`, che configura un database backend e uno storage remoto per salvare gli esperimenti. \n", + " - Questo approccio è utile per ambienti controllati, dove è necessario un setup rapido e replicabile. \n", + " - Un esempio di implementazione è disponibile nella repository interna: \n", + " [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main). \n", + "\n", + "- **Kubernetes (K8s)** \n", + " - MLflow viene distribuito su un **cluster Kubernetes**, consentendo una gestione scalabile e centralizzata degli esperimenti. \n", + " - Questo approccio è ideale per ambienti enterprise, dove sono richiesti elevati livelli di affidabilità, sicurezza e scalabilità. \n", + "\n", + "Entrambe le soluzioni si basano su un'**architettura multi-container**, che include: \n", + "- **Un database persistente** (es. PostgreSQL o MySQL) per memorizzare i metadati degli esperimenti. \n", + "- **Uno storage S3 o MinIO** per salvare i modelli e gli artefatti, garantendo una gestione sicura e scalabile dei dati. \n", + "\n", + "Questi approcci assicurano che MLflow possa essere utilizzato in modo affidabile in produzione, integrandosi con infrastrutture cloud o on-premise per una gestione efficace dei modelli di Machine Learning." ] }, { @@ -353,18 +328,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Parte 2: Integrazione con MLflow\n", + "## Parte 2: Integrazione con MLflow \n", "\n", - "Adesso, estendiamo il codice esistente per **tracciare i nostri esperimenti** con **MLflow**. Questo ci permetterà di:\n", - "- Loggare i nostri parametri (es. `n_estimators`)\n", - "- Salvare l’accuratezza e altre metriche\n", - "- Salvare il modello e ricaricarlo facilmente\n", + "Ora estenderemo il codice esistente per **tracciare i nostri esperimenti** utilizzando **MLflow**. Questo ci permetterà di monitorare il processo di addestramento del modello, confrontare le diverse configurazioni e gestire le versioni del modello in modo strutturato. \n", "\n", - "### Obiettivo\n", - "- Integrare MLflow nel codice esistente per versionare e tracciare le metriche del modello.\n", + "### Perché integrare MLflow? \n", + "Con MLflow possiamo: \n", + "- **Registrare i parametri di addestramento** (es. `n_estimators` per Random Forest) per confrontare diverse configurazioni. \n", + "- **Salvare le metriche di valutazione** (es. accuratezza, F1-score) per monitorare le prestazioni del modello. \n", + "- **Archiviare il modello addestrato** per poterlo ricaricare facilmente in futuro e riutilizzarlo senza doverlo riaddestrare. \n", "\n", - "### 1. Setup MLflow\n", - "Impostiamo le variabili di MLflow per questo esperimento:" + "### Obiettivo \n", + "Integrare MLflow nel codice esistente per **tracciare e versionare i modelli**, registrando parametri, metriche e artefatti in modo strutturato. \n", + "\n", + "### 1. Configurazione di MLflow \n", + "Prima di iniziare a tracciare gli esperimenti, impostiamo le variabili necessarie per utilizzare MLflow in questo progetto." ] }, { @@ -385,19 +363,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Verifichiamo che dalla nostra interfaccia grafica il nuovo esperimento sia visibile." + "Verifichiamo che dalla [nostra interfaccia grafica](http://127.0.0.1:5000) il nuovo esperimento sia visibile." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Logging dei Parametri e Metriche\n", + "### 2. Registrazione di Parametri, Metriche e Modello \n", + "\n", + "Con **MLflow**, possiamo salvare e tracciare automaticamente diverse informazioni durante l'addestramento del modello. Questo ci aiuta a confrontare le performance tra diverse configurazioni e a recuperare facilmente i modelli migliori. \n", + "\n", + "Ecco cosa possiamo registrare: \n", + "\n", + "- **Parametri** → Valori utilizzati per configurare il modello, come `n_estimators` (numero di alberi in Random Forest) e altri iperparametri. \n", + "- **Metriche** → Indicatori delle prestazioni del modello, come **accuratezza**, **F1-score**, precisione e recall. \n", + "- **Modello** → La versione del modello addestrato, che potrà essere ricaricata e riutilizzata senza bisogno di riaddestramento. \n", "\n", - "Possiamo loggare:\n", - "- Parametri → `n_estimators` e altre impostazioni\n", - "- Metriche → accuratezza\n", - "- Modello → versione del modello addestrato\n" + "Registrando questi elementi, possiamo analizzare e confrontare le diverse versioni del modello in modo strutturato e riproducibile." ] }, { @@ -407,56 +390,96 @@ "outputs": [], "source": [ "# Usando il modello creato nella Parte 1\n", + "import os\n", "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import accuracy_score, f1_score\n", + "from mlflow.models.signature import infer_signature\n", + "\n", + "# Eseguiremo 4 esperimenti per addestrare più modelli \n", + "n_estimators = [1, 10, 100, 500]\n", + "\n", + "for n_e in n_estimators:\n", + " # Creiamo una nuova run MLflow\n", + " with mlflow.start_run():\n", + " # Log Param\n", + " mlflow.log_param(\"n_estimators\", n_e)\n", "\n", - "n_estimators = 100\n", + " # Creazione del modello\n", + " rf_model = RandomForestClassifier(n_estimators=n_e, random_state=42)\n", + " rf_model.fit(X_train, y_train)\n", "\n", - "# Creiamo una nuova run MLflow\n", - "with mlflow.start_run():\n", - " # Log Param\n", - " mlflow.log_param(\"n_estimators\", n_estimators)\n", + " # Calcolo metriche\n", + " y_pred = rf_model.predict(X_test)\n", + " accuracy = accuracy_score(y_test, y_pred)\n", + " f1 = f1_score(y_test, y_pred, average='weighted')\n", + " mlflow.log_metric(\"accuracy\", accuracy)\n", + " mlflow.log_metric(\"f1\", f1)\n", "\n", - " # Creazione del modello\n", - " rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n", - " rf_model.fit(X_train, y_train)\n", + " # Creiamo una heatmap con Seaborn\n", + " target_names = [\"No Rain\", \"Rain\"]\n", + " cm = confusion_matrix(y_test, y_pred)\n", + " plt.figure(figsize=(6, 4))\n", + " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", "\n", - " # Calcolo metriche\n", - " y_pred = rf_model.predict(X_test)\n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " mlflow.log_metric(\"accuracy\", accuracy)\n", + " # Aggiungiamo i titoli\n", + " plt.xlabel(\"Predicted Label\")\n", + " plt.ylabel(\"True Label\")\n", + " plt.title(\"Confusion Matrix\")\n", "\n", - " # Salviamo il modello\n", - " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", + " # Salviamo l'immagine come PNG\n", + " if not os.path.exists(\"dev/\"):\n", + " os.makedirs(\"dev/\")\n", + " plt.savefig(\"dev/confusion_matrix.png\")\n", + " plt.close()\n", + " # Salviamo la confusion matrix su MLFlow come artifatto\n", + " mlflow.log_artifact(\"dev/confusion_matrix.png\")\n", "\n", - " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" + " # Salviamo il modello\n", + " example_dict = {'MinTemp': 1.1, 'MaxTemp': 1.1, 'Humidity3pm': 1.1, 'Humidity9am': 1.1}\n", + " signature = infer_signature(model_input=example_dict)\n", + " mlflow.sklearn.log_model(rf_model, \"random_forest_model\", signature=signature)\n", + "\n", + " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Visualizzare e Confrontare i Risultati\n", + "### 3. Visualizzazione e Confronto dei Risultati \n", + "\n", + "Dopo aver registrato i parametri, le metriche e i modelli, possiamo utilizzare **MLflow** per esplorare e confrontare i diversi esperimenti. \n", "\n", - "MLflow fornisce un’interfaccia web per esplorare tutte le run ed esperimenti.\n", + "MLflow fornisce un’interfaccia web accessibile all’indirizzo: \n", "\n", "```bash\n", - "http://127.0.0.1:5000\n", + " http://127.0.0.1:5000\n", "```\n", "\n", - "Accedi all’URL indicato nel terminale. Nella sezione **Experiments** potrai vedere:\n", - "- Parametri loggati\n", - "- Metriche\n", - "- Modelli salvati" + "Accedendo a questa interfaccia, nella sezione **Experiments**, sarà possibile: \n", + "- **Esaminare i parametri** utilizzati in ogni esperimento. \n", + "- **Confrontare le metriche** tra diverse configurazioni di modello. \n", + "- **Visualizzare e scaricare i modelli salvati**, facilitando il riutilizzo e il deployment. \n", + "\n", + "Questa funzionalità consente di monitorare l’evoluzione delle performance del modello e di identificare rapidamente le configurazioni migliori." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Caricare un Modello Salvato con MLflow\n", + "### 4. Caricare un Modello Salvato con MLflow \n", + "\n", + "MLflow consente di salvare e ricaricare facilmente i modelli addestrati, evitando la necessità di riaddestrarli ogni volta. \n", + "\n", + "Per recuperare un modello salvato in MLflow, è necessario copiare **l'ID della run** dell'esperimento eseguito. Questo ID identifica in modo univoco ogni esperimento registrato e permette di caricare il modello corrispondente per effettuare previsioni future. \n", + "\n", + "Questa funzionalità è particolarmente utile per: \n", + "- **Riutilizzare un modello addestrato** senza dover ripetere il training. \n", + "- **Confrontare diverse versioni** di un modello per scegliere quella più performante. \n", + "- **Integrare il modello in applicazioni o API**, senza doverlo ricostruire da zero. \n", "\n", - "Copiando l'id della run dell'esperimento eseguito, possiamo recuperare un modello da MLflow per fare previsioni future." + "Nelle prossime sezioni vedremo come eseguire questo processo in pratica." ] }, { @@ -483,34 +506,36 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "È possibile notare che con lo stesso dataset di test, il modello ha precisamente la stessa accuratezza." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusioni\n", - "In questo laboratorio abbiamo:\n", - "1. **Creato un modello di classificazione con Scikit-learn** su un dataset meteo.\n", - "2. **Aggiunto MLflow** per tracciare parametri, metriche e gestire le versioni del modello.\n", - "3. **Visualizzato i risultati** con l’interfaccia MLflow UI e ricaricato il modello salvato.\n" + "## Conclusioni \n", + "\n", + "In questo laboratorio, abbiamo seguito un processo completo per costruire e monitorare un modello di Machine Learning applicato ai dati meteorologici. In particolare, abbiamo: \n", + "\n", + "1. **Creato un modello di classificazione** utilizzando **Scikit-learn**, sfruttando temperatura e umidità per prevedere la pioggia. \n", + "2. **Integrato MLflow** per tracciare i parametri di addestramento, registrare le metriche di valutazione e gestire le versioni del modello in modo strutturato. \n", + "3. **Esplorato e confrontato i risultati** attraverso l’interfaccia di MLflow UI, verificando le diverse configurazioni e caricando un modello salvato per future previsioni. \n", + "\n", + "Questo approccio ci permette di migliorare il processo di sviluppo dei modelli di Machine Learning, rendendolo più organizzato, riproducibile e scalabile." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Prossimi Passi\n", - "- Provare iperparametri diversi (`n_estimators`, `max_depth`, ecc.) e confrontare i risultati su MLflow.\n", - "- Integrare un sistema di **continuous integration/continuous deployment (CI/CD)** per distribuire automaticamente il modello.\n", - "- Monitorare in produzione il drift del modello (eventuali cali di accuratezza)." + "## Prossimi Passi \n", + "\n", + "Ora che abbiamo costruito e tracciato il nostro modello, possiamo esplorare ulteriori miglioramenti e integrare il lavoro in un flusso più avanzato. \n", + "\n", + "- **Ottimizzazione degli iperparametri**: testare diverse configurazioni di `n_estimators`, `max_depth` e altri parametri del modello, registrando i risultati in **MLflow** per identificare la combinazione migliore. \n", + "- **Automazione con CI/CD**: integrare un sistema di **Continuous Integration/Continuous Deployment (CI/CD)** per addestrare e distribuire automaticamente nuove versioni del modello, riducendo il rischio di errori manuali. \n", + "- **Monitoraggio del modello in produzione**: implementare un sistema di **monitoraggio del drift del modello**, per rilevare eventuali cali di accuratezza nel tempo e decidere quando è necessario riaddestrarlo con nuovi dati. \n", + "\n", + "Questi passaggi aiutano a trasformare il modello sviluppato in un sistema robusto e affidabile, pronto per essere utilizzato in scenari reali." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.10 ('venv': venv)", + "display_name": "Python 3.12.9 ('lab_env')", "language": "python", "name": "python3" }, @@ -524,12 +549,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.12.9" }, "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "a8e57e261110579b348b220fe1c71aea6cd1a8cd238281550a2a6c068185ef06" + "hash": "fbd6de99d3dee89886aa6f30475b6b26f6fe2ef531b8e041d1262739f0fd6851" } } }, From b62dad8772455f28ec216d9868f6a1d1241f3072 Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 12 Mar 2025 13:24:11 +0100 Subject: [PATCH 36/70] fix accordingly to review --- AI/Academy/1-Intro-To-ML-And-MLOps/README.md | 75 ++-- AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb | 326 +++++++++--------- .../src/requirements.txt | 153 -------- .../src/solution.ipynb | 154 --------- 4 files changed, 201 insertions(+), 507 deletions(-) delete mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt delete mode 100644 AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md index e921163..76da717 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md @@ -1,98 +1,99 @@ -# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow +# Lab: Basic ML Model with Weather Dataset + MLflow Integration -Benvenuto in questo laboratorio! Qui imparerai a: +Welcome to this lab! Here you will learn how to: -1. **Caricare e preparare un dataset meteo**, con dati di temperatura e umidità. -2. **Addestrare un modello di Machine Learning** con Scikit-learn per prevedere la pioggia. -3. **Valutare il modello** e comprendere i risultati. -4. **Integrare MLflow** per tracciare metriche, parametri e versioni del modello. +1. **Load and prepare a weather dataset**, with temperature and humidity data. +2. **Train a Machine Learning model** using Scikit-learn, a powerful tool for Machine Learning in Python, to predict rain. +3. **Evaluate the model** computing metrics to determine how well it makes predictions on new data. +4. **Integrate MLflow**, one of the most used tool to track metrics, parameters, and model versions. -Seguiremo un approccio guidato, con spiegazioni dettagliate ad ogni passaggio. -La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow. +We will follow a guided approach with detailed explanations at each step. +The first part focuses on Scikit-learn and the weather dataset. The second part extends the existing code with MLflow. --- -## Parte 0: Setup dell'Environment per Python +## Part 0: Setting Up the Python Environment -Prima di iniziare, dobbiamo assicurarci di avere tutto il necessario per eseguire correttamente il laboratorio. +Before we begin, we need to ensure we have everything required to run the lab properly. -#### **Requisiti** +#### **Requirements** -Questo laboratorio assume che **Python** e **miniconda** siano già installati, la repository [kiratech/labs](https://github.com/kiratech/labs.git) sia accessibile e che Git sia configurato correttamente sul computer locale. +This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well. +In order to execute this laboratory, you will be asked to install a set of tools common in Machine Learning field: +- [**scikit-learn**](https://scikit-learn.org/stable/index.html): Machine learning library with modeling and evaluation tools. +- [**pandas**](https://pandas.pydata.org/): Framework for data manipulation and analysis in Python. +- [**seaborn**](https://seaborn.pydata.org/): Data visualization library based on Matplotlib. +- [**mlflow**](https://mlflow.org/): Tool for tracking and managing machine learning experiments. -### 1. Clonare la Repository -Per iniziare, clona la repository del laboratorio eseguendo il seguente comando nel terminale: +### 1. Clone the Repository +To start, clone the lab repository by running the following command in the terminal: ```sh git clone https://github.com/kiratech/labs.git ``` -### 2. Fare il Checkout del Branch Lab +### 2. Checkout the Lab Branch -Dopo aver clonato la repository, entra nella cartella del progetto: +After cloning the repository, navigate to the project folder: ```sh cd labs ``` -Quindi, fai il checkout del branch `lab`: +Then, checkout the `lab` branch: ```sh git checkout academy-ai ``` -In questa cartella sono presenti le risorse relative ai laboratori con tema AI. +This folder contains resources related to AI-themed labs. -### 3. Spostarsi nella Cartella 1-Intro-To-ML-And-MLOps +### 3. Navigate to the 1-Intro-To-ML-And-MLOps Folder -Naviga nella cartella del primo laboratorio: +Go to the folder of the first lab: ```sh cd AI/Academy/1-Intro-To-ML-And-MLOps ``` -### 4. Apri il progetto in VSCode +### 4. Open the Project in VSCode -A questo punto apri VSCode dall'explorer oppure lanciando il comando: +At this point, open VSCode from the file explorer or by running the command: ```sh code . ``` -### 5. Creare un Virtual Environment +### 5. Create a Virtual Environment -Un ambiente virtuale permette di isolare le dipendenze del progetto da quelle di sistema. +A virtual environment allows you to isolate the project's dependencies from the system-wide ones. -Tramite il terminale di **VSCode**, crea un ambiente virtuale: +Using the **VSCode** terminal, create a virtual environment: ```sh conda create --name lab_env python=3.12 pip -y ``` -Attiva l'Ambiente Virtuale +Activate the Virtual Environment: ```sh conda activate lab_env ``` -Dovresti vedere il prefisso `(lab_env)` nel terminale, indicando che l'ambiente virtuale è attivo. +You should see the `(lab_env)` prefix in the terminal, indicating that the virtual environment is active. -### 6. Installare i Pacchetti Necessari +### 6. Install Required Packages -Oltre ai pacchetti presenti di default nell'environment, è possibile che servano librerie aggiuntive per il laboratorio. -Prima di installarle, è sempre buona pratica aggiornare `pip` per evitare problemi di compatibilità: +Besides the default packages in the environment, additional libraries may be needed for the lab. +Before installing them, it's always a good practice to update `pip` to avoid compatibility issues: ```sh pip install --upgrade pip ``` -Ora installiamo alcuni pacchetti fondamentali per l'analisi dei dati e il machine learning: +Now, install some essential packages for data analysis and machine learning: ```sh pip install scikit-learn pandas seaborn mlflow ipykernel ``` -- [**scikit-learn**](https://scikit-learn.org/stable/index.html): Libreria per il machine learning con strumenti di modellazione e valutazione. -- [**pandas**](https://pandas.pydata.org/): Framework per la manipolazione e l'analisi dei dati in Python. -- [**seaborn**](https://seaborn.pydata.org/): Libreria per la visualizzazione di dati basata su Matplotlib. -- [**mlflow**](https://mlflow.org/): Strumento per il tracciamento e la gestione di esperimenti di machine learning. -Verifica che i pacchetti siano stati installati correttamente con: +Verify that the packages were installed correctly with: ```sh conda list ``` -A questo punto si può continuare sul file `lab.ipynb`. \ No newline at end of file +At this point, you can proceed with the `lab.ipynb` file. \ No newline at end of file diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb index b5cfc00..e2352e5 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/lab.ipynb @@ -4,50 +4,50 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Lab: Modello ML di base con Dataset Meteo + Integrazione MLflow\n", + "# Lab: Basic ML Model with Weather Dataset + MLflow Integration\n", "\n", - "Benvenuto in questo laboratorio! Qui imparerai a:\n", + "Welcome to this lab! Here you will learn how to:\n", "\n", - "1. **Caricare e preparare un dataset meteo**, con dati di temperatura e umidità.\n", - "2. **Addestrare un modello di Machine Learning** con Scikit-learn per prevedere la pioggia.\n", - "3. **Valutare il modello** e comprendere i risultati.\n", - "4. **Integrare MLflow** per tracciare metriche, parametri e versioni del modello.\n", + "1. **Load and prepare a weather dataset**, with temperature and humidity data.\n", + "2. **Train a Machine Learning model** using Scikit-learn, a powerful tool for Machine Learning in Python, to predict rain.\n", + "3. **Evaluate the model** computing metrics to determine how well it makes predictions on new data.\n", + "4. **Integrate MLflow**, one of the most used tool to track metrics, parameters, and model versions.\n", "\n", - "Seguiremo un approccio guidato, con spiegazioni dettagliate ad ogni passaggio. \n", - "La prima parte si concentra su Scikit-learn e il dataset meteo. La seconda parte estende il codice esistente con MLflow." + "We will follow a guided approach with detailed explanations at each step. \n", + "The first part focuses on Scikit-learn and the weather dataset. The second part extends the existing code with MLflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "---\n" + "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Parte 1: Dai Dati al Modello di Machine Learning (Supervised Learning con Scikit-learn) \n", + "## Part 1: From Data to Machine Learning Model (Supervised Learning with Scikit-learn) \n", "\n", - "### Obiettivo \n", - "Costruire un **modello di classificazione** che possa prevedere se pioverà, utilizzando come input i dati di **temperatura** e **umidità**. Il modello verrà addestrato con **Scikit-learn**, uno strumento potente per il Machine Learning in Python." + "### Objective \n", + "Build a **classification model** that can predict whether it will rain, using **temperature** and **humidity** as input data. The model will be trained using **Scikit-learn**, a powerful tool for Machine Learning in Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1. Preparazione del Dataset \n", + "### 1. Preparing the Dataset \n", "\n", - "Prima di addestrare un modello di Machine Learning, è fondamentale pulire i dati, perché valori mancanti o errati possono compromettere le previsioni. Un dataset ben preparato permette al modello di imparare meglio e fornire risultati più accurati. \n", + "Before training a Machine Learning model, it is essential to clean the data, as missing or incorrect values can compromise predictions. A well-prepared dataset allows the model to learn better and provide more accurate results. \n", "\n", - "Per questo laboratorio utilizzeremo un dataset di esempio: \n", + "For this lab, we will use an example dataset: \n", "[Weather Test Data](https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv) \n", "\n", - "Il dataset **Weather Test Data** contiene informazioni meteorologiche raccolte in diversi momenti. Ogni riga rappresenta un'osservazione con parametri come **temperatura**, **umidità**, **pressione atmosferica** e altre variabili meteo. \n", + "The **Weather Test Data** dataset contains meteorological information collected at different times. Each row represents an observation with parameters such as **temperature**, **humidity**, **atmospheric pressure**, and other weather variables. \n", "\n", - "L'obiettivo di questo dataset è analizzare i pattern climatici e utilizzarli per addestrare un modello di Machine Learning in grado di prevedere condizioni future, come la probabilità di pioggia o variazioni di temperatura." + "The goal of this dataset is to analyze weather patterns and use them to train a Machine Learning model capable of predicting future conditions, such as the probability of rain or temperature variations." ] }, { @@ -58,13 +58,13 @@ "source": [ "import pandas as pd\n", "\n", - "# URL del dataset \n", + "# Dataset URL \n", "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", "\n", - "# Caricamento del dataset in un dataframe Pandas\n", + "# Load the dataset in a Pandas dataframe\n", "df = pd.read_csv(url)\n", "\n", - "# Mostra le prime righe\n", + "# Show first 5 rows\n", "df.head(5)\n", "\n" ] @@ -73,13 +73,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Esplorazione e Pulizia dei Dati \n", + "### 2. Data Exploration and Cleaning \n", "\n", - "Per garantire che il nostro modello funzioni correttamente, dobbiamo prima esaminare e preparare il dataset. Ecco i passaggi fondamentali: \n", + "To ensure our model works correctly, we first need to examine and prepare the dataset. Here are the key steps: \n", "\n", - "1. **Controllo dei dati mancanti**: verifichiamo se ci sono valori assenti, poiché potrebbero compromettere l'addestramento del modello. Se necessario, possiamo eliminarli o sostituirli con valori appropriati. \n", - "2. **Conversione della colonna `Label`**: trasformiamo le categorie testuali (*NoRain* e *Rain*) in valori numerici (0 per *NoRain*, 1 per *Rain*), in modo che il modello possa interpretarli correttamente. \n", - "3. **Selezione delle feature principali**: scegliamo solo le colonne più rilevanti (es. temperatura e umidità) per semplificare il modello e migliorare le sue prestazioni." + "1. **Check for missing data**: Identify if there are any missing values, as they could compromise model training. If necessary, we can remove them or replace them with appropriate values. \n", + "2. **Convert the `Label` column**: Transform categorical values (*NoRain* and *Rain*) into numerical values (0 for *NoRain*, 1 for *Rain*), so the model can interpret them correctly. \n", + "3. **Select key features**: Choose only the most relevant columns (e.g., temperature and humidity) to simplify the model and improve its performance.\n" ] }, { @@ -88,13 +88,13 @@ "metadata": {}, "outputs": [], "source": [ - "# 1. Rimozione dei valori mancanti\n", + "# 1. Remove missing values\n", "df = df.dropna()\n", "\n", - "# 2. Conversione della colonna 'RainToday' in valori numerici\n", + "# 2. Transform column 'RainToday' into numerical values\n", "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", "\n", - "# 3. Selezione delle feature \n", + "# 3. Feature selection \n", "features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am']\n", "\n", "X = df[features]\n", @@ -105,27 +105,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Suddivisione del Dataset in Training e Test \n", + "### 3. Splitting the Dataset into Training and Test Sets \n", "\n", - "Per addestrare e valutare correttamente il modello, dividiamo il dataset in due parti: \n", + "To properly train and evaluate the model, we split the dataset into two parts: \n", "\n", - "- **X (feature)**: contiene le informazioni che useremo per fare previsioni, come **temperatura** e **umidità**. \n", - "- **y (target)**: rappresenta la variabile che vogliamo prevedere, ovvero se pioverà (*Rain*) o meno (*NoRain*). \n", + "- **X (features)**: Contains the information we will use for predictions, such as **temperature** and **humidity**. \n", + "- **y (target)**: Represents the variable we want to predict, i.e., whether it will rain (*Rain*) or not (*NoRain*). \n", "\n", - "Dividiamo i dati in **training set** (80%) e **test set** (20%) per i seguenti motivi: \n", + "We split the data into **80% training set** and **20% test set** for the following reasons: \n", "\n", - "1. **Addestramento del modello** \n", - " - L'80% dei dati viene usato per insegnare al modello a riconoscere i pattern tra le feature e il target. \n", + "1. **Model Training** \n", + " - 80% of the data is used to teach the model to recognize patterns between features and the target variable. \n", "\n", - "2. **Valutazione del modello** \n", - " - Il restante 20% dei dati non viene usato nell'addestramento, ma serve per testare il modello su dati mai visti prima. \n", - " - Questo ci permette di capire se il modello è davvero in grado di fare previsioni accurate su nuovi dati. \n", + "2. **Model Evaluation** \n", + " - The remaining 20% of the data is not used in training but serves to test the model on unseen data. \n", + " - This helps us understand whether the model can make accurate predictions on new data. \n", "\n", - "3. **Evitare overfitting** \n", - " - Se testassimo il modello sugli stessi dati con cui è stato addestrato, potremmo ottenere risultati ingannevolmente buoni, perché il modello li avrebbe semplicemente memorizzati. \n", - " - Usare dati di test separati aiuta a verificare se il modello può generalizzare le sue previsioni a nuovi dati reali. \n", + "3. **Avoid Overfitting** \n", + " - If we tested the model on the same data it was trained on, we might get deceptively good results, as the model would have simply memorized them. \n", + " - Using separate test data helps verify whether the model can generalize its predictions to real-world data. \n", "\n", - "Questa suddivisione è un passaggio fondamentale per costruire un modello affidabile e capace di fare previsioni corrette su dati che non ha mai visto prima." + "This split is a crucial step in building a reliable model capable of making accurate predictions on unseen data." ] }, { @@ -136,26 +136,26 @@ "source": [ "from sklearn.model_selection import train_test_split\n", "\n", - "# Suddivisione dei dati (80% training, 20% testing)\n", + "# Dataset split (80% training, 20% testing)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "print(f\"Dimensioni del dataset di training: {len(X_train)}\")\n", - "print(f\"Dimensioni del dataset di test: {len(X_test)}\")" + "print(f\"Training dataset dimensions: {len(X_train)}\")\n", + "print(f\"Test dataset dimensions: {len(X_test)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Creazione e Addestramento del Modello \n", + "### 4. Building and Training the Model \n", "\n", - "Ora che abbiamo preparato i dati, possiamo costruire e addestrare un modello di Machine Learning. Per questo utilizzeremo un classificatore chiamato [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), una delle tecniche più utilizzate per problemi di classificazione. \n", + "Now that we have prepared the data, we can build and train a Machine Learning model. For this, we will use a classifier called [**RandomForestClassifier**](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html), one of the most widely used techniques for classification problems. \n", "\n", - "#### Perché usiamo **Random Forest**? \n", - "- È un modello basato su **alberi decisionali**, che suddivide i dati in più passaggi per prendere decisioni accurate. \n", - "- È **robusto** e gestisce bene sia dati numerici che categoriali. \n", - "- È meno sensibile ai dati rumorosi rispetto a un singolo albero decisionale, perché combina più alberi per migliorare la precisione. \n", + "#### Why use **Random Forest**? \n", + "- It is a model based on **decision trees**, which divide the data into multiple steps to make accurate decisions. \n", + "- It is **robust** and works well with both numerical and categorical data. \n", + "- It is less sensitive to noisy data than a single decision tree because it combines multiple trees to improve accuracy. \n", "\n", - "Il modello verrà addestrato utilizzando i dati di **temperatura** e **umidità** per prevedere se ci sarà **pioggia** o meno. Dopo l'addestramento, lo testeremo su dati nuovi per valutarne l'accuratezza." + "The model will be trained using **temperature** and **humidity** data to predict whether there will be **rain** or not. After training, we will test it on new data to evaluate its accuracy." ] }, { @@ -166,7 +166,7 @@ "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", - "# Creazione e addestramento del modello\n", + "# Create and train the model\n", "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", "model.fit(X_train, y_train)" ] @@ -175,21 +175,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 5. Valutazione del Modello \n", + "### 5. Model Evaluation \n", "\n", - "Dopo aver addestrato il modello, dobbiamo verificare quanto è accurato nelle previsioni. Per farlo, calcoliamo **l'accuratezza** e altre metriche di valutazione. \n", + "After training the model, we need to verify how accurate its predictions are. To do this, we will calculate **accuracy** and other evaluation metrics. \n", "\n", - "#### Perché è importante valutare il modello? \n", - "Un modello di Machine Learning non è utile se non sappiamo quanto sia affidabile. La valutazione ci aiuta a capire: \n", - "- **Se il modello sta imparando correttamente dai dati** o se sta solo memorizzando le risposte (overfitting). \n", - "- **Se può essere utilizzato su dati nuovi** e fare previsioni realistiche. \n", + "#### Why is model evaluation important? \n", + "A Machine Learning model is not useful if we do not know how reliable it is. Evaluation helps us understand: \n", + "- **Whether the model is learning correctly from the data** or simply memorizing answers (overfitting). \n", + "- **Whether it can be used on new data** and make realistic predictions. \n", "\n", - "#### Matrice di Confusione \n", - "Oltre all'accuratezza, utilizzeremo la **matrice di confusione**, un metodo visivo che mostra dove il modello fa previsioni corrette e dove sbaglia. \n", - "- Ci aiuta a individuare **falsi positivi** e **falsi negativi**, che sono errori critici in molti scenari reali. \n", - "- È utile per migliorare il modello, ad esempio regolando soglie di decisione o bilanciando i dati di input. \n", + "#### Confusion Matrix \n", + "Besides accuracy, we will use the **confusion matrix**, a visual method that shows where the model makes correct predictions and where it makes mistakes. \n", + "- It helps identify **false positives** and **false negatives**, which are critical errors in many real-world scenarios. \n", + "- It is useful for improving the model, for example, by adjusting decision thresholds or balancing input data. \n", "\n", - "Con queste analisi possiamo capire se il nostro modello è pronto per essere utilizzato o se necessita di miglioramenti." + "With these analyses, we can determine whether our model is ready for use or needs improvement." ] }, { @@ -200,16 +200,16 @@ "source": [ "from sklearn.metrics import accuracy_score, f1_score, classification_report\n", "\n", - "# Predizioni sul set di test\n", + "# Prediction on test set\n", "y_pred = model.predict(X_test)\n", "\n", - "# Calcolo dell'accuratezza ed f1-score\n", + "# Compute accuracy and f1-score\n", "accuracy = accuracy_score(y_test, y_pred)\n", "f1 = f1_score(y_test, y_pred, average='weighted')\n", - "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", - "print(f\"F1-score del modello: {f1:.2f}\")\n", + "print(f\"Accuracy of the model: {accuracy:.2f}\")\n", + "print(f\"F1-score of the model: {f1:.2f}\")\n", "\n", - "# Report di classificazione\n", + "# Print classification report\n", "print(classification_report(y_test, y_pred))" ] }, @@ -223,18 +223,18 @@ "import matplotlib.pyplot as plt\n", "from sklearn.metrics import confusion_matrix\n", "\n", - "# Creiamo una heatmap con Seaborn\n", + "# Create a heatmap with Seaborn\n", "target_names = [\"No Rain\", \"Rain\"]\n", "cm = confusion_matrix(y_test, y_pred)\n", "plt.figure(figsize=(6, 4))\n", "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", "\n", - "# Aggiungiamo i titoli\n", + "# Add titles\n", "plt.xlabel(\"Predicted Label\")\n", "plt.ylabel(\"True Label\")\n", "plt.title(\"Confusion Matrix\")\n", "\n", - "# Mostriamo il grafico\n", + "# Show the plot\n", "plt.show()" ] }, @@ -242,17 +242,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Conclusione (Parte 1) \n", + "### Conclusion (Part 1) \n", "\n", - "In questa prima parte, abbiamo seguito un processo step-by-step per costruire un modello di Machine Learning in grado di prevedere la pioggia. Ecco cosa abbiamo fatto: \n", + "In this first part, we followed a step-by-step process to build a Machine Learning model capable of predicting rain. Here’s what we did: \n", "\n", - "1. **Caricato il dataset meteo** per analizzare temperatura, umidità e altre variabili. \n", - "2. **Pulito e preparato i dati**, gestendo eventuali valori mancanti e trasformando la variabile target in un formato comprensibile al modello. \n", - "3. **Suddiviso il dataset** in dati di training (80%) e di test (20%) per addestrare e valutare il modello correttamente. \n", - "4. **Creato un modello di classificazione** utilizzando **RandomForestClassifier**, un algoritmo potente e robusto. \n", - "5. **Valutato le prestazioni del modello** calcolando l’accuratezza e analizzando la matrice di confusione per identificare eventuali errori. \n", + "1. **Loaded the weather dataset** to analyze temperature, humidity, and other variables. \n", + "2. **Cleaned and prepared the data**, handling missing values and converting the target variable into a format the model can understand. \n", + "3. **Split the dataset** into training (80%) and test (20%) sets to properly train and evaluate the model. \n", + "4. **Built a classification model** using **RandomForestClassifier**, a powerful and robust algorithm. \n", + "5. **Evaluated the model’s performance** by calculating accuracy and analyzing the confusion matrix to identify errors. \n", "\n", - "Ora che abbiamo costruito il modello base, nella prossima parte esploreremo come integrare **MLflow** per tracciare gli esperimenti e migliorare ulteriormente le prestazioni." + "Now that we have built the base model, in the next part, we will explore how to integrate **MLflow** to track experiments and further improve performance." ] }, { @@ -266,55 +266,55 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tech: Installazione e Configurazione di MLflow \n", + "## Tech: Installing and Configuring MLflow \n", "\n", - "### Obiettivo \n", - "Configurare un'istanza locale di **MLflow** per registrare esperimenti, monitorare le metriche e gestire i modelli di Machine Learning in modo organizzato. \n", + "### Objective \n", + "Set up a local **MLflow** instance to log experiments, monitor metrics, and manage Machine Learning models in an organized way. \n", "\n", - "### 1. Avviare MLflow \n", + "### 1. Starting MLflow \n", "\n", - "Per avviare MLflow in locale, esegui il seguente comando nel terminale: \n", + "To start MLflow locally, run the following command in the terminal: \n", "\n", "```bash\n", " mlflow ui\n", "```\n", "\n", - "Dopo averlo avviato, l'interfaccia grafica sarà accessibile all'indirizzo: \n", + "Once started, the graphical interface will be accessible at: \n", "\n", "```\n", " http://127.0.0.1:5000\n", "```\n", "\n", - "Questa configurazione permette di **salvare esperimenti e modelli localmente**, consentendo di tracciare le diverse versioni dei modelli, confrontare le metriche di valutazione e ottimizzare i processi di addestramento. \n", + "This setup allows **saving experiments and models locally**, enabling tracking of different model versions, comparing evaluation metrics, and optimizing training processes. \n", "\n", - "Nelle prossime sezioni, vedremo come registrare parametri, metriche e modelli direttamente all'interno di MLflow." + "In the next sections, we will see how to log parameters, metrics, and models directly within MLflow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### MLflow in Produzione \n", + "### MLflow in Production \n", "\n", - "In ambienti di produzione e presso i clienti, **MLflow non viene eseguito in locale**, ma viene integrato in un'infrastruttura più solida e scalabile. Questo evita problemi legati alla gestione manuale degli esperimenti e alla persistenza dei dati. \n", + "In production environments and for clients, **MLflow is not run locally** but is integrated into a more robust and scalable infrastructure. This prevents issues related to manual experiment management and data persistence. \n", "\n", - "Le soluzioni più comuni includono: \n", + "Common solutions include: \n", "\n", "- **Docker Compose** \n", - " - MLflow viene avviato utilizzando un file `docker-compose.yml`, che configura un database backend e uno storage remoto per salvare gli esperimenti. \n", - " - Questo approccio è utile per ambienti controllati, dove è necessario un setup rapido e replicabile. \n", - " - Un esempio di implementazione è disponibile nella repository interna: \n", + " - MLflow is started using a `docker-compose.yml` file, which configures a backend database and remote storage for saving experiments. \n", + " - This approach is useful for controlled environments where a quick and reproducible setup is needed. \n", + " - An example implementation is available in the internal repository: \n", " [kiratech/mlops-service-portfolio](https://github.com/kiratech/mlops-service-portfolio/tree/main). \n", "\n", "- **Kubernetes (K8s)** \n", - " - MLflow viene distribuito su un **cluster Kubernetes**, consentendo una gestione scalabile e centralizzata degli esperimenti. \n", - " - Questo approccio è ideale per ambienti enterprise, dove sono richiesti elevati livelli di affidabilità, sicurezza e scalabilità. \n", + " - MLflow is deployed on a **Kubernetes cluster**, allowing scalable and centralized experiment management. \n", + " - This approach is ideal for enterprise environments that require high levels of reliability, security, and scalability. \n", "\n", - "Entrambe le soluzioni si basano su un'**architettura multi-container**, che include: \n", - "- **Un database persistente** (es. PostgreSQL o MySQL) per memorizzare i metadati degli esperimenti. \n", - "- **Uno storage S3 o MinIO** per salvare i modelli e gli artefatti, garantendo una gestione sicura e scalabile dei dati. \n", + "Both solutions rely on a **multi-container architecture**, which includes: \n", + "- **A persistent database** (e.g., PostgreSQL or MySQL) to store experiment metadata. \n", + "- **An S3 or MinIO storage** to save models and artifacts, ensuring secure and scalable data management. \n", "\n", - "Questi approcci assicurano che MLflow possa essere utilizzato in modo affidabile in produzione, integrandosi con infrastrutture cloud o on-premise per una gestione efficace dei modelli di Machine Learning." + "These approaches ensure that MLflow can be reliably used in production, integrating with cloud or on-premise infrastructures for effective Machine Learning model management." ] }, { @@ -328,21 +328,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Parte 2: Integrazione con MLflow \n", + "## Part 2: MLflow Integration \n", "\n", - "Ora estenderemo il codice esistente per **tracciare i nostri esperimenti** utilizzando **MLflow**. Questo ci permetterà di monitorare il processo di addestramento del modello, confrontare le diverse configurazioni e gestire le versioni del modello in modo strutturato. \n", + "Now, we will extend the existing code to **track our experiments** using **MLflow**. This will allow us to monitor the model training process, compare different configurations, and manage model versions in a structured way. \n", "\n", - "### Perché integrare MLflow? \n", - "Con MLflow possiamo: \n", - "- **Registrare i parametri di addestramento** (es. `n_estimators` per Random Forest) per confrontare diverse configurazioni. \n", - "- **Salvare le metriche di valutazione** (es. accuratezza, F1-score) per monitorare le prestazioni del modello. \n", - "- **Archiviare il modello addestrato** per poterlo ricaricare facilmente in futuro e riutilizzarlo senza doverlo riaddestrare. \n", + "### Why integrate MLflow? \n", + "With MLflow, we can: \n", + "- **Log training parameters** (e.g., `n_estimators` for Random Forest) to compare different configurations. \n", + "- **Save evaluation metrics** (e.g., accuracy, F1-score) to monitor the model’s performance. \n", + "- **Store the trained model** to easily reload and reuse it in the future without retraining. \n", "\n", - "### Obiettivo \n", - "Integrare MLflow nel codice esistente per **tracciare e versionare i modelli**, registrando parametri, metriche e artefatti in modo strutturato. \n", + "### Objective \n", + "Integrate MLflow into the existing code to **track and version models**, logging parameters, metrics, and artifacts in a structured way. \n", "\n", - "### 1. Configurazione di MLflow \n", - "Prima di iniziare a tracciare gli esperimenti, impostiamo le variabili necessarie per utilizzare MLflow in questo progetto." + "### 1. MLflow Configuration \n", + "Before we start tracking experiments, let's set up the necessary variables to use MLflow in this project. " ] }, { @@ -354,7 +354,7 @@ "import mlflow\n", "import mlflow.sklearn\n", "\n", - "# Impostiamo un nome per l'esperimento\n", + "# Set name of the experiment and tracking URI of local instance\n", "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n", "mlflow.set_experiment(\"weather_classification_experiment\")" ] @@ -363,24 +363,24 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Verifichiamo che dalla [nostra interfaccia grafica](http://127.0.0.1:5000) il nuovo esperimento sia visibile." + "We verify that from our [web interface](http://127.0.0.1:5000), the new experiment is visible." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 2. Registrazione di Parametri, Metriche e Modello \n", + "### 2. Logging Parameters, Metrics, and Model \n", "\n", - "Con **MLflow**, possiamo salvare e tracciare automaticamente diverse informazioni durante l'addestramento del modello. Questo ci aiuta a confrontare le performance tra diverse configurazioni e a recuperare facilmente i modelli migliori. \n", + "With **MLflow**, we can automatically save and track various information during model training. This helps compare performances across different configurations and easily retrieve the best models. \n", "\n", - "Ecco cosa possiamo registrare: \n", + "Here’s what we can log: \n", "\n", - "- **Parametri** → Valori utilizzati per configurare il modello, come `n_estimators` (numero di alberi in Random Forest) e altri iperparametri. \n", - "- **Metriche** → Indicatori delle prestazioni del modello, come **accuratezza**, **F1-score**, precisione e recall. \n", - "- **Modello** → La versione del modello addestrato, che potrà essere ricaricata e riutilizzata senza bisogno di riaddestramento. \n", + "- **Parameters** → Values used to configure the model, such as `n_estimators` (number of trees in Random Forest) and other hyperparameters. \n", + "- **Metrics** → Performance indicators of the model, such as **accuracy**, **F1-score**, precision, and recall. \n", + "- **Model** → The trained model version, which can be reloaded and reused without retraining. \n", "\n", - "Registrando questi elementi, possiamo analizzare e confrontare le diverse versioni del modello in modo strutturato e riproducibile." + "By logging these elements, we can analyze and compare different model versions in a structured and reproducible way." ] }, { @@ -389,97 +389,97 @@ "metadata": {}, "outputs": [], "source": [ - "# Usando il modello creato nella Parte 1\n", + "# Use the same type of model used in Part 1\n", "import os\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import accuracy_score, f1_score\n", "from mlflow.models.signature import infer_signature\n", "\n", - "# Eseguiremo 4 esperimenti per addestrare più modelli \n", + "# Execute 4 experiments to train multiple models \n", "n_estimators = [1, 10, 100, 500]\n", "\n", "for n_e in n_estimators:\n", - " # Creiamo una nuova run MLflow\n", + " # Create a new MLFlow run\n", " with mlflow.start_run():\n", " # Log Param\n", " mlflow.log_param(\"n_estimators\", n_e)\n", "\n", - " # Creazione del modello\n", + " # Create and train the model instance\n", " rf_model = RandomForestClassifier(n_estimators=n_e, random_state=42)\n", " rf_model.fit(X_train, y_train)\n", "\n", - " # Calcolo metriche\n", + " # Compute metrics\n", " y_pred = rf_model.predict(X_test)\n", " accuracy = accuracy_score(y_test, y_pred)\n", " f1 = f1_score(y_test, y_pred, average='weighted')\n", " mlflow.log_metric(\"accuracy\", accuracy)\n", " mlflow.log_metric(\"f1\", f1)\n", "\n", - " # Creiamo una heatmap con Seaborn\n", + " # Create heatmap with Seaborn\n", " target_names = [\"No Rain\", \"Rain\"]\n", " cm = confusion_matrix(y_test, y_pred)\n", " plt.figure(figsize=(6, 4))\n", " sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", "\n", - " # Aggiungiamo i titoli\n", + " # Add titles\n", " plt.xlabel(\"Predicted Label\")\n", " plt.ylabel(\"True Label\")\n", " plt.title(\"Confusion Matrix\")\n", "\n", - " # Salviamo l'immagine come PNG\n", + " # Save the plot as PNG\n", " if not os.path.exists(\"dev/\"):\n", " os.makedirs(\"dev/\")\n", " plt.savefig(\"dev/confusion_matrix.png\")\n", " plt.close()\n", - " # Salviamo la confusion matrix su MLFlow come artifatto\n", + " # Save the confusion matrix on MLFlow as artifact\n", " mlflow.log_artifact(\"dev/confusion_matrix.png\")\n", "\n", - " # Salviamo il modello\n", + " # Save the model on MLFlow\n", " example_dict = {'MinTemp': 1.1, 'MaxTemp': 1.1, 'Humidity3pm': 1.1, 'Humidity9am': 1.1}\n", " signature = infer_signature(model_input=example_dict)\n", " mlflow.sklearn.log_model(rf_model, \"random_forest_model\", signature=signature)\n", "\n", - " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" + " print(f\"Experiment finished. Registered accuracy: {accuracy:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3. Visualizzazione e Confronto dei Risultati \n", + "### 3. Viewing and Comparing Results \n", "\n", - "Dopo aver registrato i parametri, le metriche e i modelli, possiamo utilizzare **MLflow** per esplorare e confrontare i diversi esperimenti. \n", + "After logging parameters, metrics, and models, we can use **MLflow** to explore and compare different experiments. \n", "\n", - "MLflow fornisce un’interfaccia web accessibile all’indirizzo: \n", + "MLflow provides a web interface accessible at: \n", "\n", "```bash\n", " http://127.0.0.1:5000\n", "```\n", "\n", - "Accedendo a questa interfaccia, nella sezione **Experiments**, sarà possibile: \n", - "- **Esaminare i parametri** utilizzati in ogni esperimento. \n", - "- **Confrontare le metriche** tra diverse configurazioni di modello. \n", - "- **Visualizzare e scaricare i modelli salvati**, facilitando il riutilizzo e il deployment. \n", + "By accessing this interface, in the **Experiments** section, it will be possible to: \n", + "- **Examine the parameters** used in each experiment. \n", + "- **Compare metrics** across different model configurations. \n", + "- **View and download saved models**, making reuse and deployment easier. \n", "\n", - "Questa funzionalità consente di monitorare l’evoluzione delle performance del modello e di identificare rapidamente le configurazioni migliori." + "This feature allows monitoring the model's performance evolution and quickly identifying the best configurations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4. Caricare un Modello Salvato con MLflow \n", + "### 4. Loading a Saved Model with MLflow \n", "\n", - "MLflow consente di salvare e ricaricare facilmente i modelli addestrati, evitando la necessità di riaddestrarli ogni volta. \n", + "MLflow allows saving and reloading trained models easily, avoiding the need to retrain them every time. \n", "\n", - "Per recuperare un modello salvato in MLflow, è necessario copiare **l'ID della run** dell'esperimento eseguito. Questo ID identifica in modo univoco ogni esperimento registrato e permette di caricare il modello corrispondente per effettuare previsioni future. \n", + "To retrieve a saved model in MLflow, you need to copy the **run ID** of the executed experiment. This ID uniquely identifies each logged experiment and allows loading the corresponding model for future predictions. \n", "\n", - "Questa funzionalità è particolarmente utile per: \n", - "- **Riutilizzare un modello addestrato** senza dover ripetere il training. \n", - "- **Confrontare diverse versioni** di un modello per scegliere quella più performante. \n", - "- **Integrare il modello in applicazioni o API**, senza doverlo ricostruire da zero. \n", + "This feature is particularly useful for: \n", + "- **Reusing a trained model** without repeating the training process. \n", + "- **Comparing different model versions** to choose the most effective one. \n", + "- **Integrating the model into applications or APIs**, without rebuilding it from scratch. \n", "\n", - "Nelle prossime sezioni vedremo come eseguire questo processo in pratica." + "In the next sections, we will see how to perform this process in practice." ] }, { @@ -491,45 +491,45 @@ "import mlflow.sklearn\n", "from sklearn.metrics import accuracy_score\n", "\n", - "# Inserisci un run_id reale che trovi nell'interfaccia MLflow\n", - "RUN_ID = \"\"\n", + "# Insert a real run_id you find in MLFlow UI\n", + "RUN_ID = \"\"\n", "\n", "loaded_model = mlflow.sklearn.load_model(f\"runs:/{RUN_ID}/random_forest_model\")\n", "\n", - "# Verifichiamo l'accuratezza\n", + "# Verify accuracy\n", "y_loaded_pred = loaded_model.predict(X_test)\n", "acc_loaded = accuracy_score(y_test, y_loaded_pred)\n", - "print(f\"Accuratezza del modello caricato: {acc_loaded:.2f}\")" + "print(f\"Accuracy of the loaded model: {acc_loaded:.2f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Conclusioni \n", + "## Conclusions \n", "\n", - "In questo laboratorio, abbiamo seguito un processo completo per costruire e monitorare un modello di Machine Learning applicato ai dati meteorologici. In particolare, abbiamo: \n", + "In this lab, we followed a complete process to build and monitor a Machine Learning model applied to weather data. Specifically, we: \n", "\n", - "1. **Creato un modello di classificazione** utilizzando **Scikit-learn**, sfruttando temperatura e umidità per prevedere la pioggia. \n", - "2. **Integrato MLflow** per tracciare i parametri di addestramento, registrare le metriche di valutazione e gestire le versioni del modello in modo strutturato. \n", - "3. **Esplorato e confrontato i risultati** attraverso l’interfaccia di MLflow UI, verificando le diverse configurazioni e caricando un modello salvato per future previsioni. \n", + "1. **Created a classification model** using **Scikit-learn**, leveraging temperature and humidity to predict rain. \n", + "2. **Integrated MLflow** to track training parameters, log evaluation metrics, and manage model versions in a structured way. \n", + "3. **Explored and compared results** through the MLflow UI interface, reviewing different configurations and loading a saved model for future predictions. \n", "\n", - "Questo approccio ci permette di migliorare il processo di sviluppo dei modelli di Machine Learning, rendendolo più organizzato, riproducibile e scalabile." + "This approach allows us to improve the Machine Learning model development process, making it more organized, reproducible, and scalable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Prossimi Passi \n", + "## Next Steps \n", "\n", - "Ora che abbiamo costruito e tracciato il nostro modello, possiamo esplorare ulteriori miglioramenti e integrare il lavoro in un flusso più avanzato. \n", + "Now that we have built and tracked our model, we can explore further improvements and integrate the work into a more advanced workflow. \n", "\n", - "- **Ottimizzazione degli iperparametri**: testare diverse configurazioni di `n_estimators`, `max_depth` e altri parametri del modello, registrando i risultati in **MLflow** per identificare la combinazione migliore. \n", - "- **Automazione con CI/CD**: integrare un sistema di **Continuous Integration/Continuous Deployment (CI/CD)** per addestrare e distribuire automaticamente nuove versioni del modello, riducendo il rischio di errori manuali. \n", - "- **Monitoraggio del modello in produzione**: implementare un sistema di **monitoraggio del drift del modello**, per rilevare eventuali cali di accuratezza nel tempo e decidere quando è necessario riaddestrarlo con nuovi dati. \n", + "- **Hyperparameter Optimization**: Test different configurations of `n_estimators`, `max_depth`, and other model parameters, logging results in **MLflow** to identify the best combination. \n", + "- **Automation with CI/CD**: Integrate a **Continuous Integration/Continuous Deployment (CI/CD)** system to automatically train and deploy new model versions, reducing the risk of manual errors. \n", + "- **Model Monitoring in Production**: Implement a **model drift monitoring** system to detect any drops in accuracy over time and determine when retraining with new data is necessary. \n", "\n", - "Questi passaggi aiutano a trasformare il modello sviluppato in un sistema robusto e affidabile, pronto per essere utilizzato in scenari reali." + "These steps help transform the developed model into a robust and reliable system, ready for real-world applications." ] } ], diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt b/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt deleted file mode 100644 index 2580f85..0000000 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/src/requirements.txt +++ /dev/null @@ -1,153 +0,0 @@ -alembic==1.14.1 -annotated-types==0.7.0 -anyio==4.8.0 -appnope==0.1.4 -argon2-cffi==23.1.0 -argon2-cffi-bindings==21.2.0 -arrow==1.3.0 -asttokens==3.0.0 -async-lru==2.0.4 -attrs==25.1.0 -babel==2.17.0 -beautifulsoup4==4.13.3 -bleach==6.2.0 -blinker==1.9.0 -cachetools==5.5.2 -certifi==2025.1.31 -cffi==1.17.1 -charset-normalizer==3.4.1 -click==8.1.8 -cloudpickle==3.1.1 -comm==0.2.2 -contourpy==1.3.1 -cycler==0.12.1 -databricks-sdk==0.44.1 -debugpy==1.8.12 -decorator==5.2.1 -defusedxml==0.7.1 -Deprecated==1.2.18 -docker==7.1.0 -exceptiongroup==1.2.2 -executing==2.2.0 -fastjsonschema==2.21.1 -Flask==3.1.0 -fonttools==4.56.0 -fqdn==1.5.1 -gitdb==4.0.12 -GitPython==3.1.44 -google-auth==2.38.0 -graphene==3.4.3 -graphql-core==3.2.6 -graphql-relay==3.2.0 -gunicorn==23.0.0 -h11==0.14.0 -httpcore==1.0.7 -httpx==0.28.1 -idna==3.10 -importlib_metadata==8.5.0 -ipykernel==6.29.5 -ipython==8.33.0 -ipywidgets==8.1.5 -isoduration==20.11.0 -itsdangerous==2.2.0 -jedi==0.19.2 -Jinja2==3.1.5 -joblib==1.4.2 -json5==0.10.0 -jsonpointer==3.0.0 -jsonschema==4.23.0 -jsonschema-specifications==2024.10.1 -jupyter==1.1.1 -jupyter-console==6.6.3 -jupyter-events==0.12.0 -jupyter-lsp==2.2.5 -jupyter_client==8.6.3 -jupyter_core==5.7.2 -jupyter_server==2.15.0 -jupyter_server_terminals==0.5.3 -jupyterlab==4.3.5 -jupyterlab_pygments==0.3.0 -jupyterlab_server==2.27.3 -jupyterlab_widgets==3.0.13 -kiwisolver==1.4.8 -Mako==1.3.9 -Markdown==3.7 -MarkupSafe==3.0.2 -matplotlib==3.10.1 -matplotlib-inline==0.1.7 -mistune==3.1.2 -mlflow==2.20.3 -mlflow-skinny==2.20.3 -nbclient==0.10.2 -nbconvert==7.16.6 -nbformat==5.10.4 -nest-asyncio==1.6.0 -notebook==7.3.2 -notebook_shim==0.2.4 -numpy==2.2.3 -opentelemetry-api==1.30.0 -opentelemetry-sdk==1.30.0 -opentelemetry-semantic-conventions==0.51b0 -overrides==7.7.0 -packaging==24.2 -pandas==2.2.3 -pandocfilters==1.5.1 -parso==0.8.4 -pexpect==4.9.0 -pillow==11.1.0 -platformdirs==4.3.6 -prometheus_client==0.21.1 -prompt_toolkit==3.0.50 -protobuf==5.29.3 -psutil==7.0.0 -ptyprocess==0.7.0 -pure_eval==0.2.3 -pyarrow==19.0.1 -pyasn1==0.6.1 -pyasn1_modules==0.4.1 -pycparser==2.22 -pydantic==2.10.6 -pydantic_core==2.27.2 -Pygments==2.19.1 -pyparsing==3.2.1 -python-dateutil==2.9.0.post0 -python-json-logger==3.2.1 -pytz==2025.1 -PyYAML==6.0.2 -pyzmq==26.2.1 -referencing==0.36.2 -requests==2.32.3 -rfc3339-validator==0.1.4 -rfc3986-validator==0.1.1 -rpds-py==0.23.1 -rsa==4.9 -scikit-learn==1.6.1 -scipy==1.15.2 -seaborn==0.13.2 -Send2Trash==1.8.3 -six==1.17.0 -smmap==5.0.2 -sniffio==1.3.1 -soupsieve==2.6 -SQLAlchemy==2.0.38 -sqlparse==0.5.3 -stack-data==0.6.3 -terminado==0.18.1 -threadpoolctl==3.5.0 -tinycss2==1.4.0 -tomli==2.2.1 -tornado==6.4.2 -traitlets==5.14.3 -types-python-dateutil==2.9.0.20241206 -typing_extensions==4.12.2 -tzdata==2025.1 -uri-template==1.3.0 -urllib3==2.3.0 -wcwidth==0.2.13 -webcolors==24.11.1 -webencodings==0.5.1 -websocket-client==1.8.0 -Werkzeug==3.1.3 -widgetsnbextension==4.0.13 -wrapt==1.17.2 -zipp==3.21.0 diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb b/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb deleted file mode 100644 index 848696a..0000000 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/src/solution.ipynb +++ /dev/null @@ -1,154 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# URL del dataset (sostituiscilo con il tuo link)\n", - "url = \"https://raw.githubusercontent.com/boradpreet/Weather_dataset/refs/heads/master/Weather%20Test%20Data.csv\"\n", - "# Carica il dataset\n", - "df = pd.read_csv(url)\n", - "\n", - "# 1. Rimozione dei valori mancanti\n", - "df = df.dropna()\n", - "# 2. Conversione della colonna 'RainToday' in valori numerici\n", - "df['RainToday'] = df['RainToday'].apply(lambda x: 1 if x == 'Yes' else 0)\n", - "# 3. Selezione delle feature \n", - "features = ['MinTemp', 'MaxTemp', 'Humidity3pm', 'Humidity9am']\n", - "X = df[features]\n", - "y = df['RainToday']\n", - "\n", - "from sklearn.model_selection import train_test_split\n", - "# Suddivisione dei dati (80% training, 20% testing)\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "print(f\"Dimensioni del dataset di training: {len(X_train)}\")\n", - "print(f\"Dimensioni del dataset di test: {len(X_test)}\")\n", - "\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "# Creazione e addestramento del modello\n", - "model = RandomForestClassifier(n_estimators=100, random_state=42)\n", - "model.fit(X_train, y_train)\n", - "\n", - "from sklearn.metrics import accuracy_score, classification_report\n", - "# Predizioni sul set di test\n", - "y_pred = model.predict(X_test)\n", - "# Calcolo dell'accuratezza\n", - "accuracy = accuracy_score(y_test, y_pred)\n", - "print(f\"Accuratezza del modello: {accuracy:.2f}\")\n", - "# Report di classificazione\n", - "print(classification_report(y_test, y_pred))\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import confusion_matrix\n", - "# Creiamo una heatmap con Seaborn\n", - "target_names = [\"No Rain\", \"Rain\"]\n", - "cm = confusion_matrix(y_test, y_pred)\n", - "plt.figure(figsize=(6, 4))\n", - "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=target_names, yticklabels=target_names)\n", - "# Aggiungiamo i titoli\n", - "plt.xlabel(\"Predicted Label\")\n", - "plt.ylabel(\"True Label\")\n", - "plt.title(\"Confusion Matrix\")\n", - "# Mostriamo il grafico\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow\n", - "import mlflow.sklearn\n", - "\n", - "# Impostiamo un nome per l'esperimento\n", - "mlflow.set_tracking_uri(\"http://127.0.0.1:5000\")\n", - "mlflow.set_experiment(\"weather_classification_experiment\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Usando il modello creato nella Parte 1\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "n_estimators = 100\n", - "\n", - "# Creiamo una nuova run MLflow\n", - "with mlflow.start_run():\n", - " # Log Param\n", - " mlflow.log_param(\"n_estimators\", n_estimators)\n", - "\n", - " # Creazione del modello\n", - " rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)\n", - " rf_model.fit(X_train, y_train)\n", - "\n", - " # Calcolo metriche\n", - " y_pred = rf_model.predict(X_test)\n", - " accuracy = accuracy_score(y_test, y_pred)\n", - " mlflow.log_metric(\"accuracy\", accuracy)\n", - "\n", - " # Salviamo il modello\n", - " mlflow.sklearn.log_model(rf_model, \"random_forest_model\")\n", - "\n", - " print(f\"Esperimento concluso. Accuratezza registrata: {accuracy:.2f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import mlflow.sklearn\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "# Inserisci un run_id reale che trovi nell'interfaccia MLflow\n", - "RUN_ID = \"\"\n", - "\n", - "loaded_model = mlflow.sklearn.load_model(f\"runs:/{RUN_ID}/random_forest_model\")\n", - "\n", - "# Verifichiamo l'accuratezza\n", - "y_loaded_pred = loaded_model.predict(X_test)\n", - "acc_loaded = accuracy_score(y_test, y_loaded_pred)\n", - "print(f\"Accuratezza del modello caricato: {acc_loaded:.2f}\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.10 ('venv': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "vscode": { - "interpreter": { - "hash": "a8e57e261110579b348b220fe1c71aea6cd1a8cd238281550a2a6c068185ef06" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 3e5be1e2752c25932dced866e7398741e4a098ac Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 12 Mar 2025 13:32:52 +0100 Subject: [PATCH 37/70] fix MD lint --- AI/Academy/1-Intro-To-ML-And-MLOps/README.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md index 76da717..3928205 100644 --- a/AI/Academy/1-Intro-To-ML-And-MLOps/README.md +++ b/AI/Academy/1-Intro-To-ML-And-MLOps/README.md @@ -16,17 +16,20 @@ The first part focuses on Scikit-learn and the weather dataset. The second part Before we begin, we need to ensure we have everything required to run the lab properly. -#### **Requirements** +### **Requirements** This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well. In order to execute this laboratory, you will be asked to install a set of tools common in Machine Learning field: + - [**scikit-learn**](https://scikit-learn.org/stable/index.html): Machine learning library with modeling and evaluation tools. - [**pandas**](https://pandas.pydata.org/): Framework for data manipulation and analysis in Python. - [**seaborn**](https://seaborn.pydata.org/): Data visualization library based on Matplotlib. - [**mlflow**](https://mlflow.org/): Tool for tracking and managing machine learning experiments. ### 1. Clone the Repository + To start, clone the lab repository by running the following command in the terminal: + ```sh git clone https://github.com/kiratech/labs.git ``` @@ -44,6 +47,7 @@ Then, checkout the `lab` branch: ```sh git checkout academy-ai ``` + This folder contains resources related to AI-themed labs. ### 3. Navigate to the 1-Intro-To-ML-And-MLOps Folder @@ -57,6 +61,7 @@ Go to the folder of the first lab: ### 4. Open the Project in VSCode At this point, open VSCode from the file explorer or by running the command: + ```sh code . ``` @@ -72,6 +77,7 @@ Using the **VSCode** terminal, create a virtual environment: ``` Activate the Virtual Environment: + ```sh conda activate lab_env ``` @@ -82,18 +88,21 @@ You should see the `(lab_env)` prefix in the terminal, indicating that the virtu Besides the default packages in the environment, additional libraries may be needed for the lab. Before installing them, it's always a good practice to update `pip` to avoid compatibility issues: + ```sh pip install --upgrade pip ``` Now, install some essential packages for data analysis and machine learning: + ```sh pip install scikit-learn pandas seaborn mlflow ipykernel ``` Verify that the packages were installed correctly with: + ```sh conda list ``` -At this point, you can proceed with the `lab.ipynb` file. \ No newline at end of file +At this point, you can proceed with the `lab.ipynb` file. From 034863ac93795bda78aee25ba59ec9c3a9765d46 Mon Sep 17 00:00:00 2001 From: Ale3e Date: Thu, 13 Mar 2025 17:10:48 +0100 Subject: [PATCH 38/70] Change folder structure --- Academy/AI/1-Intro-To-ML-And-MLOps/.DS_Store | Bin 0 -> 6148 bytes .../AI}/1-Intro-To-ML-And-MLOps/.gitignore | 0 .../AI}/1-Intro-To-ML-And-MLOps/README.md | 0 .../AI}/1-Intro-To-ML-And-MLOps/lab.ipynb | 0 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 Academy/AI/1-Intro-To-ML-And-MLOps/.DS_Store rename {AI/Academy => Academy/AI}/1-Intro-To-ML-And-MLOps/.gitignore (100%) rename {AI/Academy => Academy/AI}/1-Intro-To-ML-And-MLOps/README.md (100%) rename {AI/Academy => Academy/AI}/1-Intro-To-ML-And-MLOps/lab.ipynb (100%) diff --git a/Academy/AI/1-Intro-To-ML-And-MLOps/.DS_Store b/Academy/AI/1-Intro-To-ML-And-MLOps/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..eff540416d709edfdecfcc302f2f9ff1093ebf89 GIT binary patch literal 6148 zcmeH~F>b>!3`IW^4*{}x?5HIN$PI)bIYBQ_G;XIN8G>fk(fdhpsnazF0T;svrMTPG_t%jv Date: Fri, 4 Apr 2025 09:51:28 +0200 Subject: [PATCH 39/70] Update README.md --- Academy/AI/1-Intro-To-ML-And-MLOps/README.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/Academy/AI/1-Intro-To-ML-And-MLOps/README.md b/Academy/AI/1-Intro-To-ML-And-MLOps/README.md index 3928205..e4c0705 100644 --- a/Academy/AI/1-Intro-To-ML-And-MLOps/README.md +++ b/Academy/AI/1-Intro-To-ML-And-MLOps/README.md @@ -58,15 +58,7 @@ Go to the folder of the first lab: cd AI/Academy/1-Intro-To-ML-And-MLOps ``` -### 4. Open the Project in VSCode - -At this point, open VSCode from the file explorer or by running the command: - -```sh - code . -``` - -### 5. Create a Virtual Environment +### 4. Create a Virtual Environment A virtual environment allows you to isolate the project's dependencies from the system-wide ones. @@ -84,7 +76,7 @@ Activate the Virtual Environment: You should see the `(lab_env)` prefix in the terminal, indicating that the virtual environment is active. -### 6. Install Required Packages +### 5. Install Required Packages Besides the default packages in the environment, additional libraries may be needed for the lab. Before installing them, it's always a good practice to update `pip` to avoid compatibility issues: @@ -106,3 +98,11 @@ Verify that the packages were installed correctly with: ``` At this point, you can proceed with the `lab.ipynb` file. + +### 6. Open the Project in VSCode + +At this point, open VSCode from the file explorer or by running the command: + +```sh + code . +``` From e171841e594c9d6942484622d0ff6c939543dae8 Mon Sep 17 00:00:00 2001 From: rndg <37615999+rndg@users.noreply.github.com> Date: Fri, 4 Apr 2025 09:56:09 +0200 Subject: [PATCH 40/70] Update README.md --- Academy/AI/1-Intro-To-ML-And-MLOps/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Academy/AI/1-Intro-To-ML-And-MLOps/README.md b/Academy/AI/1-Intro-To-ML-And-MLOps/README.md index e4c0705..648588c 100644 --- a/Academy/AI/1-Intro-To-ML-And-MLOps/README.md +++ b/Academy/AI/1-Intro-To-ML-And-MLOps/README.md @@ -55,7 +55,7 @@ This folder contains resources related to AI-themed labs. Go to the folder of the first lab: ```sh - cd AI/Academy/1-Intro-To-ML-And-MLOps + cd Academy/AI/1-Intro-To-ML-And-MLOps ``` ### 4. Create a Virtual Environment From 90d252fb2b65382e573a29fcfcd992eff7f1e01a Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 23 Apr 2025 22:40:02 +0200 Subject: [PATCH 41/70] first skeleton for part 2 --- Academy/AI/2-ML-Model-Lifecycle/.DS_Store | Bin 0 -> 6148 bytes Academy/AI/2-ML-Model-Lifecycle/.gitignore | 179 ++++++++++++++++++ .../.pre-commit-config.yaml | 23 +++ Academy/AI/2-ML-Model-Lifecycle/Makefile | 22 +++ Academy/AI/2-ML-Model-Lifecycle/README.md | 4 + .../AI/2-ML-Model-Lifecycle/environment.yaml | 17 ++ .../2-ML-Model-Lifecycle/notebooks/.DS_Store | Bin 0 -> 6148 bytes .../notebooks/01_explore_and_train.ipynb | 93 +++++++++ Academy/AI/2-ML-Model-Lifecycle/noxfile.py | 40 ++++ .../AI/2-ML-Model-Lifecycle/pyproject.toml | 26 +++ .../AI/2-ML-Model-Lifecycle/src/__init__.py | 0 Academy/AI/2-ML-Model-Lifecycle/src/config.py | 12 ++ Academy/AI/2-ML-Model-Lifecycle/src/data.py | 8 + .../AI/2-ML-Model-Lifecycle/src/monitor.py | 23 +++ .../AI/2-ML-Model-Lifecycle/src/pipeline.py | 45 +++++ .../AI/2-ML-Model-Lifecycle/src/retrain.py | 25 +++ Academy/AI/2-ML-Model-Lifecycle/src/serve.py | 25 +++ Academy/AI/2-ML-Model-Lifecycle/src/train.py | 35 ++++ .../2-ML-Model-Lifecycle/tests/test_data.py | 15 ++ .../2-ML-Model-Lifecycle/tests/test_train.py | 7 + 20 files changed, 599 insertions(+) create mode 100644 Academy/AI/2-ML-Model-Lifecycle/.DS_Store create mode 100644 Academy/AI/2-ML-Model-Lifecycle/.gitignore create mode 100644 Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml create mode 100644 Academy/AI/2-ML-Model-Lifecycle/Makefile create mode 100644 Academy/AI/2-ML-Model-Lifecycle/README.md create mode 100644 Academy/AI/2-ML-Model-Lifecycle/environment.yaml create mode 100644 Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store create mode 100644 Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb create mode 100644 Academy/AI/2-ML-Model-Lifecycle/noxfile.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/pyproject.toml create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/__init__.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/config.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/data.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/monitor.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/retrain.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/serve.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/train.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py diff --git a/Academy/AI/2-ML-Model-Lifecycle/.DS_Store b/Academy/AI/2-ML-Model-Lifecycle/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5172429f264de2441865cb4700216d4256da9242 GIT binary patch literal 6148 zcmeH~J!%6%427R!7lt%jx}3%b$PET#pTHLgIFQEJ;E>dF^gR7ES*H$5cmnB-G%I%Z zD|S`@Z2$T80!#olbXV*=%*>dt@PRwdU#I)^a=X5>;#J@&VrHyNnC;iLL0pQvfVyTmjO&;ssLc!1UOG})p;=82 zR;?Ceh}WZ?+UmMqI#RP8R>OzYoz15hnq@nzF`-!xQ4j$Um=RcIKKc27r2jVm&svm< zfC&6E0=7P!4tu^-ovjbA=k?dB`g+i*aXG_}p8zI)6mRKa+;6_1_R^8c3Qa!(fk8n8 H{*=HsM+*^= literal 0 HcmV?d00001 diff --git a/Academy/AI/2-ML-Model-Lifecycle/.gitignore b/Academy/AI/2-ML-Model-Lifecycle/.gitignore new file mode 100644 index 0000000..1c5d00a --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/.gitignore @@ -0,0 +1,179 @@ +# local mlflow +mlartifacts/* +mlruns/* +dev/* + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc \ No newline at end of file diff --git a/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml b/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml new file mode 100644 index 0000000..fde97d9 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + - repo: https://github.com/psf/black + rev: 24.3.0 + hooks: + - id: black + language_version: python3.11 + - repo: https://github.com/PyCQA/isort + rev: 5.13.2 + hooks: + - id: isort + language_version: python3.11 + - repo: https://github.com/PyCQA/flake8 + rev: 7.0.0 + hooks: + - id: flake8 + additional_dependencies: [flake8-bugbear] + - repo: local + hooks: + - id: pytest + name: pytest + entry: pytest -q + language: system + pass_filenames: false diff --git a/Academy/AI/2-ML-Model-Lifecycle/Makefile b/Academy/AI/2-ML-Model-Lifecycle/Makefile new file mode 100644 index 0000000..5baa515 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/Makefile @@ -0,0 +1,22 @@ +.PHONY: setup ci train serve monitor retrain pipeline clean +SHELL := /bin/zsh +ENV := lab_env_2 +CONDA_ACTIVATE = source $$(conda info --base)/etc/profile.d/conda.sh ; conda activate ; conda activate +NOX := $(CONDA_ACTIVATE) $(ENV) && nox + +setup: ## create environment + install hooks + conda env create -f environment.yaml --quiet + $(NOX) pre-commit install + +## ---------------------------------------------- +## simulazione "push" su vari branch Git / GitHub +## ---------------------------------------------- + +push-feature: ## simula push su feature/* + $(NOX) -R -s feature_pipeline + +push-develop: ## simula push su develop + $(NOX) -R -s lint -s tests -s develop_pipeline + +push-master: ## simula push su master/main + $(NOX) -R -s lint -s tests -s master_pipeline diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md new file mode 100644 index 0000000..89eb251 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -0,0 +1,4 @@ +ML Model Lifecycle +- makefile for pushes +- nox for gh actions +- prefect for training orchestrator diff --git a/Academy/AI/2-ML-Model-Lifecycle/environment.yaml b/Academy/AI/2-ML-Model-Lifecycle/environment.yaml new file mode 100644 index 0000000..9b0e1b1 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/environment.yaml @@ -0,0 +1,17 @@ +name: lab_env_2 +channels: [conda-forge] +dependencies: + - python=3.11 + - pip + - pip: + - scikit-learn + - pandas + - mlflow + - prefect==2.* + - fastapi + - uvicorn[standard] + - pydantic + - pytest + - nox + - typer[all] + - evidently # per il drift monitoring diff --git a/Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store b/Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0 per esplorare gli esperimenti." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.6 64-bit", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.6" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Academy/AI/2-ML-Model-Lifecycle/noxfile.py b/Academy/AI/2-ML-Model-Lifecycle/noxfile.py new file mode 100644 index 0000000..8dac37e --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/noxfile.py @@ -0,0 +1,40 @@ +import nox +nox.options.reuse_existing_virtualenvs = True + +PYTHON = "3.11" + +# ----- helper ------------------------------------------------ +def install_project(session): + session.install("-e", ".") # pacchetto src/ + +# ----------------- CI step di base -------------------------- +@nox.session(python=PYTHON) +def lint(session): + session.install("pre-commit") + session.run("pre-commit", "run", "--all-files", "--show-diff-on-failure") + +@nox.session(python=PYTHON) +def tests(session): + session.install("pytest", "scikit-learn", "pandas") + install_project(session) + session.run("pytest", "-q") + +# ----------------- WORKFLOW SIMULATI ------------------------ +@nox.session(python=PYTHON) +def feature_pipeline(session): + """Hyper-parameter sweep – solo metadati.""" + install_project(session) + # lancia Prefect flow + session.run("python", "-m", "src.pipeline", "train_sweep") + +@nox.session(python=PYTHON) +def develop_pipeline(session): + """Train con best hp – salva anche il modello.""" + install_project(session) + session.run("python", "-m", "src.pipeline", "train_best") + +@nox.session(python=PYTHON) +def master_pipeline(session): + """Serve il miglior modello salvato.""" + install_project(session) + session.run("python", "-m", "src.pipeline", "serve_best") diff --git a/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml b/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml new file mode 100644 index 0000000..176fbe7 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml @@ -0,0 +1,26 @@ +[build-system] +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" + +[project] +name = "mlops_lab" +version = "0.1.0" +description = "End-to-end MLOps lab pipeline" +authors = [{ name = "ranl", email = "lorenzo.randazzo@kiratech.ch" }] +readme = "README.md" +license = { text = "MIT" } +dependencies = [ + "scikit-learn", + "pandas", + "mlflow", + "prefect", + "fastapi", + "uvicorn[standard]", + "pydantic", + "typer[all]", + "evidently", +] +requires-python = ">=3.11" + +[tool.setuptools] +packages = ["src"] diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/__init__.py b/Academy/AI/2-ML-Model-Lifecycle/src/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/config.py b/Academy/AI/2-ML-Model-Lifecycle/src/config.py new file mode 100644 index 0000000..b80101e --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/config.py @@ -0,0 +1,12 @@ +# src/config.py +from pathlib import Path +from pydantic import BaseModel + +class Settings(BaseModel): + project_root: Path = Path(__file__).resolve().parents[1] + artifacts_dir: Path = project_root / "artifacts" + model_name: str = "wine_rf.pkl" + mlflow_uri: str = f"file:{project_root/'mlruns'}" + +CFG = Settings() +CFG.artifacts_dir.mkdir(exist_ok=True) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/data.py b/Academy/AI/2-ML-Model-Lifecycle/src/data.py new file mode 100644 index 0000000..66d4946 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/data.py @@ -0,0 +1,8 @@ +# src/data.py +import pandas as pd +from sklearn.datasets import load_wine +from sklearn.model_selection import train_test_split + +def load(split: float = .2, seed: int = 42): + X, y = load_wine(return_X_y=True, as_frame=True) + return train_test_split(X, y, test_size=split, random_state=seed) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py b/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py new file mode 100644 index 0000000..97af243 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py @@ -0,0 +1,23 @@ +# src/monitor.py +""" +Lettura delle predizioni servite, verifica di drift su Evidently +e scrittura di un report HTML. +Richiamabile via cron o manualmente: python monitor.py run +""" +import typer, pandas as pd +from evidently import Report +from evidently.metrics import DataDriftPreset + +app = typer.Typer() + +@app.command() +def run(ref_csv: str = "data/reference.csv", + cur_csv: str = "data/current.csv"): + ref, cur = pd.read_csv(ref_csv), pd.read_csv(cur_csv) + report = Report(metrics=[DataDriftPreset()]) + report.run(ref, cur) + report.save_html("artifacts/drift_report.html") + typer.echo("✅ Drift report scritto in artifacts/drift_report.html") + +if __name__ == "__main__": + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py new file mode 100644 index 0000000..bb76ffd --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py @@ -0,0 +1,45 @@ +import subprocess +from prefect import flow +from itertools import product +import mlflow, tempfile, subprocess + +# ----------------------------------------------------------- +# 1) sweep: lancia molti train con log_model=False +@flow +def train_sweep(): + params_grid = { + "n_estimators": [50, 100, 200, 400], + "max_depth": [None, 5, 10], + } + for n_est, d in product(params_grid["n_estimators"], params_grid["max_depth"]): + train.with_options(name=f"rf_ne{n_est}_md{d}").submit( + n_estimators=n_est, max_depth=d, log_model=False + ) + +# 2) train_best: sceglie il run con max accuracy e salva il modello +@flow +def train_best(): + client = mlflow.MlflowClient() + runs = mlflow.search_runs(order_by=["metrics.accuracy DESC"], max_results=1) + top = runs.iloc[0] + best_params = { + "n_estimators": int(top["params.n_estimators"]), + "max_depth": None if top["params.max_depth"] == "None" else int(top["params.max_depth"]), + } + train(**best_params, log_model=True) + +# 3) serve_best: scarica l'ultimo modello loggato e avvia FastAPI +@flow +def serve_best(): + runs = mlflow.search_runs( + filter_string="tags.mlflow.runName LIKE '%rf_%'", + order_by=["start_time DESC"], + max_results=1, + ) + run_id = runs.iloc[0]["run_id"] + client = mlflow.MlflowClient() + art = client.list_artifacts(run_id) + model_path = next(a.path for a in art if a.path.endswith(".pkl")) + with tempfile.TemporaryDirectory() as tmp: + local_pkl = client.download_artifacts(run_id, model_path, tmp) + subprocess.run(["python", "-m", "src.serve", local_pkl]) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py b/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py new file mode 100644 index 0000000..77875ec --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py @@ -0,0 +1,25 @@ +# src/retrain.py +""" +Retrain se il drift supera soglia; esegue train.run() con parametri di default. +""" +import json, subprocess, typer, pathlib + +DRIFT_PATH = pathlib.Path("artifacts/drift_report.html") + +app = typer.Typer() + +@app.command() +def run(threshold: float = 0.6): + from bs4 import BeautifulSoup + drift = 0.0 + if DRIFT_PATH.exists(): + soup = BeautifulSoup(DRIFT_PATH.read_text(), "html.parser") + drift = float(soup.select_one("#drift_score").text) + if drift > threshold: + typer.echo(f"⚠️ Drift {drift:.2f} > {threshold}: retraining…") + subprocess.run(["python", "-m", "src.train", "run"]) + else: + typer.echo(f"👍 Drift ok ({drift:.2f} ≤ {threshold})") + +if __name__ == "__main__": + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py new file mode 100644 index 0000000..42a62da --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py @@ -0,0 +1,25 @@ +# src/serve.py +import joblib, uvicorn, typer +from fastapi import FastAPI +from pydantic import BaseModel +from config import CFG + +app = FastAPI(title="Wine Quality Inference") + +model = joblib.load(CFG.artifacts_dir / CFG.model_name) + +class Features(BaseModel): # 11 feature numeriche + alcohol: float + magnesium: float + hue: float + # … (completare con le altre) + +@app.post("/predict") +def predict(feat: Features): + import numpy as np, pandas as pd + X = pd.DataFrame([feat.dict()]) + proba = model.predict_proba(X)[0].tolist() + return {"class": int(model.predict(X)[0]), "proba": proba} + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/train.py b/Academy/AI/2-ML-Model-Lifecycle/src/train.py new file mode 100644 index 0000000..1328cac --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/train.py @@ -0,0 +1,35 @@ +# src/train.py +import mlflow, joblib, typer +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +from src.config import CFG +from src.data import load + +app = typer.Typer() + +@app.command() +def run( + n_estimators: int = 200, + max_depth: int | None = None, + log_model: bool = typer.Option(True, "--log-model/--no-log-model") +): + mlflow.set_tracking_uri("http://127.0.0.1:5000") + mlflow.set_experiment("weather_classification_experiment") + + X_tr, X_te, y_tr, y_te = load() + with mlflow.start_run(): + model = RandomForestClassifier( + n_estimators=n_estimators, max_depth=max_depth, random_state=42 + ).fit(X_tr, y_tr) + + acc = accuracy_score(y_te, model.predict(X_te)) + mlflow.log_param("n_estimators", n_estimators) + mlflow.log_metric("accuracy", acc) + + if log_model: + joblib.dump(model, CFG.artifacts_dir / CFG.model_name) + mlflow.log_artifact(CFG.artifacts_dir / CFG.model_name) + typer.echo(f"✅ accuracy={acc:0.3f}") + +if __name__ == "__main__": + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py new file mode 100644 index 0000000..7604141 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py @@ -0,0 +1,15 @@ +"""Test basilari sul caricamento e integrità del dataset""" + +from src.data import load +import pandas as pd + +def test_split_shapes(): + X_tr, X_te, y_tr, y_te = load() + assert len(X_tr) > 0 and len(X_te) > 0 + assert len(X_tr) == len(y_tr) + assert len(X_te) == len(y_te) + +def test_no_missing_values(): + X_tr, X_te, y_tr, y_te = load() + assert not X_tr.isna().any().any() + assert not X_te.isna().any().any() diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py new file mode 100644 index 0000000..3ef3065 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py @@ -0,0 +1,7 @@ +# tests/test_train.py +from src.train import RandomForestClassifier, load + +def test_accuracy(): + X_tr, X_te, y_tr, y_te = load() + model = RandomForestClassifier(random_state=0).fit(X_tr, y_tr) + assert model.score(X_te, y_te) > 0.8 From 07f694bdf553fadd0ce3f70dd35a665b23127ea9 Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 24 Apr 2025 00:06:25 +0200 Subject: [PATCH 42/70] working prefect and mlflow --- .../artifacts/wine_rf.pkl | Bin 0 -> 210881 bytes .../AI/2-ML-Model-Lifecycle/environment.yaml | 2 + Academy/AI/2-ML-Model-Lifecycle/load_data.py | 6 + .../notebooks/01_explore_and_train.ipynb | 46 ++++++- Academy/AI/2-ML-Model-Lifecycle/src/config.py | 9 +- .../AI/2-ML-Model-Lifecycle/src/pipeline.py | 126 +++++++++++++----- Academy/AI/2-ML-Model-Lifecycle/src/serve.py | 73 +++++++--- Academy/AI/2-ML-Model-Lifecycle/src/sweep.py | 58 ++++++++ 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zec{LrU3PGn_X=gvpl9pe`OTxAKkI}PAGrk#$uD=ld92hVG8yOZ&tGbw%a7x7;F4r2axzC)hZXuk9+bANwkeV=u8=JWiXBmPGXu937e`+Q3EJpMoz z7^3gKKSnQs2Mpm`d?~tSJpnvm2;ZHrcafU=iu~#Ml~2EL!H(?2VE z$B7(_zgNe5{6pw{hLy+PY5#C`9G~Xj^ds$Yp?R+7#o6bBgz}As>ZzEnp$9(wqVrn3 znDozr^n5MyaFPcCJQq@LsnF%-WUc3Ko^=TPCiqcYE^_e`Xbku@_|><+?l}IilePl; zYcsFsPanGOhF)`4RJtDk{R~tir}BU9ChPoT{|Aq`#hZT@pPJfF$W`nw3qQ`+HRkAd z&KW;yhMRAzsCSBw+yaK=m+OBxPV#0U7qJiECw?y5%aS@n8oobrg?D~N^OQdN3j5RI zU)eA9hGR+_h+hnVbn*vfuZ)J-&MWpWBm2t0YmuE z)A&ZdVYgvN(VwcEIzMK$sQ2^IreD>0&D#&tL+W~Rw_n}y%cS_e{bjwsDqlivS=?mm z>hHu8?tU7}|3bTg`L-WCU \u001b[39m\u001b[32m57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:42\u001b[39m, in \u001b[36mBunch.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 38\u001b[39m warnings.warn(\n\u001b[32m 39\u001b[39m \u001b[38;5;28mself\u001b[39m._deprecated_key_to_warnings[key],\n\u001b[32m 40\u001b[39m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m 41\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__getitem__\u001b[39m(key)\n", + "\u001b[31mKeyError\u001b[39m: 'head'", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_wine\n\u001b[32m 3\u001b[39m data = load_wine(as_frame=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m data.head()\n", + "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:59\u001b[39m, in \u001b[36mBunch.__getattr__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(key)\n", + "\u001b[31mAttributeError\u001b[39m: head" + ] + } + ], + "source": [ + "from sklearn.datasets import load_wine\n", + "\n", + "data = load_wine(as_frame=True)\n", + "\n", + "data.data.head()" + ] + }, { "cell_type": "markdown", "id": "28e5c13e", @@ -74,17 +106,25 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.6 64-bit", + "display_name": "Python 3.11.12 ('lab_env_2')", "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.9.6" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.12" }, "vscode": { "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + "hash": "f3091eeb433eba5ffd1439d05e3608594fa2a8935778ae687bbee724d90b3358" } } }, diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/config.py b/Academy/AI/2-ML-Model-Lifecycle/src/config.py index b80101e..d757d3d 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/config.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/config.py @@ -4,9 +4,10 @@ class Settings(BaseModel): project_root: Path = Path(__file__).resolve().parents[1] - artifacts_dir: Path = project_root / "artifacts" - model_name: str = "wine_rf.pkl" - mlflow_uri: str = f"file:{project_root/'mlruns'}" - + artifacts_dir: Path = project_root / "artifacts" + model_name: str = "wine_rf.pkl" + mlflow_uri: str = f"file:{project_root/'mlruns'}" + experiment_name: str = "WineSweepDemo" + CFG = Settings() CFG.artifacts_dir.mkdir(exist_ok=True) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py index bb76ffd..322bcc4 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py @@ -1,45 +1,101 @@ -import subprocess -from prefect import flow +# src/pipeline.py +"""Prefect 2 flows: + • train_sweep grid-search parallelo (solo metadati) + • train_best recupera best hp + addestra salvando modello + • serve_best scarica modello migliore e lo serve via FastAPI +""" from itertools import product -import mlflow, tempfile, subprocess +from pathlib import Path +from subprocess import Popen +import tempfile -# ----------------------------------------------------------- -# 1) sweep: lancia molti train con log_model=False -@flow -def train_sweep(): - params_grid = { - "n_estimators": [50, 100, 200, 400], - "max_depth": [None, 5, 10], - } - for n_est, d in product(params_grid["n_estimators"], params_grid["max_depth"]): - train.with_options(name=f"rf_ne{n_est}_md{d}").submit( - n_estimators=n_est, max_depth=d, log_model=False - ) +import mlflow +from prefect import flow, task, get_run_logger -# 2) train_best: sceglie il run con max accuracy e salva il modello -@flow -def train_best(): - client = mlflow.MlflowClient() - runs = mlflow.search_runs(order_by=["metrics.accuracy DESC"], max_results=1) - top = runs.iloc[0] - best_params = { - "n_estimators": int(top["params.n_estimators"]), - "max_depth": None if top["params.max_depth"] == "None" else int(top["params.max_depth"]), +from src.config import CFG +from src.train import run as train_run +from src.serve import run as serve_run + +# --------------------------------------------------------------------------- +# Tasks +# --------------------------------------------------------------------------- + + +@task +def train_task(n_estimators: int, max_depth: int | None): + return train_run(n_estimators=n_estimators, max_depth=max_depth, log_model=False) + + +@task +def find_best_params(metric: str = "accuracy") -> dict: + logger = get_run_logger() + runs = mlflow.search_runs(order_by=[f"metrics.{metric} DESC"], max_results=1) + best = runs.iloc[0] + params = { + "n_estimators": int(best["params.n_estimators"]), + "max_depth": None + if best["params.max_depth"] in ("", "None") + else int(best["params.max_depth"]), } - train(**best_params, log_model=True) + logger.info(f"Best params found: {params}") + return params -# 3) serve_best: scarica l'ultimo modello loggato e avvia FastAPI -@flow -def serve_best(): + +@task +def download_best_model(metric: str = "accuracy") -> Path: + logger = get_run_logger() + EXP_ID = mlflow.get_experiment_by_name(CFG.experiment_name).experiment_id runs = mlflow.search_runs( - filter_string="tags.mlflow.runName LIKE '%rf_%'", - order_by=["start_time DESC"], - max_results=1, + experiment_ids=[EXP_ID], order_by=[f"metrics.{metric} DESC"], max_results=1 ) run_id = runs.iloc[0]["run_id"] + client = mlflow.MlflowClient() art = client.list_artifacts(run_id) - model_path = next(a.path for a in art if a.path.endswith(".pkl")) - with tempfile.TemporaryDirectory() as tmp: - local_pkl = client.download_artifacts(run_id, model_path, tmp) - subprocess.run(["python", "-m", "src.serve", local_pkl]) + pkl_path = next(a.path for a in art if a.path.endswith(".pkl")) + + tmp_dir = tempfile.mkdtemp() + local_path = Path(client.download_artifacts(run_id, pkl_path, tmp_dir)) + logger.info(f"Downloaded best model to: {local_path}") + return local_path + + +@task +def serve_model(model_path: Path): + # Avvia l'API in un processo dedicato (non blocca il flow calling) + Popen(["python", "-m", "src.serve", "--model-path", str(model_path)]) + + +# --------------------------------------------------------------------------- +# Flows +# --------------------------------------------------------------------------- + + +@flow(name="train_sweep") +def train_sweep(): + grid = { + "n_estimators": [50, 100, 200], + "max_depth": [None, 5, 10], + } + for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): + train_task.submit(n_estimators=n_est, max_depth=m_depth) + + +@flow(name="train_best") +def train_best(): + params = find_best_params() + # unpack dict in **kwargs, salva anche il modello + train_run(**params, log_model=True) + + +@flow(name="serve_best") +def serve_best(): + model_path = download_best_model() + serve_model(model_path) + + +if __name__ == "__main__": + import sys + + # Facile CLI: python -m src.pipeline + locals()[sys.argv[1]]() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py index 42a62da..fc7b4e3 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py @@ -1,25 +1,66 @@ # src/serve.py -import joblib, uvicorn, typer +"""Inferenza FastAPI: serve un modello RF salvato con joblib.""" +from pathlib import Path +from typing import Optional + +import joblib +import typer +import uvicorn from fastapi import FastAPI from pydantic import BaseModel -from config import CFG -app = FastAPI(title="Wine Quality Inference") +from src.config import CFG + +app_cli = typer.Typer(add_completion=False) + + +def build_app(model_path: Path) -> FastAPI: + model = joblib.load(model_path) + + class Features(BaseModel): + alcohol: float + malic_acid: float + ash: float + alcalinity_of_ash: float + magnesium: float + total_phenols: float + flavanoids: float + nonflavanoid_phenols: float + proanthocyanins: float + color_intensity: float + hue: float + od280_od315_of_diluted_wines: float + proline: float + + api = FastAPI(title="Wine Quality Inference") + + @api.post("/predict") + def predict(feat: Features): + import pandas as pd + + X = pd.DataFrame([feat.dict()]) + proba = model.predict_proba(X)[0].tolist() + return {"class": int(model.predict(X)[0]), "proba": proba} + + return api -model = joblib.load(CFG.artifacts_dir / CFG.model_name) -class Features(BaseModel): # 11 feature numeriche - alcohol: float - magnesium: float - hue: float - # … (completare con le altre) +@app_cli.command() +def run( + model_path: Optional[Path] = typer.Option( + None, + "--model-path", + "-m", + help="Percorso del modello .pkl; default = ultimo train locale.", + ), + host: str = "0.0.0.0", + port: int = 8000, +): + """Avvia l'API sul modello indicato (o su quello di default).""" + path = model_path or (CFG.artifacts_dir / CFG.model_name) + api = build_app(path) + uvicorn.run(api, host=host, port=port) -@app.post("/predict") -def predict(feat: Features): - import numpy as np, pandas as pd - X = pd.DataFrame([feat.dict()]) - proba = model.predict_proba(X)[0].tolist() - return {"class": int(model.predict(X)[0]), "proba": proba} if __name__ == "__main__": - uvicorn.run(app, host="0.0.0.0", port=8000) + app_cli() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py b/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py new file mode 100644 index 0000000..91e141c --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py @@ -0,0 +1,58 @@ +# src/sweep.py +""" +Train-sweep demo +——————— +• Ciclo su piccoli hyper-parametri RF +• Log parametri + accuracy su MLflow +• Flow tracciata sulla UI di Prefect 2 (Orion) +""" + +from itertools import product + +import mlflow +from prefect import flow, task +from sklearn.datasets import load_wine +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split + +# -------------------------------------------------------------------- +# configura MLflow ➜ cartella locale "mlruns" nella root del progetto +mlflow.set_tracking_uri("file:mlruns") + +# -------------------------------------------------------------------- +@task(log_prints=True) +def train_once(n_estimators: int, max_depth: int | None): + """Un singolo addestramento + log su MLflow.""" + X, y = load_wine(return_X_y=True, as_frame=True) + X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42) + + print(f"Training with n_estimators={n_estimators}, max_depth={max_depth}") + with mlflow.start_run(): + mlflow.log_param("n_estimators", n_estimators) + mlflow.log_param("max_depth", max_depth) + + model = RandomForestClassifier( + n_estimators=n_estimators, max_depth=max_depth, random_state=0 + ).fit(X_tr, y_tr) + + acc = accuracy_score(y_te, model.predict(X_te)) + mlflow.log_metric("accuracy", acc) + + return acc + + +@flow(name="train_sweep") +def train_sweep(): + """Grid-search minimal su due hyper-parametri RF.""" + grid = { + "n_estimators": [1, 10, 100], + "max_depth": [None, 5, 10], + } + + for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): + train_once.submit(n_estimators=n_est, max_depth=m_depth) # .submit = task async, appare come sub-run + + +if __name__ == "__main__": + train_sweep() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/train.py b/Academy/AI/2-ML-Model-Lifecycle/src/train.py index 1328cac..6dfbbd9 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/train.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/train.py @@ -1,35 +1,62 @@ # src/train.py -import mlflow, joblib, typer +"""Un singolo ciclo di training + logging su MLflow.""" + +from pathlib import Path + +import joblib +import mlflow +import typer +from sklearn.datasets import load_wine from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split + from src.config import CFG -from src.data import load -app = typer.Typer() +app = typer.Typer(add_completion=False) + +# Configura il backend MLflow (cartella "mlruns" nella root) +mlflow.set_tracking_uri(CFG.mlflow_uri) +mlflow.set_experiment(CFG.experiment_name) + +def _train_core(n_estimators: int, max_depth: int | None): + """Ritorna modello addestrato e accuracy.""" + X, y = load_wine(return_X_y=True, as_frame=True) + X.columns = X.columns.str.replace('/', '_') + X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42) + + model = RandomForestClassifier( + n_estimators=n_estimators, max_depth=max_depth, random_state=0 + ).fit(X_tr, y_tr) + + acc = accuracy_score(y_te, model.predict(X_te)) + return model, acc + @app.command() def run( n_estimators: int = 200, max_depth: int | None = None, - log_model: bool = typer.Option(True, "--log-model/--no-log-model") -): - mlflow.set_tracking_uri("http://127.0.0.1:5000") - mlflow.set_experiment("weather_classification_experiment") - - X_tr, X_te, y_tr, y_te = load() + log_model: bool = typer.Option( + True, "--log-model/--no-log-model", help="Salva anche il modello su MLflow." + ), +) -> float: + """Addestra e logga parametri + metriche (+ modello opzionale).""" with mlflow.start_run(): - model = RandomForestClassifier( - n_estimators=n_estimators, max_depth=max_depth, random_state=42 - ).fit(X_tr, y_tr) - - acc = accuracy_score(y_te, model.predict(X_te)) mlflow.log_param("n_estimators", n_estimators) + mlflow.log_param("max_depth", max_depth) + + model, acc = _train_core(n_estimators, max_depth) mlflow.log_metric("accuracy", acc) - + if log_model: - joblib.dump(model, CFG.artifacts_dir / CFG.model_name) - mlflow.log_artifact(CFG.artifacts_dir / CFG.model_name) - typer.echo(f"✅ accuracy={acc:0.3f}") + outfile = CFG.artifacts_dir / CFG.model_name + joblib.dump(model, outfile) + mlflow.log_artifact(outfile) + + typer.echo(f"✅ accuracy={acc:0.3f}") + return acc # utile se importato come funzione + if __name__ == "__main__": app() From 69dea0d73f14fafa9db47d5133a4c05ef2930ece Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 24 Apr 2025 00:07:39 +0200 Subject: [PATCH 43/70] rm of model pkl --- Academy/AI/2-ML-Model-Lifecycle/.gitignore | 2 +- .../2-ML-Model-Lifecycle/artifacts/wine_rf.pkl | Bin 210881 -> 0 bytes 2 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/artifacts/wine_rf.pkl diff --git a/Academy/AI/2-ML-Model-Lifecycle/.gitignore b/Academy/AI/2-ML-Model-Lifecycle/.gitignore index 1c5d00a..202df2a 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/.gitignore +++ b/Academy/AI/2-ML-Model-Lifecycle/.gitignore @@ -1,5 +1,5 @@ # local mlflow -mlartifacts/* +artifacts/* mlruns/* dev/* diff --git a/Academy/AI/2-ML-Model-Lifecycle/artifacts/wine_rf.pkl b/Academy/AI/2-ML-Model-Lifecycle/artifacts/wine_rf.pkl deleted file mode 100644 index f9ca17686db2e228d58a3a6af199cd7e36dd7e1d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 210881 zcmeEv2Y436_V>~x^oTSOAtEA0DK=2R89D;e^kPsn3C*C80zn1E8x;^WiY3_PqJUTc 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Academy/AI/2-ML-Model-Lifecycle/load_data.py | 6 - .../notebooks/01_explore_and_train.ipynb | 196 ++++++++++++-- Academy/AI/2-ML-Model-Lifecycle/noxfile.py | 69 +++-- .../AI/2-ML-Model-Lifecycle/pyproject.toml | 15 +- .../AI/2-ML-Model-Lifecycle/requirements.txt | 212 +++++++++++++++ Academy/AI/2-ML-Model-Lifecycle/src/config.py | 45 +++- Academy/AI/2-ML-Model-Lifecycle/src/data.py | 102 ++++++- .../AI/2-ML-Model-Lifecycle/src/monitor.py | 114 ++++++-- .../AI/2-ML-Model-Lifecycle/src/pipeline.py | 101 ------- .../2-ML-Model-Lifecycle/src/pipeline_ml.py | 195 ++++++++++++++ .../src/pipeline_monitoring.py | 111 ++++++++ .../AI/2-ML-Model-Lifecycle/src/retrain.py | 25 -- Academy/AI/2-ML-Model-Lifecycle/src/serve.py | 91 ++++++- Academy/AI/2-ML-Model-Lifecycle/src/sweep.py | 58 ---- Academy/AI/2-ML-Model-Lifecycle/src/train.py | 88 +++++-- Academy/AI/2-ML-Model-Lifecycle/src/utils.py | 29 ++ .../AI/2-ML-Model-Lifecycle/tests/__init__.py | 0 .../2-ML-Model-Lifecycle/tests/test_data.py | 24 +- .../2-ML-Model-Lifecycle/tests/test_serve.py | 33 +++ .../2-ML-Model-Lifecycle/tests/test_train.py | 13 +- 26 files changed, 1630 insertions(+), 357 deletions(-) delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/load_data.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/requirements.txt delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/pipeline_ml.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/pipeline_monitoring.py delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/retrain.py delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/sweep.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/src/utils.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/tests/__init__.py create mode 100644 Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py diff --git a/Academy/AI/2-ML-Model-Lifecycle/.DS_Store b/Academy/AI/2-ML-Model-Lifecycle/.DS_Store index 5172429f264de2441865cb4700216d4256da9242..b5c4c43ba91e8c72d6c550df3ec3bb345705cb2b 100644 GIT binary patch delta 353 zcmZoMXfc=|#>B)qu~3YagMop8V`8C*EEA9c0?a_n$iN`Lz>vm}$dJrX!cd%4zOisQ zV?9WQ6DU>$6w5@GEiOpP$xi~x?no-g$t*50Fu2CZ#LU9V#?HaP!OamHoRME1T#{H) zTI`fq6b<5qqy7GO&29EVw8yCqFM8D9gw&QCDhX`V*GP1|lq*xq;pS2H?iR@640= UMHE>AjHu~68Yk%57MnW31Wh#|QwxF|0tKQA390up6lU;>j69#BMp0U^Ix sk@*?RW&`H^Oq0BN0U!lS%7Mq~vu0ItRr3jhEB diff --git a/Academy/AI/2-ML-Model-Lifecycle/.gitignore b/Academy/AI/2-ML-Model-Lifecycle/.gitignore index 202df2a..7869f53 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/.gitignore +++ b/Academy/AI/2-ML-Model-Lifecycle/.gitignore @@ -1,7 +1,8 @@ # local mlflow -artifacts/* -mlruns/* -dev/* +artifacts +mlruns +dev +data # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml b/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml index fde97d9..7f9e0df 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml +++ b/Academy/AI/2-ML-Model-Lifecycle/.pre-commit-config.yaml @@ -1,23 +1,6 @@ +# .pre-commit-config.yaml repos: - - repo: https://github.com/psf/black - rev: 24.3.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.3.0 hooks: - - id: black - language_version: python3.11 - - repo: https://github.com/PyCQA/isort - rev: 5.13.2 - hooks: - - id: isort - language_version: python3.11 - - repo: https://github.com/PyCQA/flake8 - rev: 7.0.0 - hooks: - - id: flake8 - additional_dependencies: [flake8-bugbear] - - repo: local - hooks: - - id: pytest - name: pytest - entry: pytest -q - language: system - pass_filenames: false + - id: ruff diff --git a/Academy/AI/2-ML-Model-Lifecycle/Makefile b/Academy/AI/2-ML-Model-Lifecycle/Makefile index 5baa515..14b3806 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/Makefile +++ b/Academy/AI/2-ML-Model-Lifecycle/Makefile @@ -1,22 +1,35 @@ +# Makefile — Local GitHub Actions simulation using nox + conda environments .PHONY: setup ci train serve monitor retrain pipeline clean SHELL := /bin/zsh + +# Conda environment name ENV := lab_env_2 + +# Activate Conda + run nox in the correct environment CONDA_ACTIVATE = source $$(conda info --base)/etc/profile.d/conda.sh ; conda activate ; conda activate -NOX := $(CONDA_ACTIVATE) $(ENV) && nox +NOX := $(CONDA_ACTIVATE) $(ENV) && nox --no-venv + +# ------------------------------------------------------------ +# Environment setup +# ------------------------------------------------------------ -setup: ## create environment + install hooks +setup: ## Create conda environment + install pre-commit hooks conda env create -f environment.yaml --quiet $(NOX) pre-commit install -## ---------------------------------------------- -## simulazione "push" su vari branch Git / GitHub -## ---------------------------------------------- +# ------------------------------------------------------------ +# Simulated Git branch pushes (as CI/CD entrypoints) +# ------------------------------------------------------------ -push-feature: ## simula push su feature/* +push-feature: ## Simulate push to feature/* → hyperparameter sweep only $(NOX) -R -s feature_pipeline -push-develop: ## simula push su develop - $(NOX) -R -s lint -s tests -s develop_pipeline +push-develop: ## Simulate push to develop → lint + tests + train best model + $(NOX) -R -s lint + $(NOX) -R -s tests + $(NOX) -R -s develop_pipeline -push-master: ## simula push su master/main - $(NOX) -R -s lint -s tests -s master_pipeline +push-main: ## Simulate push to master/main → lint + tests + serve model + $(NOX) -R -s lint + $(NOX) -R -s tests + $(NOX) -R -s main_pipeline diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 89eb251..30e159f 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -1,4 +1,142 @@ -ML Model Lifecycle -- makefile for pushes -- nox for gh actions -- prefect for training orchestrator + +# Wine Quality ML Pipeline – Technical Review Guide + +This README provides a step-by-step guide for reviewing the Wine Quality MLOps proof of concept. +All workflows must be tested using the `Makefile` commands only. + +--- + +## 1. Environment Setup + +Create and activate the Conda environment (`lab_env_2`): + +```bash +conda env create -f environment.yaml +conda activate lab_env_2 +``` + +--- + +## 2. Start Required Services + +### MLflow UI + +```bash +conda activate lab_env_2 +mlflow ui +``` + +Open: [http://localhost:5000](http://localhost:5000) + +--- + +### Prefect Server + +```bash +conda activate lab_env_2 +prefect server start +``` + +Open: [http://localhost:4200](http://localhost:4200) + +In another terminal: + +```bash +prefect agent start +``` + +--- + +## 3. Run Workflows via `make` + +All workflows must be executed via the provided `Makefile` in another terminal: +```bash +conda activate lab_env_2 +``` + +### Feature Branch (Hyperparameter Sweep) + +```bash +make push-feature +``` + +- Runs `train_experiment` on a parameter grid +- Logs metadata only (no model artifact) + +--- + +### Develop Branch (Train Best Model) + +```bash +make push-develop +``` + +- Runs: `lint`, `tests`, `train_best` +- Trains model using best hyperparams from MLflow +- Logs model artifact to MLflow + +--- + +### Main Branch (Model Serving) + +```bash +make push-main +``` + +- Runs: `lint`, `tests`, `serve_best` +- Starts a FastAPI app at [http://localhost:9000/predict](http://localhost:9000/predict) +- To shutdown the FastAPI server: `kill -9 $(lsof -ti :9000)` + +Example test request: + +```bash +curl -X POST http://localhost:9000/predict -H "Content-Type: application/json" \ + -d '{"alcohol":13.0,"malic_acid":2.0,"ash":2.5,"alcalinity_of_ash":15.0, + "magnesium":100.0,"total_phenols":2.0,"flavanoids":2.5, + "nonflavanoid_phenols":0.3,"proanthocyanins":1.9,"color_intensity":5.0, + "hue":1.0,"od280_od315_of_diluted_wines":3.0,"proline":1000}' +``` + +--- + +### Monitoring + Drift Detection + +This runs a simulated batch with drift. If drift is detected (p ≤ 0.05), it retriggers training. + +```bash +python -m src.pipeline_monitoring +``` + +Generates: +- A deployment of flow running every minute +- `artifacts/drift_report.html` +- `artifacts/drift_report.json` +- Retraining of best model if triggered + +--- + +## 4. Verify Output in UI Tools + +| Component | URL | Check | +|----------|-----|-------| +| **MLflow UI** | http://localhost:5000 | Parameters, metrics, model | +| **Prefect UI** | http://localhost:4200 | Flow runs, logs | +| **FastAPI** | http://localhost:9000/predict | Model serving | +| **Drift Report** | `artifacts/drift_report.html` | Open manually in browser | + +--- + +## Summary + +This PoC runs fully locally using: + +- Conda +- Nox +- Make +- MLflow +- Prefect +- FastAPI + +No Docker or remote services are required. + +Please run and verify each `make` command as documented above. diff --git a/Academy/AI/2-ML-Model-Lifecycle/environment.yaml b/Academy/AI/2-ML-Model-Lifecycle/environment.yaml index 0233080..6a85694 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/environment.yaml +++ b/Academy/AI/2-ML-Model-Lifecycle/environment.yaml @@ -1,19 +1,236 @@ name: lab_env_2 -channels: [conda-forge] +channels: + - conda-forge + - defaults dependencies: - - python=3.11 - - ipykernel - - pip + - appnope=0.1.2 + - argcomplete=1.12.3 + - asttokens=3.0.0 + - attrs=25.3.0 + - backports=1.0 + - backports.functools_lru_cache=2.0.0 + - bzip2=1.0.8 + - ca-certificates=2025.2.25 + - cachetools=5.5.2 + - chardet=5.2.0 + - colorama=0.4.6 + - colorlog=5.0.1 + - comm=0.2.1 + - debugpy=1.8.11 + - decorator=5.1.1 + - dependency-groups=1.3.0 + - distlib=0.3.8 + - exceptiongroup=1.2.2 + - executing=0.8.3 + - filelock=3.18.0 + - importlib_metadata=4.13.0 + - ipykernel=6.29.5 + - ipython=9.1.0 + - ipython_pygments_lexers=1.1.1 + - jedi=0.19.2 + - jinja2=3.1.6 + - jupyter_client=8.6.3 + - jupyter_core=5.7.2 + - libcxx=14.0.6 + - libexpat=2.7.0 + - libffi=3.4.6 + - liblzma=5.8.1 + - libsodium=1.0.18 + - libsqlite=3.49.1 + - libzlib=1.3.1 + - markupsafe=3.0.2 + - matplotlib-inline=0.1.6 + - ncurses=6.5 + - nest-asyncio=1.6.0 + - nox=2025.2.9 + - openssl=3.5.0 + - packaging=24.2 + - parso=0.8.4 + - pexpect=4.8.0 + - pickleshare=0.7.5 + - pip=25.0.1 + - platformdirs=4.3.7 + - pluggy=1.5.0 + - prompt-toolkit=3.0.43 + - prompt_toolkit=3.0.43 + - psutil=5.9.0 + - ptyprocess=0.7.0 + - pure_eval=0.2.2 + - pygments=2.19.1 + - pyproject-api=1.9.0 + - python=3.11.12 + - python-dateutil=2.9.0post0 + - python_abi=3.11 + - pyzmq=26.2.0 + - readline=8.2 + - setuptools=78.1.0 + - six=1.17.0 + - stack_data=0.2.0 + - tk=8.6.13 + - tomli=2.2.1 + - tornado=6.4.2 + - tox=4.25.0 + - traitlets=5.14.3 + - virtualenv=20.30.0 + - wcwidth=0.2.5 + - wheel=0.45.1 + - zeromq=4.3.5 + - zipp=3.21.0 - pip: - - config - - scikit-learn - - pandas - - mlflow - - prefect==2.* - - fastapi - - uvicorn[standard] - - pydantic - - pytest - - nox - - typer[all] - - evidently # per il drift monitoring + - aiosqlite==0.21.0 + - alembic==1.15.2 + - annotated-types==0.7.0 + - anyio==4.6.0 + - appdirs==1.4.4 + - apprise==1.9.3 + - asgi-lifespan==2.1.0 + - async-timeout==5.0.1 + - asyncpg==0.29.0 + - blinker==1.9.0 + - certifi==2025.1.31 + - cffi==1.17.1 + - cfgv==3.4.0 + - charset-normalizer==3.4.1 + - click==8.1.8 + - cloudpickle==3.1.1 + - contourpy==1.3.2 + - coolname==2.2.0 + - croniter==5.0.1 + - cryptography==44.0.2 + - cycler==0.12.1 + - databricks-sdk==0.50.0 + - dateparser==1.2.1 + - deprecated==1.2.18 + - deprecation==2.1.0 + - distro==1.9.0 + - dnspython==2.7.0 + - docker==7.1.0 + - durationpy==0.9 + - dynaconf==3.2.10 + - email-validator==2.2.0 + - evidently==0.4.40 + - faker==37.1.0 + - fastapi==0.115.12 + - flask==3.1.0 + - fonttools==4.57.0 + - fsspec==2025.3.2 + - gitdb==4.0.12 + - gitpython==3.1.44 + - google-auth==2.39.0 + - graphene==3.4.3 + - graphql-core==3.2.6 + - graphql-relay==3.2.0 + - greenlet==3.2.0 + - griffe==1.7.2 + - gunicorn==23.0.0 + - h11==0.14.0 + - h2==4.2.0 + - hpack==4.1.0 + - httpcore==1.0.8 + - httptools==0.6.4 + - httpx==0.28.1 + - humanize==4.12.2 + - hyperframe==6.1.0 + - identify==2.6.10 + - idna==3.10 + - importlib-metadata==8.6.1 + - importlib-resources==6.4.5 + - iniconfig==2.1.0 + - iterative-telemetry==0.0.10 + - itsdangerous==2.2.0 + - jinja2-humanize-extension==0.4.0 + - joblib==1.4.2 + - jsonpatch==1.33 + - jsonpointer==3.0.0 + - jsonschema==4.23.0 + - jsonschema-specifications==2024.10.1 + - kiwisolver==1.4.8 + - kubernetes==31.0.0 + - litestar==2.15.2 + - litestar-htmx==0.4.1 + - mako==1.3.10 + - markdown==3.8 + - markdown-it-py==3.0.0 + - matplotlib==3.10.1 + - mdurl==0.1.2 + - mlflow==2.21.3 + - mlflow-skinny==2.21.3 + - msgspec==0.19.0 + - multidict==6.4.3 + - multipart==1.2.1 + - mypy-extensions==1.0.0 + - nltk==3.9.1 + - nodeenv==1.9.1 + - numpy==2.0.2 + - oauthlib==3.2.2 + - opentelemetry-api==1.32.1 + - opentelemetry-sdk==1.32.1 + - opentelemetry-semantic-conventions==0.53b1 + - orjson==3.10.16 + - pandas==2.2.3 + - pathspec==0.12.1 + - patsy==1.0.1 + - pendulum==2.1.2 + - pillow==11.2.1 + - plotly==5.24.1 + - polyfactory==2.21.0 + - pre-commit==4.2.0 + - prefect==2.20.17 + - protobuf==5.29.4 + - pyarrow==19.0.1 + - pyasn1==0.6.1 + - pyasn1-modules==0.4.2 + - pycparser==2.22 + - pydantic==2.11.3 + - pydantic-core==2.33.1 + - pyparsing==3.2.3 + - pytest==8.3.5 + - python-dotenv==1.1.0 + - python-multipart==0.0.20 + - python-slugify==8.0.4 + - pytz==2024.2 + - pytzdata==2020.1 + - pyyaml==6.0.2 + - readchar==4.2.1 + - referencing==0.36.2 + - regex==2024.11.6 + - requests==2.32.3 + - requests-oauthlib==2.0.0 + - rfc3339-validator==0.1.4 + - rich==13.9.4 + - rich-click==1.8.8 + - rpds-py==0.24.0 + - rsa==4.9.1 + - ruamel-yaml==0.18.10 + - ruamel-yaml-clib==0.2.12 + - scikit-learn==1.6.1 + - scipy==1.15.2 + - shellingham==1.5.4 + - smmap==5.0.2 + - sniffio==1.3.1 + - sqlalchemy==2.0.35 + - sqlparse==0.5.3 + - starlette==0.46.2 + - statsmodels==0.14.4 + - tenacity==9.1.2 + - text-unidecode==1.3 + - threadpoolctl==3.6.0 + - toml==0.10.2 + - tqdm==4.67.1 + - typer==0.15.2 + - typing-extensions==4.13.2 + - typing-inspect==0.9.0 + - typing-inspection==0.4.0 + - tzdata==2025.2 + - tzlocal==5.3.1 + - ujson==5.10.0 + - urllib3==2.4.0 + - uuid6==2024.7.10 + - uvicorn==0.34.1 + - uvloop==0.21.0 + - watchdog==6.0.0 + - watchfiles==1.0.5 + - websocket-client==1.8.0 + - websockets==13.1 + - werkzeug==3.1.3 + - wrapt==1.17.2 diff --git a/Academy/AI/2-ML-Model-Lifecycle/load_data.py b/Academy/AI/2-ML-Model-Lifecycle/load_data.py deleted file mode 100644 index 32fc1ac..0000000 --- a/Academy/AI/2-ML-Model-Lifecycle/load_data.py +++ /dev/null @@ -1,6 +0,0 @@ -from sklearn.datasets import load_wine - -data, y = load_wine(as_frame=True) - -data.head() - diff --git a/Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb b/Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb index fd968c1..a80094e 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb +++ b/Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb @@ -60,26 +60,157 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "6d5b1b79", "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "head", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mKeyError\u001b[39m Traceback (most recent call last)", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:57\u001b[39m, in \u001b[36mBunch.__getattr__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 56\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:42\u001b[39m, in \u001b[36mBunch.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 38\u001b[39m warnings.warn(\n\u001b[32m 39\u001b[39m \u001b[38;5;28mself\u001b[39m._deprecated_key_to_warnings[key],\n\u001b[32m 40\u001b[39m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[32m 41\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m42\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m().\u001b[34m__getitem__\u001b[39m(key)\n", - "\u001b[31mKeyError\u001b[39m: 'head'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdatasets\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load_wine\n\u001b[32m 3\u001b[39m data = load_wine(as_frame=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m data.head()\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/lab_env_2/lib/python3.11/site-packages/sklearn/utils/_bunch.py:59\u001b[39m, in \u001b[36mBunch.__getattr__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[key]\n\u001b[32m 58\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m59\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(key)\n", - "\u001b[31mAttributeError\u001b[39m: head" - ] + "data": { + "text/html": [ + "

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alcoholmalic_acidashalcalinity_of_ashmagnesiumtotal_phenolsflavanoidsnonflavanoid_phenolsproanthocyaninscolor_intensityhueod280/od315_of_diluted_winesproline
014.231.712.4315.6127.02.803.060.282.295.641.043.921065.0
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" + ], + "text/plain": [ + " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", + "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", + "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", + "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", + "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", + "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", + "\n", + " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", + "0 3.06 0.28 2.29 5.64 1.04 \n", + "1 2.76 0.26 1.28 4.38 1.05 \n", + "2 3.24 0.30 2.81 5.68 1.03 \n", + "3 3.49 0.24 2.18 7.80 0.86 \n", + "4 2.69 0.39 1.82 4.32 1.04 \n", + "\n", + " od280/od315_of_diluted_wines proline \n", + "0 3.92 1065.0 \n", + "1 3.40 1050.0 \n", + "2 3.17 1185.0 \n", + "3 3.45 1480.0 \n", + "4 2.93 735.0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -90,6 +221,39 @@ "data.data.head()" ] }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2884b86d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['alcohol',\n", + " 'malic_acid',\n", + " 'ash',\n", + " 'alcalinity_of_ash',\n", + " 'magnesium',\n", + " 'total_phenols',\n", + " 'flavanoids',\n", + " 'nonflavanoid_phenols',\n", + " 'proanthocyanins',\n", + " 'color_intensity',\n", + " 'hue',\n", + " 'od280/od315_of_diluted_wines',\n", + " 'proline']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.feature_names" + ] + }, { "cell_type": "markdown", "id": "28e5c13e", diff --git a/Academy/AI/2-ML-Model-Lifecycle/noxfile.py b/Academy/AI/2-ML-Model-Lifecycle/noxfile.py index 8dac37e..1f11bf7 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/noxfile.py +++ b/Academy/AI/2-ML-Model-Lifecycle/noxfile.py @@ -1,40 +1,79 @@ +# noxfile.py — Local CI/CD simulation using Nox sessions import nox + +# Reuse virtualenvs across runs to avoid reinstallation nox.options.reuse_existing_virtualenvs = True +# Shared environment for all sessions +nox.options.envdir = ".nox/shared" +# Python version to use in all sessions PYTHON = "3.11" -# ----- helper ------------------------------------------------ +# ------------------------------------------------------------ +# Shared setup for installing local package +# ------------------------------------------------------------ + + def install_project(session): - session.install("-e", ".") # pacchetto src/ + """ + Install the local src/ package in editable mode. + """ + session.install("-e", ".") + + +# ------------------------------------------------------------ +# Core CI checks: linting and unit tests +# ------------------------------------------------------------ + -# ----------------- CI step di base -------------------------- @nox.session(python=PYTHON) def lint(session): - session.install("pre-commit") - session.run("pre-commit", "run", "--all-files", "--show-diff-on-failure") + """ + Run pre-commit hooks for linting and formatting. + """ + session.run("pre-commit", "run", "--all-files", "--hook-stage", "manual") @nox.session(python=PYTHON) def tests(session): - session.install("pytest", "scikit-learn", "pandas") + """ + Run unit tests using pytest. + """ install_project(session) session.run("pytest", "-q") -# ----------------- WORKFLOW SIMULATI ------------------------ + +# ------------------------------------------------------------ +# Simulated ML workflows triggered by Git pushes +# ------------------------------------------------------------ + + @nox.session(python=PYTHON) def feature_pipeline(session): - """Hyper-parameter sweep – solo metadati.""" + """ + Simulates a push to feature/*: + - Runs hyperparameter sweep (no model saved). + """ install_project(session) - # lancia Prefect flow - session.run("python", "-m", "src.pipeline", "train_sweep") + session.run("python", "-m", "src.pipeline_ml", "train_experiment") + @nox.session(python=PYTHON) def develop_pipeline(session): - """Train con best hp – salva anche il modello.""" + """ + Simulates a push to develop: + - Trains the model using best hyperparameters. + - Logs metrics and saves model to MLflow. + """ install_project(session) - session.run("python", "-m", "src.pipeline", "train_best") + session.run("python", "-m", "src.pipeline_ml", "train_best") + @nox.session(python=PYTHON) -def master_pipeline(session): - """Serve il miglior modello salvato.""" +def main_pipeline(session): + """ + Simulates a push to main: + - Downloads the best saved model. + - Serves the model through FastAPI. + """ install_project(session) - session.run("python", "-m", "src.pipeline", "serve_best") + session.run("python", "-m", "src.pipeline_ml", "serve_best") diff --git a/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml b/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml index 176fbe7..734e0c8 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml +++ b/Academy/AI/2-ML-Model-Lifecycle/pyproject.toml @@ -9,18 +9,11 @@ description = "End-to-end MLOps lab pipeline" authors = [{ name = "ranl", email = "lorenzo.randazzo@kiratech.ch" }] readme = "README.md" license = { text = "MIT" } -dependencies = [ - "scikit-learn", - "pandas", - "mlflow", - "prefect", - "fastapi", - "uvicorn[standard]", - "pydantic", - "typer[all]", - "evidently", -] +dynamic = ["dependencies"] requires-python = ">=3.11" [tool.setuptools] packages = ["src"] + +[tool.setuptools.dynamic] +dependencies = {file = ["requirements.txt"]} diff --git a/Academy/AI/2-ML-Model-Lifecycle/requirements.txt b/Academy/AI/2-ML-Model-Lifecycle/requirements.txt new file mode 100644 index 0000000..05e2720 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/requirements.txt @@ -0,0 +1,212 @@ +aiosqlite==0.21.0 +alembic==1.15.2 +annotated-types==0.7.0 +anyio==4.6.0 +appdirs==1.4.4 +appnope==0.1.2 +apprise==1.9.3 +argcomplete==1.12.3 +asgi-lifespan==2.1.0 +asttokens==3.0.0 +async-timeout==5.0.1 +asyncpg==0.29.0 +attrs==25.3.0 +backports.functools_lru_cache==2.0.0 +blinker==1.9.0 +cachetools==5.5.2 +certifi==2025.1.31 +cffi==1.17.1 +cfgv==3.4.0 +chardet==5.2.0 +charset-normalizer==3.4.1 +click==8.1.8 +cloudpickle==3.1.1 +colorama==0.4.6 +colorlog==5.0.1 +comm==0.2.1 +contourpy==1.3.2 +coolname==2.2.0 +croniter==5.0.1 +cryptography==44.0.2 +cycler==0.12.1 +databricks-sdk==0.50.0 +dateparser==1.2.1 +debugpy==1.8.11 +decorator==5.1.1 +dependency-groups==1.3.0 +Deprecated==1.2.18 +deprecation==2.1.0 +distlib==0.3.8 +distro==1.9.0 +dnspython==2.7.0 +docker==7.1.0 +durationpy==0.9 +dynaconf==3.2.10 +email_validator==2.2.0 +evidently==0.4.40 +exceptiongroup==1.2.2 +executing==0.8.3 +Faker==37.1.0 +fastapi==0.115.12 +filelock==3.18.0 +Flask==3.1.0 +fonttools==4.57.0 +fsspec==2025.3.2 +gitdb==4.0.12 +GitPython==3.1.44 +google-auth==2.39.0 +graphene==3.4.3 +graphql-core==3.2.6 +graphql-relay==3.2.0 +graphviz==0.20.3 +greenlet==3.2.0 +griffe==1.7.2 +gunicorn==23.0.0 +h11==0.14.0 +h2==4.2.0 +hpack==4.1.0 +httpcore==1.0.8 +httptools==0.6.4 +httpx==0.28.1 +humanize==4.12.2 +hyperframe==6.1.0 +identify==2.6.10 +idna==3.10 +importlib_metadata==8.6.1 +importlib_resources==6.4.5 +iniconfig==2.1.0 +ipykernel==6.29.5 +ipython==9.1.0 +ipython_pygments_lexers==1.1.1 +iterative-telemetry==0.0.10 +itsdangerous==2.2.0 +jedi==0.19.2 +Jinja2==3.1.6 +jinja2-humanize-extension==0.4.0 +joblib==1.4.2 +jsonpatch==1.33 +jsonpointer==3.0.0 +jsonschema==4.23.0 +jsonschema-specifications==2024.10.1 +jupyter_client==8.6.3 +jupyter_core==5.7.2 +kiwisolver==1.4.8 +kubernetes==31.0.0 +litestar==2.15.2 +litestar-htmx==0.4.1 +Mako==1.3.10 +Markdown==3.8 +markdown-it-py==3.0.0 +MarkupSafe==3.0.2 +matplotlib==3.10.1 +matplotlib-inline==0.1.6 +mdurl==0.1.2 +mlflow==2.21.3 +mlflow-skinny==2.21.3 +msgspec==0.19.0 +multidict==6.4.3 +multipart==1.2.1 +mypy-extensions==1.0.0 +nest_asyncio==1.6.0 +nltk==3.9.1 +nodeenv==1.9.1 +nox==2025.2.9 +numpy==2.0.2 +oauthlib==3.2.2 +opentelemetry-api==1.32.1 +opentelemetry-sdk==1.32.1 +opentelemetry-semantic-conventions==0.53b1 +orjson==3.10.16 +packaging==24.2 +pandas==2.2.3 +parso==0.8.4 +pathspec==0.12.1 +patsy==1.0.1 +pendulum==2.1.2 +pexpect==4.8.0 +pickleshare==0.7.5 +pillow==11.2.1 +pip==25.0.1 +platformdirs==4.3.7 +plotly==5.24.1 +pluggy==1.5.0 +polyfactory==2.21.0 +pre_commit==4.2.0 +prefect==2.20.17 +prompt_toolkit==3.0.43 +protobuf==5.29.4 +psutil==5.9.0 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pyarrow==19.0.1 +pyasn1==0.6.1 +pyasn1_modules==0.4.2 +pycparser==2.22 +pydantic==2.11.3 +pydantic_core==2.33.1 +Pygments==2.19.1 +pyparsing==3.2.3 +pyproject-api==1.9.0 +pytest==8.3.5 +python-dateutil==2.9.0.post0 +python-dotenv==1.1.0 +python-multipart==0.0.20 +python-slugify==8.0.4 +pytz==2024.2 +pytzdata==2020.1 +PyYAML==6.0.2 +pyzmq==26.2.0 +readchar==4.2.1 +referencing==0.36.2 +regex==2024.11.6 +requests==2.32.3 +requests-oauthlib==2.0.0 +rfc3339-validator==0.1.4 +rich==13.9.4 +rich-click==1.8.8 +rpds-py==0.24.0 +rsa==4.9.1 +ruamel.yaml==0.18.10 +ruamel.yaml.clib==0.2.12 +scikit-learn==1.6.1 +scipy==1.15.2 +setuptools==78.1.0 +shellingham==1.5.4 +six==1.17.0 +smmap==5.0.2 +sniffio==1.3.1 +SQLAlchemy==2.0.35 +sqlparse==0.5.3 +stack-data==0.2.0 +starlette==0.46.2 +statsmodels==0.14.4 +tenacity==9.1.2 +text-unidecode==1.3 +threadpoolctl==3.6.0 +toml==0.10.2 +tomli==2.2.1 +tornado==6.4.2 +tox==4.25.0 +tqdm==4.67.1 +traitlets==5.14.3 +typer==0.15.2 +typing_extensions==4.13.2 +typing-inspect==0.9.0 +typing-inspection==0.4.0 +tzdata==2025.2 +tzlocal==5.3.1 +ujson==5.10.0 +urllib3==2.4.0 +uuid6==2024.7.10 +uvicorn==0.34.1 +uvloop==0.21.0 +virtualenv==20.30.0 +watchdog==6.0.0 +watchfiles==1.0.5 +wcwidth==0.2.5 +websocket-client==1.8.0 +websockets==13.1 +Werkzeug==3.1.3 +wheel==0.45.1 +wrapt==1.17.2 +zipp==3.21.0 diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/config.py b/Academy/AI/2-ML-Model-Lifecycle/src/config.py index d757d3d..56f3add 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/config.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/config.py @@ -1,13 +1,54 @@ -# src/config.py +""" +src/config.py +Configuration module for the Wine Quality pipeline. + +- Defines global paths, MLflow settings, artifact naming, and Evidently monitoring endpoints. +- Uses Pydantic's BaseModel for structured configuration. +""" + from pathlib import Path + from pydantic import BaseModel + class Settings(BaseModel): + """ + Centralized application settings. + + Attributes: + project_root (Path): Root directory of the project. + artifacts_dir (Path): Directory where artifacts (models, reports) will be stored. + model_name (str): Default filename for the trained model artifact. + mlflow_uri (str): URI where MLflow tracking server or local files are stored. + experiment_name (str): Name of the MLflow experiment. + evidently_url (str): Base URL where the Evidently UI dashboard is running. + evidently_project (str): Project name under Evidently for monitoring. + """ + + # Root of the project (assumes config.py is in src/) project_root: Path = Path(__file__).resolve().parents[1] + + # Directory for storing artifacts (models, drift reports, etc.) artifacts_dir: Path = project_root / "artifacts" + + # Default model filename for serialization model_name: str = "wine_rf.pkl" + + # MLflow tracking URI (using local file storage in 'mlruns/') mlflow_uri: str = f"file:{project_root/'mlruns'}" - experiment_name: str = "WineSweepDemo" + # MLflow experiment name for tracking runs + experiment_name: str = "WineSweepDemo" + + # Evidently monitoring - URL to the local Evidently UI server + evidently_url: str = "http://localhost:8000" + + # Evidently monitoring - Name of the project for batch drift tracking + evidently_project: str = "wine_quality_monitoring" + + +# Singleton instance used throughout the project CFG = Settings() + +# Ensure artifacts directory exists (create if it doesn't) CFG.artifacts_dir.mkdir(exist_ok=True) diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/data.py b/Academy/AI/2-ML-Model-Lifecycle/src/data.py index 66d4946..d8a5843 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/data.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/data.py @@ -1,8 +1,104 @@ -# src/data.py +""" +src/data.py +Data handling module for the Wine Quality pipeline. + +Provides: +- data_collection: Loads the wine dataset from scikit-learn. +- new_data_collection: Simulates a new batch with data drift in alcohol. +- data_preparation: Prepares the dataset (renames columns, performs train-test split). +""" + +import random + import pandas as pd +import typer from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split +from src.utils import get_logger + +app = typer.Typer(add_completion=False) + -def load(split: float = .2, seed: int = 42): +def data_collection() -> tuple[pd.DataFrame, pd.Series]: + """ + Loads the wine dataset. + + Returns: + tuple: + - X (pd.DataFrame): Feature matrix. + - y (pd.Series): Target labels. + """ + logger = get_logger() + logger.info("Loading Wine dataset...") X, y = load_wine(return_X_y=True, as_frame=True) - return train_test_split(X, y, test_size=split, random_state=seed) + logger.info( + f"Wine dataset loaded with {X.shape[0]} samples and {X.shape[1]} features." + ) + return (X, y) + + +def new_data_collection() -> pd.DataFrame: + """ + Loads the wine dataset and introduces data drift in the 'alcohol' feature. + + Randomly perturbs alcohol values within a multiplier range + to simulate a distributional change for monitoring. + + Returns: + pd.DataFrame: Drifted dataset (features only, no target). + """ + X, _ = data_collection() + df = X.copy() + + logger = get_logger() + logger.info("Generating drifted data batch...") + + # Filter high alcohol wines + df_filtered = df[df["alcohol"] > 13] + + # Apply random scaling to simulate drift + rnd_drift = random.uniform(1.03, 1.13) + df_filtered["alcohol"] = df["alcohol"] * rnd_drift + logger.warning(f"Random drif generated for alchol with coefficient {rnd_drift}.") + # Sample up to 100 instances + df_current = df_filtered.sample(min(100, len(df_filtered)), random_state=0) + return df_current + + +def data_preparation( + dataset: tuple[pd.DataFrame, pd.Series], split: float = 0.2, seed: int = 42 +) -> tuple[pd.DataFrame, pd.DataFrame, pd.Series, pd.Series]: + """ + Prepares the dataset for training: + - Renames any problematic columns (e.g., replaces slashes). + - Splits the dataset into train and test sets. + + Args: + dataset (tuple): Tuple containing (X, y). + split (float, optional): Proportion of the dataset to include in the test split (default: 0.2). + seed (int, optional): Random seed for reproducibility (default: 42). + + Returns: + tuple: + - X_train (pd.DataFrame): Training features. + - X_test (pd.DataFrame): Testing features. + - y_train (pd.Series): Training labels. + - y_test (pd.Series): Testing labels. + """ + X, y = dataset + logger = get_logger() + # Ensure column names are safe (replace slashes with underscores) + logger.info("Replacing / with - in column names...") + X.columns = X.columns.str.replace("/", "_") + + # Split into train/test + logger.info(f"Preparing dataset: split={split}, seed={seed}...") + X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=split, random_state=seed) + logger.info( + f"Dataset split into {X_tr.shape[0]} train and {X_te.shape[0]} test samples." + ) + return (X_tr, X_te, y_tr, y_te) + + +if __name__ == "__main__": + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py b/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py index 97af243..56b6bdc 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/monitor.py @@ -1,23 +1,107 @@ -# src/monitor.py """ -Lettura delle predizioni servite, verifica di drift su Evidently -e scrittura di un report HTML. -Richiamabile via cron o manualmente: python monitor.py run +src/monitor.py +Data drift monitoring module using Evidently. + +- Compares current data to a reference dataset. +- Generates an HTML and JSON report saved to artifacts/. +- Can be called via CLI or integrated as a Prefect task. """ -import typer, pandas as pd -from evidently import Report -from evidently.metrics import DataDriftPreset -app = typer.Typer() +import json +from pathlib import Path + +import pandas as pd +import typer +from evidently.metric_preset import DataDriftPreset +from evidently.report import Report +from src.utils import get_logger +from src.config import CFG + +app = typer.Typer(add_completion=False) + + +def compute_drift( + output_json: Path = Path(CFG.artifacts_dir / "drift_report.json"), +) -> float: + """ + Computes the minimum drift score from an Evidently JSON report. + + Args: + output_json (Path, optional): Path to the JSON drift report (default: artifacts/drift_report.json). + + Returns: + float: The minimum drift score across all monitored features. + """ + logger = get_logger() + logger.info("Computing minimum drift score from report...") + + # Load the JSON report + report = json.loads(output_json.read_text()) + pvals = [] + + # Extract drift scores for each monitored column + for attribute, value in report["metrics"][1]["result"]["drift_by_columns"].items(): + pvals.append(value["drift_score"]) + + # Compute and return the minimum p-value + min_p = min(pvals) + logger.info(f"Minimum drift score detected: {min_p}.") + return min_p -@app.command() -def run(ref_csv: str = "data/reference.csv", - cur_csv: str = "data/current.csv"): - ref, cur = pd.read_csv(ref_csv), pd.read_csv(cur_csv) + +def generate_drift_report( + reference_data: pd.DataFrame, + current_data: pd.DataFrame, + output_path_html: Path, + output_path_json: Path, +) -> Path: + """ + Generates a drift report comparing current data with reference data. + + Args: + reference_data (pd.DataFrame): DataFrame containing the reference dataset. + current_data (pd.DataFrame): DataFrame containing the current dataset to monitor. + output_path_html (Path): Path to save the generated HTML report. + output_path_json (Path): Path to save the generated JSON report. + + Returns: + Path: Path to the generated JSON report (for downstream processing). + """ + logger = get_logger() + logger.info("Starting drift report generation...") + + # Initialize Evidently report with DataDriftPreset report = Report(metrics=[DataDriftPreset()]) - report.run(ref, cur) - report.save_html("artifacts/drift_report.html") - typer.echo("✅ Drift report scritto in artifacts/drift_report.html") + report.run(reference_data=reference_data, current_data=current_data) + + # Save the report as both HTML and JSON + output_path_html.parent.mkdir(exist_ok=True) + report.save_html(str(output_path_html)) + report.save_json(str(output_path_json)) + logger.info(f"Drift report saved to {output_path_html} and {output_path_json}.") + return output_path_json + + +def run( + reference_data: pd.DataFrame, + current_data: pd.DataFrame, + output_html: Path = Path(CFG.artifacts_dir / "drift_report.html"), + output_json: Path = Path(CFG.artifacts_dir / "drift_report.json"), +) -> Path: + """ + CLI entry point for generating a drift report. + + Args: + reference_data (pd.DataFrame): DataFrame containing the reference dataset. + current_data (pd.DataFrame): DataFrame containing the current dataset. + output_html (Path, optional): Output path for the HTML report (default: artifacts/drift_report.html). + output_json (Path, optional): Output path for the JSON report (default: artifacts/drift_report.json). + + Returns: + Path: Path to the generated JSON report. + """ + return generate_drift_report(reference_data, current_data, output_html, output_json) + if __name__ == "__main__": app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py deleted file mode 100644 index 322bcc4..0000000 --- a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline.py +++ /dev/null @@ -1,101 +0,0 @@ -# src/pipeline.py -"""Prefect 2 flows: - • train_sweep grid-search parallelo (solo metadati) - • train_best recupera best hp + addestra salvando modello - • serve_best scarica modello migliore e lo serve via FastAPI -""" -from itertools import product -from pathlib import Path -from subprocess import Popen -import tempfile - -import mlflow -from prefect import flow, task, get_run_logger - -from src.config import CFG -from src.train import run as train_run -from src.serve import run as serve_run - -# --------------------------------------------------------------------------- -# Tasks -# --------------------------------------------------------------------------- - - -@task -def train_task(n_estimators: int, max_depth: int | None): - return train_run(n_estimators=n_estimators, max_depth=max_depth, log_model=False) - - -@task -def find_best_params(metric: str = "accuracy") -> dict: - logger = get_run_logger() - runs = mlflow.search_runs(order_by=[f"metrics.{metric} DESC"], max_results=1) - best = runs.iloc[0] - params = { - "n_estimators": int(best["params.n_estimators"]), - "max_depth": None - if best["params.max_depth"] in ("", "None") - else int(best["params.max_depth"]), - } - logger.info(f"Best params found: {params}") - return params - - -@task -def download_best_model(metric: str = "accuracy") -> Path: - logger = get_run_logger() - EXP_ID = mlflow.get_experiment_by_name(CFG.experiment_name).experiment_id - runs = mlflow.search_runs( - experiment_ids=[EXP_ID], order_by=[f"metrics.{metric} DESC"], max_results=1 - ) - run_id = runs.iloc[0]["run_id"] - - client = mlflow.MlflowClient() - art = client.list_artifacts(run_id) - pkl_path = next(a.path for a in art if a.path.endswith(".pkl")) - - tmp_dir = tempfile.mkdtemp() - local_path = Path(client.download_artifacts(run_id, pkl_path, tmp_dir)) - logger.info(f"Downloaded best model to: {local_path}") - return local_path - - -@task -def serve_model(model_path: Path): - # Avvia l'API in un processo dedicato (non blocca il flow calling) - Popen(["python", "-m", "src.serve", "--model-path", str(model_path)]) - - -# --------------------------------------------------------------------------- -# Flows -# --------------------------------------------------------------------------- - - -@flow(name="train_sweep") -def train_sweep(): - grid = { - "n_estimators": [50, 100, 200], - "max_depth": [None, 5, 10], - } - for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): - train_task.submit(n_estimators=n_est, max_depth=m_depth) - - -@flow(name="train_best") -def train_best(): - params = find_best_params() - # unpack dict in **kwargs, salva anche il modello - train_run(**params, log_model=True) - - -@flow(name="serve_best") -def serve_best(): - model_path = download_best_model() - serve_model(model_path) - - -if __name__ == "__main__": - import sys - - # Facile CLI: python -m src.pipeline - locals()[sys.argv[1]]() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_ml.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_ml.py new file mode 100644 index 0000000..a43d425 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_ml.py @@ -0,0 +1,195 @@ +""" +src/pipeline_ml.py +Prefect 2 flows for the Wine Quality ML pipeline. + +Flows: +- train_experiment: Performs a grid search (hyperparameter sweep) without saving models. +- train_best: Trains the best model based on MLflow runs and saves the model artifact. +- serve_best: Downloads the best saved model and serves it through a FastAPI endpoint. +""" + +from itertools import product +from pathlib import Path +import tempfile + +import mlflow +from prefect import flow, task + +from src.config import CFG +from src.data import data_collection as data_coll_run, data_preparation as data_prep_run +from src.train import run as train_run +from src.serve import deploy +from src.utils import get_logger + +# --------------------------------------------------------------------------- +# Tasks +# --------------------------------------------------------------------------- + +@task +def data_collection_task() -> tuple: + """ + Collects the dataset (features and labels). + + Returns: + tuple: (X, y) dataset. + """ + return data_coll_run() + +@task +def data_preparation_task(dataset: tuple) -> tuple: + """ + Prepares the dataset for training: + - Fixes feature names. + - Splits into train/test sets. + + Args: + dataset (tuple): (X, y) dataset. + + Returns: + tuple: (X_train, X_test, y_train, y_test). + """ + return data_prep_run(dataset) + +@task +def model_training_task( + dataset: tuple, n_estimators: int, max_depth: int | None, log_model: bool = False +) -> float: + """ + Trains a RandomForest model and optionally logs it to MLflow. + + Args: + dataset (tuple): Prepared train/test splits. + n_estimators (int): Number of trees. + max_depth (int | None): Maximum depth per tree. + log_model (bool): Whether to save model artifact to MLflow. + + Returns: + float: Model accuracy. + """ + return train_run(dataset, n_estimators=n_estimators, max_depth=max_depth, log_model=log_model) + +@task +def find_best_params_task(metric: str = "accuracy") -> tuple: + """ + Retrieves the best model hyperparameters from MLflow by specified metric. + + Args: + metric (str, optional): Metric to order by (default: 'accuracy'). + + Returns: + tuple: (n_estimators, max_depth) for best model. + """ + logger = get_logger() + logger.info(f"Searching best hyperparameters by {metric}...") + + runs = mlflow.search_runs(order_by=[f"metrics.{metric} DESC"], max_results=1) + best = runs.iloc[0] + params = { + "n_estimators": int(best["params.n_estimators"]), + "max_depth": None if best["params.max_depth"] in ("", "None") else int(best["params.max_depth"]), + } + logger.info(f"Best hyperparameters found: n_estimators={params['n_estimators']}, max_depth={params['max_depth']}.") + return params["n_estimators"], params["max_depth"] + +@task +def download_best_model(metric: str = "accuracy") -> Path: + """ + Downloads the best model artifact from MLflow. + + Args: + metric (str, optional): Metric to order by (default: 'accuracy'). + + Returns: + Path: Local path to downloaded model (.pkl file). + """ + logger = get_logger() + logger.info(f"Downloading best model artifact in experiment {CFG.experiment_name}...") + + exp = mlflow.get_experiment_by_name(CFG.experiment_name) + runs = mlflow.search_runs( + experiment_ids=[exp.experiment_id], order_by=[f"metrics.{metric} DESC"], max_results=1 + ) + run_id = runs.iloc[0]["run_id"] + + client = mlflow.MlflowClient() + artifacts = client.list_artifacts(run_id) + pkl_path = next(a.path for a in artifacts if a.path.endswith(".pkl")) + + tmp_dir = tempfile.mkdtemp() + local_path = Path(client.download_artifacts(run_id, pkl_path, tmp_dir)) + logger.info(f"Downloaded best model to: {local_path}") + return local_path + +@task +def serve_model_task(model_path: Path): + """ + Deploys a FastAPI app for model inference. + + Args: + model_path (Path): Path to serialized model (.pkl). + """ + deploy(model_path=model_path) + +# --------------------------------------------------------------------------- +# Flows +# --------------------------------------------------------------------------- + +@flow(name="train_experiment") +def train_experiment(): + """ + End-to-end grid search training flow: + - Collects and prepares dataset. + - Launches multiple model training tasks for different hyperparameters. + - Only logs parameters and metrics, no model artifacts. + """ + logger = get_logger() + logger.info("Starting train_experiment flow...") + + dataset = data_collection_task() + dataset_prep = data_preparation_task(dataset) + + grid = { + "n_estimators": [1, 5, 10], + "max_depth": [None, 5, 10], + } + + for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): + model_training_task.submit( + dataset=dataset_prep, n_estimators=n_est, max_depth=m_depth + ) + +@flow(name="train_best") +def train_best(): + """ + Retrains the best model based on MLflow search: + - Finds best hyperparameters. + - Retrains model and saves it as artifact. + """ + logger = get_logger() + logger.info("Starting train_best flow...") + + dataset = data_collection_task() + dataset_prep = data_preparation_task(dataset) + n_estimators, max_depth = find_best_params_task() + model_training_task( + dataset_prep, n_estimators=n_estimators, max_depth=max_depth, log_model=True + ) + +@flow(name="serve_best") +def serve_best(): + """ + Deploys the best model as a live API: + - Downloads best model from MLflow. + - Builds FastAPI app. + - Launches serving. + """ + logger = get_logger() + logger.info("Starting serve_best flow...") + + model_path = download_best_model() + #api = build_app_task(model_path=model_path) + serve_model_task(model_path) + +if __name__ == "__main__": + import sys + locals()[sys.argv[1]]() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_monitoring.py b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_monitoring.py new file mode 100644 index 0000000..9209f54 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/pipeline_monitoring.py @@ -0,0 +1,111 @@ +""" +src/pipeline_monitoring.py +Prefect 2 flows for the Wine Quality ML pipeline. + +Flow: +- monitoring_best: Detects data drift and triggers retraining if significant drift is found. +""" + +import pandas as pd +from pathlib import Path + +from prefect import flow, task +from src.data import data_collection as data_coll_run, new_data_collection as new_data_coll_run +from src.monitor import run as monitoring_run, compute_drift +from src.pipeline_ml import train_best +from src.utils import get_logger + +# --------------------------------------------------------------------------- +# Tasks +# --------------------------------------------------------------------------- + +@task +def data_collection_task() -> tuple: + """ + Collects the original reference dataset. + + Returns: + tuple: (X, y) dataset. + """ + return data_coll_run() + +@task +def get_drift_data_task() -> pd.DataFrame: + """ + Generates a new dataset with simulated data drift. + + Returns: + pd.DataFrame: New batch of data with potential drift. + """ + return new_data_coll_run() + +@task +def create_drift_report_task(current_data: pd.DataFrame, reference_data: pd.DataFrame) -> Path: + """ + Creates a drift monitoring report comparing current batch vs reference. + + Args: + current_data (pd.DataFrame): Current dataset batch. + reference_data (pd.DataFrame): Original reference dataset. + + Returns: + Path: Path to the generated JSON drift report. + """ + return monitoring_run(current_data=current_data, reference_data=reference_data) + +@task +def compute_drift_task(output_json: Path) -> float: + """ + Computes the minimum drift score from the JSON report. + + Args: + output_json (Path): Path to the generated JSON report. + + Returns: + float: Minimum p-value detected across all features. + """ + return compute_drift(output_json) + +@task +def trigger_if_drift_task(min_p: float, threshold: float = 0.05): + """ + Triggers retraining if minimum drift p-value is below the threshold. + + Args: + min_p (float): Minimum p-value detected. + threshold (float, optional): Threshold below which retraining is triggered (default: 0.05). + """ + logger = get_logger() + logger.info("Starting monitoring_best flow...") + + if min_p <= threshold: + logger.warning(f"Drift detected (min_p={min_p}). Launching retraining...") + train_best() + else: + logger.warning(f"No drift detected (min_p={min_p}).") + +# --------------------------------------------------------------------------- +# Flows +# --------------------------------------------------------------------------- + +@flow(name="monitoring_best") +def monitoring_best(): + """ + Drift monitoring flow: + - Collects reference and new batch datasets. + - Generates drift report (HTML + JSON). + - Computes drift metrics. + - Triggers model retraining if drift is significant. + """ + logger = get_logger() + logger.info("Starting monitoring_best flow...") + + training_data, _ = data_collection_task() + new_data = get_drift_data_task() + report_json = create_drift_report_task(current_data=new_data, reference_data=training_data) + p_val = compute_drift_task(report_json) + trigger_if_drift_task(min_p=p_val) + +if __name__ == "__main__": + # Serve this flow with a cron schedule (here: every minute) + monitoring_best.serve(cron="* * * * *") diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py b/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py deleted file mode 100644 index 77875ec..0000000 --- a/Academy/AI/2-ML-Model-Lifecycle/src/retrain.py +++ /dev/null @@ -1,25 +0,0 @@ -# src/retrain.py -""" -Retrain se il drift supera soglia; esegue train.run() con parametri di default. -""" -import json, subprocess, typer, pathlib - -DRIFT_PATH = pathlib.Path("artifacts/drift_report.html") - -app = typer.Typer() - -@app.command() -def run(threshold: float = 0.6): - from bs4 import BeautifulSoup - drift = 0.0 - if DRIFT_PATH.exists(): - soup = BeautifulSoup(DRIFT_PATH.read_text(), "html.parser") - drift = float(soup.select_one("#drift_score").text) - if drift > threshold: - typer.echo(f"⚠️ Drift {drift:.2f} > {threshold}: retraining…") - subprocess.run(["python", "-m", "src.train", "run"]) - else: - typer.echo(f"👍 Drift ok ({drift:.2f} ≤ {threshold})") - -if __name__ == "__main__": - app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py index fc7b4e3..3f0c0fb 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/serve.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/serve.py @@ -1,5 +1,16 @@ -# src/serve.py -"""Inferenza FastAPI: serve un modello RF salvato con joblib.""" +""" +src/serve.py +FastAPI inference service for the Wine Quality model. + +- Loads a RandomForestClassifier model (joblib). +- Serves predictions via a REST API endpoint. +- CLI options allow flexible deployment or local testing. + +Endpoints: +- POST /predict : Accepts wine features and returns the predicted class and probabilities. +""" + +import subprocess from pathlib import Path from typing import Optional @@ -9,15 +20,29 @@ from fastapi import FastAPI from pydantic import BaseModel -from src.config import CFG +from src.utils import get_logger -app_cli = typer.Typer(add_completion=False) +app = typer.Typer(add_completion=False) def build_app(model_path: Path) -> FastAPI: + """ + Constructs the FastAPI app with the loaded model. + + Args: + model_path (Path): Path to the trained model file (.pkl). + + Returns: + FastAPI: Configured FastAPI app with one /predict endpoint. + """ + # Load trained model model = joblib.load(model_path) class Features(BaseModel): + """ + Request body schema for prediction. + """ + alcohol: float malic_acid: float ash: float @@ -28,39 +53,79 @@ class Features(BaseModel): nonflavanoid_phenols: float proanthocyanins: float color_intensity: float - hue: float + hue: float od280_od315_of_diluted_wines: float proline: float + # Initialize FastAPI app api = FastAPI(title="Wine Quality Inference") @api.post("/predict") def predict(feat: Features): + """ + Predicts the wine quality class based on provided features. + + Args: + feat (Features): Input wine features. + + Returns: + dict: Predicted class and probabilities for each class. + """ import pandas as pd + # Convert input features to DataFrame X = pd.DataFrame([feat.dict()]) + + # Predict class and class probabilities proba = model.predict_proba(X)[0].tolist() - return {"class": int(model.predict(X)[0]), "proba": proba} + predicted_class = int(model.predict(X)[0]) + + return {"class": predicted_class, "proba": proba} return api -@app_cli.command() +def deploy(model_path: Path, host: str = "0.0.0.0", port: int = 9000): + """ + Deploys the given FastAPI app with Uvicorn. + + Args: + api (FastAPI): The app to serve. + host (str, optional): Host IP address to bind (default: 0.0.0.0). + port (int, optional): Port number to expose (default: 9000). + """ + logger = get_logger() + logger.info(f"Building FastAPI app for model from {model_path}...") + logger.info(f"Starting Uvicorn server on {host}:{port}...") + subprocess.Popen(["python", "-m", "src.serve", "--model-path", str(model_path)]) + + +@app.command() def run( model_path: Optional[Path] = typer.Option( None, "--model-path", "-m", - help="Percorso del modello .pkl; default = ultimo train locale.", + help="Path to the .pkl model file.", ), host: str = "0.0.0.0", - port: int = 8000, + port: int = 9000, ): - """Avvia l'API sul modello indicato (o su quello di default).""" - path = model_path or (CFG.artifacts_dir / CFG.model_name) - api = build_app(path) + """ + CLI entry point for running the API locally. + + - Loads the model from the given path (or default artifacts directory). + - Builds the FastAPI app. + - Serves the API with Uvicorn. + + Args: + model_path (Optional[Path], optional): Path to the .pkl model file. + host (str, optional): Host IP to bind (default: 0.0.0.0). + port (int, optional): Port to expose the API (default: 9000). + """ + api = build_app(model_path) uvicorn.run(api, host=host, port=port) if __name__ == "__main__": - app_cli() + app() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py b/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py deleted file mode 100644 index 91e141c..0000000 --- a/Academy/AI/2-ML-Model-Lifecycle/src/sweep.py +++ /dev/null @@ -1,58 +0,0 @@ -# src/sweep.py -""" -Train-sweep demo -——————— -• Ciclo su piccoli hyper-parametri RF -• Log parametri + accuracy su MLflow -• Flow tracciata sulla UI di Prefect 2 (Orion) -""" - -from itertools import product - -import mlflow -from prefect import flow, task -from sklearn.datasets import load_wine -from sklearn.ensemble import RandomForestClassifier -from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split - -# -------------------------------------------------------------------- -# configura MLflow ➜ cartella locale "mlruns" nella root del progetto -mlflow.set_tracking_uri("file:mlruns") - -# -------------------------------------------------------------------- -@task(log_prints=True) -def train_once(n_estimators: int, max_depth: int | None): - """Un singolo addestramento + log su MLflow.""" - X, y = load_wine(return_X_y=True, as_frame=True) - X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42) - - print(f"Training with n_estimators={n_estimators}, max_depth={max_depth}") - with mlflow.start_run(): - mlflow.log_param("n_estimators", n_estimators) - mlflow.log_param("max_depth", max_depth) - - model = RandomForestClassifier( - n_estimators=n_estimators, max_depth=max_depth, random_state=0 - ).fit(X_tr, y_tr) - - acc = accuracy_score(y_te, model.predict(X_te)) - mlflow.log_metric("accuracy", acc) - - return acc - - -@flow(name="train_sweep") -def train_sweep(): - """Grid-search minimal su due hyper-parametri RF.""" - grid = { - "n_estimators": [1, 10, 100], - "max_depth": [None, 5, 10], - } - - for n_est, m_depth in product(grid["n_estimators"], grid["max_depth"]): - train_once.submit(n_estimators=n_est, max_depth=m_depth) # .submit = task async, appare come sub-run - - -if __name__ == "__main__": - train_sweep() diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/train.py b/Academy/AI/2-ML-Model-Lifecycle/src/train.py index 6dfbbd9..e700190 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/src/train.py +++ b/Academy/AI/2-ML-Model-Lifecycle/src/train.py @@ -1,61 +1,111 @@ -# src/train.py -"""Un singolo ciclo di training + logging su MLflow.""" +""" +src/train.py +Model training module for the Wine Quality pipeline. -from pathlib import Path +- Trains a RandomForestClassifier on provided dataset splits. +- Logs parameters, metrics, and optionally the trained model to MLflow. +- Can be executed both via CLI (Typer) or imported in Prefect flows. +""" import joblib import mlflow import typer -from sklearn.datasets import load_wine from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score -from sklearn.model_selection import train_test_split - from src.config import CFG +from src.utils import get_logger + app = typer.Typer(add_completion=False) -# Configura il backend MLflow (cartella "mlruns" nella root) +# Configure MLflow tracking (uses the experiment defined in CFG) mlflow.set_tracking_uri(CFG.mlflow_uri) -mlflow.set_experiment(CFG.experiment_name) +mlflow.set_experiment(CFG.experiment_name) + + +def train_model( + dataset_split: tuple, n_estimators: int, max_depth: int | None +) -> tuple: + """ + Trains a RandomForest model on the provided dataset. + + Args: + dataset_split (tuple): Tuple of (X_train, X_test, y_train, y_test) splits. + n_estimators (int): Number of trees in the forest. + max_depth (int | None): Maximum depth of the trees (None means no limit). + + Returns: + tuple: + - model (RandomForestClassifier): Trained model. + - acc (float): Accuracy on the test set. + """ + logger = get_logger() + logger.info( + f"Training RandomForest with n_estimators={n_estimators}, max_depth={max_depth}..." + ) -def _train_core(n_estimators: int, max_depth: int | None): - """Ritorna modello addestrato e accuracy.""" - X, y = load_wine(return_X_y=True, as_frame=True) - X.columns = X.columns.str.replace('/', '_') - X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=42) + X_tr, X_te, y_tr, y_te = dataset_split + # Train the RandomForest model model = RandomForestClassifier( n_estimators=n_estimators, max_depth=max_depth, random_state=0 ).fit(X_tr, y_tr) + # Compute accuracy on the test set acc = accuracy_score(y_te, model.predict(X_te)) + logger.info(f"Model trained. Accuracy={acc:.4f}.") return model, acc -@app.command() def run( + dataset_split: tuple, n_estimators: int = 200, max_depth: int | None = None, log_model: bool = typer.Option( - True, "--log-model/--no-log-model", help="Salva anche il modello su MLflow." + True, + "--log-model/--no-log-model", + help="Whether to log the model artifact to MLflow.", ), ) -> float: - """Addestra e logga parametri + metriche (+ modello opzionale).""" + """ + Main training entry point. + + - Trains a model using specified hyperparameters. + - Logs training parameters, accuracy metric, and optionally the trained model to MLflow. + + Args: + dataset_split (tuple): Tuple of (X_train, X_test, y_train, y_test) splits. + n_estimators (int, optional): Number of trees (default: 200). + max_depth (int | None, optional): Maximum tree depth (default: None, unlimited). + log_model (bool, optional): If True, logs the model artifact to MLflow (default: True). + + Returns: + float: Accuracy score of the trained model. + """ + logger = get_logger() + logger.info( + f"Running full training pipeline (MLflow logging enabled={log_model})..." + ) + with mlflow.start_run(): + # Log hyperparameters mlflow.log_param("n_estimators", n_estimators) mlflow.log_param("max_depth", max_depth) - model, acc = _train_core(n_estimators, max_depth) + # Train the model + model, acc = train_model(dataset_split, n_estimators, max_depth) + + # Log model accuracy mlflow.log_metric("accuracy", acc) + # Optionally serialize and log the trained model if log_model: outfile = CFG.artifacts_dir / CFG.model_name joblib.dump(model, outfile) mlflow.log_artifact(outfile) - typer.echo(f"✅ accuracy={acc:0.3f}") - return acc # utile se importato come funzione + logger.info("Training run completed and logged to MLflow.") + return acc # Useful for Prefect tasks or external calls if __name__ == "__main__": diff --git a/Academy/AI/2-ML-Model-Lifecycle/src/utils.py b/Academy/AI/2-ML-Model-Lifecycle/src/utils.py new file mode 100644 index 0000000..a7c53ba --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/src/utils.py @@ -0,0 +1,29 @@ +""" +src/utils.py +Utility functions for the Wine Quality ML project. + +Includes: +- get_logger(): returns a Prefect logger if available, otherwise falls back to standard Python logging. +""" + +import logging + +def get_logger(): + """ + Returns a logger instance. + + - If inside a Prefect flow or task, returns the Prefect run logger. + - If outside Prefect (e.g., during unit tests or CLI usage), returns a standard Python logger. + + This allows all modules to use consistent logging behavior without crashing when Prefect context is missing. + + Returns: + Logger: Either a Prefect run logger or a standard Python logger. + """ + try: + from prefect import get_run_logger + return get_run_logger() + except RuntimeError: + # No active Prefect context; use fallback logger + logging.basicConfig(level=logging.INFO) + return logging.getLogger("fallback") diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/__init__.py b/Academy/AI/2-ML-Model-Lifecycle/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py index 7604141..88dde81 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py @@ -1,15 +1,13 @@ -"""Test basilari sul caricamento e integrità del dataset""" +from src import data -from src.data import load -import pandas as pd +def test_data_collection(): + X, y = data.data_collection() + assert not X.empty + assert len(X) == len(y) + assert "alcohol" in X.columns -def test_split_shapes(): - X_tr, X_te, y_tr, y_te = load() - assert len(X_tr) > 0 and len(X_te) > 0 - assert len(X_tr) == len(y_tr) - assert len(X_te) == len(y_te) - -def test_no_missing_values(): - X_tr, X_te, y_tr, y_te = load() - assert not X_tr.isna().any().any() - assert not X_te.isna().any().any() +def test_data_preparation(): + X, y = data.data_collection() + X_tr, X_te, y_tr, y_te = data.data_preparation((X, y), split=0.2) + assert len(X_tr) + len(X_te) == len(X) + assert len(y_tr) + len(y_te) == len(y) diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py new file mode 100644 index 0000000..5a5c545 --- /dev/null +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py @@ -0,0 +1,33 @@ +from src import serve +from src.config import CFG + +def test_build_app_and_predict(): + # Carica modello (assicurati che esista già da train_best) + model_path = CFG.artifacts_dir / CFG.model_name + assert model_path.exists(), "Trained model file is missing" + + api = serve.build_app(model_path) + + sample = { + "alcohol": 13.0, + "malic_acid": 2.0, + "ash": 2.5, + "alcalinity_of_ash": 15.0, + "magnesium": 100.0, + "total_phenols": 2.0, + "flavanoids": 2.5, + "nonflavanoid_phenols": 0.3, + "proanthocyanins": 1.9, + "color_intensity": 5.0, + "hue": 1.0, + "od280_od315_of_diluted_wines": 3.0, + "proline": 1000, + } + + from fastapi.testclient import TestClient + client = TestClient(api) + response = client.post("/predict", json=sample) + assert response.status_code == 200 + result = response.json() + assert "class" in result + assert isinstance(result["class"], int) diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py index 3ef3065..eba4412 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py @@ -1,7 +1,8 @@ -# tests/test_train.py -from src.train import RandomForestClassifier, load +from src import data, train -def test_accuracy(): - X_tr, X_te, y_tr, y_te = load() - model = RandomForestClassifier(random_state=0).fit(X_tr, y_tr) - assert model.score(X_te, y_te) > 0.8 +def test_train_model_accuracy_threshold(): + dataset = data.data_collection() + prepared = data.data_preparation(dataset) + model, acc = train.train_model(prepared, n_estimators=10, max_depth=5) + assert acc > 0.5 # basic threshold check + assert hasattr(model, "predict") From e6ce6e565820d9db51cfa79662e02c6f8b033405 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 5 May 2025 17:17:25 +0200 Subject: [PATCH 45/70] fix readme --- Academy/AI/2-ML-Model-Lifecycle/README.md | 6 ------ 1 file changed, 6 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 30e159f..463d558 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -39,12 +39,6 @@ prefect server start Open: [http://localhost:4200](http://localhost:4200) -In another terminal: - -```bash -prefect agent start -``` - --- ## 3. Run Workflows via `make` From 7fe413be36cccfe6a1578724db24ff8123869e4f Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:11:57 +0200 Subject: [PATCH 46/70] fix tests and prepare readme --- Academy/AI/2-ML-Model-Lifecycle/README.md | 332 +++++++++++++----- .../2-ML-Model-Lifecycle/notebooks/.DS_Store | Bin 6148 -> 0 bytes .../notebooks/01_explore_and_train.ipynb | 297 ---------------- .../2-ML-Model-Lifecycle/tests/test_data.py | 32 +- .../2-ML-Model-Lifecycle/tests/test_serve.py | 47 ++- .../2-ML-Model-Lifecycle/tests/test_train.py | 30 +- 6 files changed, 337 insertions(+), 401 deletions(-) delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store delete mode 100644 Academy/AI/2-ML-Model-Lifecycle/notebooks/01_explore_and_train.ipynb diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 463d558..cdbff36 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -1,136 +1,298 @@ -# Wine Quality ML Pipeline – Technical Review Guide -This README provides a step-by-step guide for reviewing the Wine Quality MLOps proof of concept. -All workflows must be tested using the `Makefile` commands only. +# Lab: From Experiments to Production with Wine Classification +Welcome to this live lab! +In the previous lab session we: +1. **Created a classification model** using **Scikit-learn**, leveraging temperature and humidity to predict rain. +2. **Integrated MLflow** to track training parameters, log evaluation metrics, and manage model versions in a structured way. +3. **Explored and compared results** through the MLflow UI interface, reviewing different configurations and loading a saved model for future predictions. ---- +In this session, you will learn how to: +1. **Train a classification model** on a real dataset using `Scikit-learn`. +2. **Manage and track your experiments** with `MLflow`. +3. **Build CI/CD-like workflows** using `Makefile` and `nox`. +4. **Build scalable and orchestrated ML pipelines** using `Prefect`. +5. **Monitor data drift over time** and trigger retraining with `Evidently`. -## 1. Environment Setup +We will follow a step-by-step approach combining **code execution**, **conceptual explanation**, and **UI walkthroughs**. -Create and activate the Conda environment (`lab_env_2`): +--- -```bash -conda env create -f environment.yaml -conda activate lab_env_2 -``` +## 1 Context and Story +A mid‑sized wine producer wants to modernise quality control. +Instead of relying on manual tasting alone, the company decides to analyse +chemical characteristics of each batch and predict the wine’s *cultivar* +(three classes) using machine learning. +The stakes: +- **Repeatable experimentation** – new blends need fresh tuning +- **Fast promotion** – once a model looks good, it must move to production quickly +- **Operational monitoring** – chemical profiles may drift with soil and weather; the system must recognise drift and retrain autonomously ---- +Our lab demonstrates how to meet these requirements **entirely on a laptop**, +using open‑source tools and zero cloud resources. -## 2. Start Required Services +--- +## 2 Dataset Overview – UCI Wine Recognition + +The lab uses the well-known [**Wine Recognition**](https://archive.ics.uci.edu/dataset/109/wine) dataset from the UCI Machine Learning Repository. +Originally collected at the Institute of Oenology and Viticulture in Italy, the dataset contains the results of a chemical analysis of **178 wine samples** from three different *cultivars* (grape varieties) grown in the same region. +Each sample is described by **13 continuous variables** that capture key chemical properties influencing flavour and quality. + +### 2.1 Features +| Feature | Description | Typical Range | +| ------------------------------ | ------------------------------------------- | ------------- | +| `alcohol` | Ethyl-alcohol content (%) | 11 – 14 % | +| `malic_acid` | Malic acid concentration (g/L) | 0.7 – 5.8 | +| `ash` | Total ash (g/100 mL) | 1.3 – 3.3 | +| `alcalinity_of_ash` | Ash alkalinity (mEq NaOH) | 10 – 30 | +| `magnesium` | Magnesium (mg/L) | 70 – 162 | +| `total_phenols` | Total phenolic content (g/L) | 0.9 – 3.9 | +| `flavanoids` | Flavonoid phenols (g/L) | 0.3 – 5.1 | +| `nonflavanoid_phenols` | Non-flavonoid phenols (g/L) | 0.1 – 0.7 | +| `proanthocyanins` | Proanthocyanins (g/L) | 0.4 – 3.6 | +| `color_intensity` | Red/blue color intensity | 1.3 – 13.0 | +| `hue` | Hue at 520 nm relative to 420 nm | 0.4 – 1.8 | +| `od280_od315_of_diluted_wines` | Optical density ratio (aromatic compounds) | 1.3 – 4.0 | +| `proline` | Proline (mg/L) – associated with mouth-feel | 270 – 1680 | + +### 2.2 Target variable: `class` +The classes are almost balanced (59 / 71 / 48 samples): +- **Class 0** – Cultivar A +- **Class 1** – Cultivar B +- **Class 2** – Cultivar C + +### 2.3 Why This Dataset Fits the Demo +1. **Compact and clean** – You can train models in seconds, perfect for live coding. +2. **Chemically interpretable features** – Easy to discuss how drifting alcohol content affects predictions. +3. **Multi-class problem** – Demonstrates probabilities and class selection in the REST API. +4. **No privacy concerns** – The data are public domain, ideal for workshops. + +### 2.4 Synthetic Drift Generation +To illustrate monitoring, the lab creates a *current batch* where `alcohol` values are artificially increased (e.g., harvest with higher sugar). +Evidently then compares this drifted batch to the original reference set, detects the shift and—if severe—triggers retraining through Prefect. -### MLflow UI -```bash -conda activate lab_env_2 -mlflow ui -``` +--- -Open: [http://localhost:5000](http://localhost:5000) +## 3 Tooling Overview – Concept + Our Usage +| Tool | Key Concept | How We Use It in the Lab | +|------|-------------|--------------------------| +| [**Makefile**](https://www.gnu.org/software/make/manual/make.html) | Declarative build/task runner | Simulates Git push events and invokes Nox sessions (`push-feature`, `push-develop`, `push-master`). | +| [**Nox**](https://nox.thea.codes/en/stable/) | Python automation with virtual/conda envs | Acts like GitHub Actions runners; executes lint, tests and Prefect flows in a reproducible and isolated way. | +| [**Prefect**](https://docs.prefect.io/v3/get-started) | Workflow orchestration + observability | Wraps training, serving and monitoring as flows; offers scheduling, retries and a UI at [http://127.0.0.1:4200](http://127.0.0.1:4200). | +| [**MLflow**](https://mlflow.org/) | Experiment tracking & model registry | Logs parameters, metrics and model artefacts; source of truth for the “best model”. UI at [http://127.0.0.1:5000](http://127.0.0.1:5000). | +| [**Scikit-learn**](https://scikit-learn.org/stable/index.html) | ML algorithms and utilities | Provides a fast RandomForest classifier for our demo. | +| [**FastAPI**](https://fastapi.tiangolo.com/) | High‑performance Python web API | Exposes the model at `/predict` with auto‑generated docs at [http://127.0.0.1:9000](http://127.0.0.1:9000). | +| [**Evidently**](https://docs.evidentlyai.com/introduction) | Data drift & model monitoring | Generates HTML/JSON drift reports; triggers retraining when drift is detected. | --- -### Prefect Server +## 4 Environment Setup +### 4.0 Requirements +This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well. +As in the previous lab, in order to execute this laboratory, you will be asked to install a set of tools common in MLOps field. -```bash -conda activate lab_env_2 -prefect server start +### 4.1 Clone the Repository +To start, clone the lab repository by running the following command in the terminal: +```sh + git clone https://github.com/kiratech/labs.git ``` -Open: [http://localhost:4200](http://localhost:4200) +### 4.2 Checkout the Lab Branch +After cloning the repository, checkout the `academy-ai` branch: +```sh + git checkout academy-ai +``` +Then, navigate to the project folder: +```sh + cd labs/Academy/AI/2-ML-Model-Lifecycle +``` +This folder contains resources related to this lab. ---- +### 4.3 Create a Virtual Environment -## 3. Run Workflows via `make` +A virtual environment allows you to isolate the project's dependencies from the system-wide ones. +In the previous lab, we first created the environment and then installed the dependencies. This time we'll do it with a single command, creating the environment with all the necessary dependencies. -All workflows must be executed via the provided `Makefile` in another terminal: -```bash -conda activate lab_env_2 +In your terminal, create a virtual environment from an existing file: + +```sh + conda env create -f environment.yaml ``` -### Feature Branch (Hyperparameter Sweep) +Activate the Virtual Environment: -```bash -make push-feature +```sh + conda activate lab_env_2 ``` -- Runs `train_experiment` on a parameter grid -- Logs metadata only (no model artifact) +You should see the `(lab_env_2)` prefix in the terminal, indicating that the virtual environment is active. ---- +### 4.4 Open the Project in VSCode -### Develop Branch (Train Best Model) +At this point, open VSCode from the file explorer or by running the command: -```bash -make push-develop +```sh + code . ``` -- Runs: `lint`, `tests`, `train_best` -- Trains model using best hyperparams from MLflow -- Logs model artifact to MLflow +### 4.5 Start the services +Open three integrated terminals and in each one activate the environemnt with `conda activate lab_env_2` and then: + +| Terminal | Command | Purpose | +|----------|---------|---------| +| T‑1 | `prefect server start` | Prefect API + UI | +| T‑2 | `mlflow ui` | Experiment tracking UI | +| T‑3 | Run workflow commands (next sections) | + +### 4.6 Open services UI +Open in the browser Prefect and MLflow at: +| Service | Address | +|----------|---------| +| Prefect | [http://127.0.0.1:4200](http://127.0.0.1:4200) | +| MLflow | [http://127.0.0.1:5000](http://127.0.0.1:5000) | --- -### Main Branch (Model Serving) +## 5 Branch Workflows -```bash -make push-main +### 5.1 Feature Branch Workflow +- **Command** +In your terminal, once the conda environment is active, run: +```sh + make push-feature ``` +- **Goal** + Explore the parameter space quickly and cheaply. No model is intended for production at this stage. + We only want evidence that “something promising” exists. +- **What the Command Simulates** + A developer pushes code to **`feature/`**. + CI/CD should **only run experiments**, producing metrics the team can compare later. +- **What It Does** + - A Nox session calls Prefect flow `train_experiment`. + - Nine RandomForest training tasks run in parallel (3  `n_estimators` × 3  `max_depth`). + - Each task logs **parameters + accuracy** to MLflow, but **does not store a model file**. + - Prefect captures task logs and execution graph. +- **What to Explore** + - MLflow UI: nine runs with different hyper‑params, no artifacts saved. + - Prefect UI: one flow, nine parallel tasks—visual confirmation of parallel runs. -- Runs: `lint`, `tests`, `serve_best` -- Starts a FastAPI app at [http://localhost:9000/predict](http://localhost:9000/predict) -- To shutdown the FastAPI server: `kill -9 $(lsof -ti :9000)` - -Example test request: +--- -```bash -curl -X POST http://localhost:9000/predict -H "Content-Type: application/json" \ - -d '{"alcohol":13.0,"malic_acid":2.0,"ash":2.5,"alcalinity_of_ash":15.0, - "magnesium":100.0,"total_phenols":2.0,"flavanoids":2.5, - "nonflavanoid_phenols":0.3,"proanthocyanins":1.9,"color_intensity":5.0, - "hue":1.0,"od280_od315_of_diluted_wines":3.0,"proline":1000}' +### 5.2 Develop Branch Workflow  +- **Command** +In your terminal, once the conda environment is active, run: +```sh + make push-develop ``` +- **Goal** + Promote the best experimental configuration, validate code quality, and log a **deployable model artifact**. +- **What the Command Simulates** + A merge/push to **`develop`**. CI/CD should lint, test, **re‑train with chosen hyper‑params**, and register the resulting model. +- **What It Does** + 1. **Lint** (`ruff via pre‑commit`) and **unit tests** (`pytest`) run first. Build stops on failure. + 2. Prefect flow `train_best` queries MLflow, grabs the run with highest accuracy. + 3. It re‑trains a RandomForest using those parameters on fresh data splits. + 4. Saves the `.pkl` artifact to `artifacts/` and logs it to MLflow. +- **What to Explore** + - Terminal: lint/test output. + - MLflow UI: a new run with an **artifact path**, this is the candidate for production. + - Prefect UI: see the “find best params” task feeding the “train” task. --- -### Monitoring + Drift Detection - -This runs a simulated batch with drift. If drift is detected (p ≤ 0.05), it retriggers training. +### 5.3 Master Branch Workflow  `make push-master` +- **Command** +In your terminal, once the conda environment is active, run: +```sh + make push-main +``` +- **Goal** + Deploy the champion model as an HTTP service usable by downstream teams. +- **What the Command Simulates** + A merge/push to **`main`**. CI/CD should perform a last sanity check, then bring the model online. +- **What It Does** + 1. Re‑runs lint and tests (quick safety net). + 2. Prefect flow `serve_best` downloads the best model artifact from MLflow. + 3. Builds a FastAPI app and launches Uvicorn on **port 9000**. +- **What to Explore** + - Swagger UI at `http://127.0.0.1:9000/docs`: live documentation, try a prediction. + - Prefect UI: a small flow made of download, build app and serve. + - MLflow UI: confirm the run ID of the served model matches the develop stage. +### 5.4 Querying the Best Model + +Once the **main branch workflow** has deployed the FastAPI service, you can send a prediction request directly from the terminal: ```bash -python -m src.pipeline_monitoring +curl -X POST http://localhost:9000/predict \ + -H "Content-Type: application/json" \ + -d '{ + "alcohol": 13.0, + "malic_acid": 2.0, + "ash": 2.5, + "alcalinity_of_ash": 15.0, + "magnesium": 100.0, + "total_phenols": 2.0, + "flavanoids": 2.5, + "nonflavanoid_phenols": 0.3, + "proanthocyanins": 1.9, + "color_intensity": 5.0, + "hue": 1.0, + "od280_od315_of_diluted_wines": 3.0, + "proline": 1000 + }' ``` -Generates: -- A deployment of flow running every minute -- `artifacts/drift_report.html` -- `artifacts/drift_report.json` -- Retraining of best model if triggered +Expected JSON response: ---- - -## 4. Verify Output in UI Tools - -| Component | URL | Check | -|----------|-----|-------| -| **MLflow UI** | http://localhost:5000 | Parameters, metrics, model | -| **Prefect UI** | http://localhost:4200 | Flow runs, logs | -| **FastAPI** | http://localhost:9000/predict | Model serving | -| **Drift Report** | `artifacts/drift_report.html` | Open manually in browser | +```json +{ + "class": 1, + "proba": [0.02, 0.95, 0.03] +} +``` --- -## Summary - -This PoC runs fully locally using: - -- Conda -- Nox -- Make -- MLflow -- Prefect -- FastAPI +## 6 Monitoring & Auto‑Retraining  +- **Command** +In your terminal, once the conda environment is active, run: +```sh + python -m src.pipeline_monitoring monitoring_best +``` +- **Goal** + Detect distributional drift and kick off retraining only when needed. +- **What the Command Simulates** + A **scheduled batch job** (cron in Prefect) that runs nightly, comparing that day’s data against a reference baseline. +- **What It Does** + 1. Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. + 2. Evidently creates an HTML + JSON drift report. + 3. Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. + 4. All actions—report generation and optional retraining—are logged in Prefect and MLflow. +- **What to Explore** + - Drift Report: open `artifacts/drift_report.html`; discuss which features drifted. + - Prefect UI: see conditional branching—one path ends, the other chains into a training flow. + - MLflow UI: a new run appears **only** when drift threshold is exceeded, proving closed‑loop automation. -No Docker or remote services are required. +--- -Please run and verify each `make` command as documented above. +## 7 Conclusions + +Over the course of this lab we have: +- **Simulated a full CI/CD loop locally** + using Makefile to trigger branch‑style workflows and Nox as a lightweight stand‑in for GitHub Actions. +- **Captured the complete model lineage** + in MLflow, from exploratory runs to the promoted, deployable artifact. +- **Orchestrated repeatable pipelines** + with Prefect, gaining retry logic, scheduling, and an audit‑friendly UI. +- **Deployed an inference endpoint** + via FastAPI that is immediately testable through cURL or Swagger docs. +- **Closed the monitoring loop** + by integrating Evidently to detect drift and automatically retrain when data shifts. + +Together these elements demonstrate an end‑to‑end, production‑style MLOps workflow that fits entirely on a developer laptop—yet scales conceptually to cloud or on‑prem environments. +The key takeaway: **tooling synergy** matters more than individual components. +By combining focused, purpose‑built tools, we achieve reproducibility, observability and automation without heavyweight infrastructure. + +## 8 Next steps +TBD \ No newline at end of file diff --git a/Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store b/Academy/AI/2-ML-Model-Lifecycle/notebooks/.DS_Store deleted file mode 100644 index 5008ddfcf53c02e82d7eee2e57c38e5672ef89f6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - "" - ], - "text/plain": [ - " alcohol malic_acid ash alcalinity_of_ash magnesium total_phenols \\\n", - "0 14.23 1.71 2.43 15.6 127.0 2.80 \n", - "1 13.20 1.78 2.14 11.2 100.0 2.65 \n", - "2 13.16 2.36 2.67 18.6 101.0 2.80 \n", - "3 14.37 1.95 2.50 16.8 113.0 3.85 \n", - "4 13.24 2.59 2.87 21.0 118.0 2.80 \n", - "\n", - " flavanoids nonflavanoid_phenols proanthocyanins color_intensity hue \\\n", - "0 3.06 0.28 2.29 5.64 1.04 \n", - "1 2.76 0.26 1.28 4.38 1.05 \n", - "2 3.24 0.30 2.81 5.68 1.03 \n", - "3 3.49 0.24 2.18 7.80 0.86 \n", - "4 2.69 0.39 1.82 4.32 1.04 \n", - "\n", - " od280/od315_of_diluted_wines proline \n", - "0 3.92 1065.0 \n", - "1 3.40 1050.0 \n", - "2 3.17 1185.0 \n", - "3 3.45 1480.0 \n", - "4 2.93 735.0 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.datasets import load_wine\n", - "\n", - "data = load_wine(as_frame=True)\n", - "\n", - "data.data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2884b86d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['alcohol',\n", - " 'malic_acid',\n", - " 'ash',\n", - " 'alcalinity_of_ash',\n", - " 'magnesium',\n", - " 'total_phenols',\n", - " 'flavanoids',\n", - " 'nonflavanoid_phenols',\n", - " 'proanthocyanins',\n", - " 'color_intensity',\n", - " 'hue',\n", - " 'od280/od315_of_diluted_wines',\n", - " 'proline']" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.feature_names" - ] - }, - { - "cell_type": "markdown", - "id": "28e5c13e", - "metadata": {}, - "source": [ - "### Aprire MLflow UI\n", - "Da terminale, nella cartella di progetto:\n", - "```bash\n", - "mlflow ui --port 5001\n", - "```\n", - "e navigate su per esplorare gli esperimenti." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.11.12 ('lab_env_2')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.12" - }, - "vscode": { - "interpreter": { - "hash": "f3091eeb433eba5ffd1439d05e3608594fa2a8935778ae687bbee724d90b3358" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py index 88dde81..f0848f2 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py @@ -1,13 +1,35 @@ +""" +Unit tests for the data module in the Wine Quality ML pipeline. + +Covers: +- Dataset loading +- Train-test splitting logic +""" + from src import data def test_data_collection(): + """ + Test that the wine dataset is loaded correctly with non-empty features and labels. + """ X, y = data.data_collection() - assert not X.empty - assert len(X) == len(y) - assert "alcohol" in X.columns + + # Assert features and labels are not empty and match in length + assert not X.empty, "Feature dataframe is empty" + assert len(X) == len(y), "Mismatch between features and labels" + assert "alcohol" in X.columns, "'alcohol' column should exist in features" def test_data_preparation(): + """ + Test that the dataset is correctly split into train and test sets. + """ X, y = data.data_collection() X_tr, X_te, y_tr, y_te = data.data_preparation((X, y), split=0.2) - assert len(X_tr) + len(X_te) == len(X) - assert len(y_tr) + len(y_te) == len(y) + + # Assert that the train + test sizes add up to the original + assert len(X_tr) + len(X_te) == len(X), "Train + test features mismatch" + assert len(y_tr) + len(y_te) == len(y), "Train + test labels mismatch" + + # Optional: Check that no data was lost and all parts are non-empty + assert not X_tr.empty and not X_te.empty, "Split feature sets are empty" + assert not y_tr.empty and not y_te.empty, "Split label sets are empty" diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py index 5a5c545..8adeb52 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py @@ -1,14 +1,28 @@ -from src import serve -from src.config import CFG +""" +Unit test for FastAPI model serving without relying on a pre-existing trained model. -def test_build_app_and_predict(): - # Carica modello (assicurati che esista già da train_best) - model_path = CFG.artifacts_dir / CFG.model_name - assert model_path.exists(), "Trained model file is missing" +- Creates a dummy RandomForestClassifier +- Saves it to a temporary .pkl file +- Builds the FastAPI app using the test model +- Sends a prediction request to /predict +""" - api = serve.build_app(model_path) +import tempfile +import joblib +from pathlib import Path +from fastapi.testclient import TestClient +from sklearn.ensemble import RandomForestClassifier +import pandas as pd +from src import serve - sample = { +def test_build_app_and_predict(): + """ + Test that the FastAPI app is correctly built with a temporary dummy model, + and that the /predict endpoint returns a valid response. + """ + # --- Create a dummy model and train it on minimal synthetic data --- + model = RandomForestClassifier() + X_dummy = pd.DataFrame([{ "alcohol": 13.0, "malic_acid": 2.0, "ash": 2.5, @@ -22,11 +36,24 @@ def test_build_app_and_predict(): "hue": 1.0, "od280_od315_of_diluted_wines": 3.0, "proline": 1000, - } + }]) + y_dummy = [1] + model.fit(X_dummy, y_dummy) - from fastapi.testclient import TestClient + # --- Save model to a temporary .pkl file --- + with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp: + model_path = Path(tmp.name) + joblib.dump(model, model_path) + + # --- Build FastAPI app using the dummy model --- + api = serve.build_app(model_path) + + # --- Create a test client and send a sample request --- + sample = X_dummy.iloc[0].to_dict() client = TestClient(api) response = client.post("/predict", json=sample) + + # --- Assertions --- assert response.status_code == 200 result = response.json() assert "class" in result diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py index eba4412..52203e0 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py +++ b/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py @@ -1,8 +1,30 @@ +""" +Unit test for the train module in the Wine Quality ML pipeline. + +Covers: +- Training a RandomForestClassifier +- Validating model accuracy and structure +""" + from src import data, train def test_train_model_accuracy_threshold(): - dataset = data.data_collection() - prepared = data.data_preparation(dataset) + """ + Test that the train_model function returns a trained model with acceptable accuracy. + + - Uses a real dataset split + - Checks that accuracy is above a baseline threshold + - Verifies that the model supports .predict() + """ + # Load and split the dataset + X, y = data.data_collection() + prepared = data.data_preparation((X, y)) + + # Train a RandomForest model with basic hyperparameters model, acc = train.train_model(prepared, n_estimators=10, max_depth=5) - assert acc > 0.5 # basic threshold check - assert hasattr(model, "predict") + + # Assert accuracy is above 50% (weak sanity check) + assert acc > 0.5, f"Expected accuracy > 0.5, got {acc}" + + # Check that model has a predict method + assert hasattr(model, "predict"), "Model does not implement .predict()" From d316256f49ad8972522f71afa6a07cd50e92e29e Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:23:20 +0200 Subject: [PATCH 47/70] fix readme lint --- Academy/AI/2-ML-Model-Lifecycle/README.md | 57 ++++++++++++++++++----- 1 file changed, 46 insertions(+), 11 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index cdbff36..e74bdf5 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -1,13 +1,15 @@ - # Lab: From Experiments to Production with Wine Classification -Welcome to this live lab! + +Welcome to this live lab! In the previous lab session we: + 1. **Created a classification model** using **Scikit-learn**, leveraging temperature and humidity to predict rain. 2. **Integrated MLflow** to track training parameters, log evaluation metrics, and manage model versions in a structured way. 3. **Explored and compared results** through the MLflow UI interface, reviewing different configurations and loading a saved model for future predictions. In this session, you will learn how to: + 1. **Train a classification model** on a real dataset using `Scikit-learn`. 2. **Manage and track your experiments** with `MLflow`. 3. **Build CI/CD-like workflows** using `Makefile` and `nox`. @@ -19,11 +21,13 @@ We will follow a step-by-step approach combining **code execution**, **conceptua --- ## 1 Context and Story + A mid‑sized wine producer wants to modernise quality control. Instead of relying on manual tasting alone, the company decides to analyse chemical characteristics of each batch and predict the wine’s *cultivar* (three classes) using machine learning. The stakes: + - **Repeatable experimentation** – new blends need fresh tuning - **Fast promotion** – once a model looks good, it must move to production quickly - **Operational monitoring** – chemical profiles may drift with soil and weather; the system must recognise drift and retrain autonomously @@ -39,6 +43,7 @@ Originally collected at the Institute of Oenology and Viticulture in Italy, the Each sample is described by **13 continuous variables** that capture key chemical properties influencing flavour and quality. ### 2.1 Features + | Feature | Description | Typical Range | | ------------------------------ | ------------------------------------------- | ------------- | | `alcohol` | Ethyl-alcohol content (%) | 11 – 14 % | @@ -56,25 +61,29 @@ Each sample is described by **13 continuous variables** that capture key chemica | `proline` | Proline (mg/L) – associated with mouth-feel | 270 – 1680 | ### 2.2 Target variable: `class` + The classes are almost balanced (59 / 71 / 48 samples): + - **Class 0** – Cultivar A - **Class 1** – Cultivar B - **Class 2** – Cultivar C ### 2.3 Why This Dataset Fits the Demo + 1. **Compact and clean** – You can train models in seconds, perfect for live coding. 2. **Chemically interpretable features** – Easy to discuss how drifting alcohol content affects predictions. 3. **Multi-class problem** – Demonstrates probabilities and class selection in the REST API. 4. **No privacy concerns** – The data are public domain, ideal for workshops. ### 2.4 Synthetic Drift Generation + To illustrate monitoring, the lab creates a *current batch* where `alcohol` values are artificially increased (e.g., harvest with higher sugar). Evidently then compares this drifted batch to the original reference set, detects the shift and—if severe—triggers retraining through Prefect. - --- ## 3 Tooling Overview – Concept + Our Usage + | Tool | Key Concept | How We Use It in the Lab | |------|-------------|--------------------------| | [**Makefile**](https://www.gnu.org/software/make/manual/make.html) | Declarative build/task runner | Simulates Git push events and invokes Nox sessions (`push-feature`, `push-develop`, `push-master`). | @@ -88,25 +97,34 @@ Evidently then compares this drifted batch to the original reference set, detect --- ## 4 Environment Setup + ### 4.0 Requirements + This lab assumes that **Python** and **miniconda** are already installed, the repository [kiratech/labs](https://github.com/kiratech/labs.git) is accessible, and **Git** is properly configured on your local machine. Furthermore, **VSCode** or an IDE able to run Jupyter Notebooks, must be installed as well. As in the previous lab, in order to execute this laboratory, you will be asked to install a set of tools common in MLOps field. ### 4.1 Clone the Repository + To start, clone the lab repository by running the following command in the terminal: + ```sh git clone https://github.com/kiratech/labs.git ``` ### 4.2 Checkout the Lab Branch + After cloning the repository, checkout the `academy-ai` branch: + ```sh git checkout academy-ai ``` + Then, navigate to the project folder: + ```sh cd labs/Academy/AI/2-ML-Model-Lifecycle ``` + This folder contains resources related to this lab. ### 4.3 Create a Virtual Environment @@ -136,16 +154,18 @@ At this point, open VSCode from the file explorer or by running the command: code . ``` -### 4.5 Start the services +### 4.5 Start the services + Open three integrated terminals and in each one activate the environemnt with `conda activate lab_env_2` and then: | Terminal | Command | Purpose | |----------|---------|---------| | T‑1 | `prefect server start` | Prefect API + UI | | T‑2 | `mlflow ui` | Experiment tracking UI | -| T‑3 | Run workflow commands (next sections) | +| T‑3 | `-` | Run workflow commands (next sections) | ### 4.6 Open services UI + Open in the browser Prefect and MLflow at: | Service | Address | |----------|---------| @@ -157,11 +177,14 @@ Open in the browser Prefect and MLflow at: ## 5 Branch Workflows ### 5.1 Feature Branch Workflow + - **Command** In your terminal, once the conda environment is active, run: + ```sh make push-feature ``` + - **Goal** Explore the parameter space quickly and cheaply. No model is intended for production at this stage. We only want evidence that “something promising” exists. @@ -177,44 +200,50 @@ In your terminal, once the conda environment is active, run: - MLflow UI: nine runs with different hyper‑params, no artifacts saved. - Prefect UI: one flow, nine parallel tasks—visual confirmation of parallel runs. ---- - ### 5.2 Develop Branch Workflow  + - **Command** In your terminal, once the conda environment is active, run: + ```sh make push-develop ``` + - **Goal** Promote the best experimental configuration, validate code quality, and log a **deployable model artifact**. - **What the Command Simulates** A merge/push to **`develop`**. CI/CD should lint, test, **re‑train with chosen hyper‑params**, and register the resulting model. - **What It Does** + 1. **Lint** (`ruff via pre‑commit`) and **unit tests** (`pytest`) run first. Build stops on failure. 2. Prefect flow `train_best` queries MLflow, grabs the run with highest accuracy. 3. It re‑trains a RandomForest using those parameters on fresh data splits. 4. Saves the `.pkl` artifact to `artifacts/` and logs it to MLflow. + - **What to Explore** - Terminal: lint/test output. - MLflow UI: a new run with an **artifact path**, this is the candidate for production. - Prefect UI: see the “find best params” task feeding the “train” task. ---- - ### 5.3 Master Branch Workflow  `make push-master` + - **Command** In your terminal, once the conda environment is active, run: + ```sh make push-main ``` + - **Goal** Deploy the champion model as an HTTP service usable by downstream teams. - **What the Command Simulates** A merge/push to **`main`**. CI/CD should perform a last sanity check, then bring the model online. - **What It Does** + 1. Re‑runs lint and tests (quick safety net). 2. Prefect flow `serve_best` downloads the best model artifact from MLflow. 3. Builds a FastAPI app and launches Uvicorn on **port 9000**. + - **What to Explore** - Swagger UI at `http://127.0.0.1:9000/docs`: live documentation, try a prediction. - Prefect UI: a small flow made of download, build app and serve. @@ -250,25 +279,30 @@ Expected JSON response: "class": 1, "proba": [0.02, 0.95, 0.03] } -``` +``` --- ## 6 Monitoring & Auto‑Retraining  + - **Command** In your terminal, once the conda environment is active, run: + ```sh python -m src.pipeline_monitoring monitoring_best ``` + - **Goal** Detect distributional drift and kick off retraining only when needed. - **What the Command Simulates** A **scheduled batch job** (cron in Prefect) that runs nightly, comparing that day’s data against a reference baseline. - **What It Does** + 1. Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. 2. Evidently creates an HTML + JSON drift report. 3. Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. 4. All actions—report generation and optional retraining—are logged in Prefect and MLflow. + - **What to Explore** - Drift Report: open `artifacts/drift_report.html`; discuss which features drifted. - Prefect UI: see conditional branching—one path ends, the other chains into a training flow. @@ -279,6 +313,7 @@ In your terminal, once the conda environment is active, run: ## 7 Conclusions Over the course of this lab we have: + - **Simulated a full CI/CD loop locally** using Makefile to trigger branch‑style workflows and Nox as a lightweight stand‑in for GitHub Actions. - **Captured the complete model lineage** @@ -295,4 +330,4 @@ The key takeaway: **tooling synergy** matters more than individual components. By combining focused, purpose‑built tools, we achieve reproducibility, observability and automation without heavyweight infrastructure. ## 8 Next steps -TBD \ No newline at end of file +TBD From a460e254f9c2468f031acc2ee57fceca6c3be20f Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:30:45 +0200 Subject: [PATCH 48/70] fix readme lint --- Academy/AI/2-ML-Model-Lifecycle/README.md | 23 +++++++++++++++++------ 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index e74bdf5..943d68f 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -36,6 +36,7 @@ Our lab demonstrates how to meet these requirements **entirely on a laptop**, using open‑source tools and zero cloud resources. --- + ## 2 Dataset Overview – UCI Wine Recognition The lab uses the well-known [**Wine Recognition**](https://archive.ics.uci.edu/dataset/109/wine) dataset from the UCI Machine Learning Repository. @@ -192,11 +193,14 @@ In your terminal, once the conda environment is active, run: A developer pushes code to **`feature/`**. CI/CD should **only run experiments**, producing metrics the team can compare later. - **What It Does** + - A Nox session calls Prefect flow `train_experiment`. - Nine RandomForest training tasks run in parallel (3  `n_estimators` × 3  `max_depth`). - Each task logs **parameters + accuracy** to MLflow, but **does not store a model file**. - Prefect captures task logs and execution graph. + - **What to Explore** + - MLflow UI: nine runs with different hyper‑params, no artifacts saved. - Prefect UI: one flow, nine parallel tasks—visual confirmation of parallel runs. @@ -214,13 +218,14 @@ In your terminal, once the conda environment is active, run: - **What the Command Simulates** A merge/push to **`develop`**. CI/CD should lint, test, **re‑train with chosen hyper‑params**, and register the resulting model. - **What It Does** - + 1. **Lint** (`ruff via pre‑commit`) and **unit tests** (`pytest`) run first. Build stops on failure. 2. Prefect flow `train_best` queries MLflow, grabs the run with highest accuracy. 3. It re‑trains a RandomForest using those parameters on fresh data splits. 4. Saves the `.pkl` artifact to `artifacts/` and logs it to MLflow. - **What to Explore** + - Terminal: lint/test output. - MLflow UI: a new run with an **artifact path**, this is the candidate for production. - Prefect UI: see the “find best params” task feeding the “train” task. @@ -239,15 +244,17 @@ In your terminal, once the conda environment is active, run: - **What the Command Simulates** A merge/push to **`main`**. CI/CD should perform a last sanity check, then bring the model online. - **What It Does** - + 1. Re‑runs lint and tests (quick safety net). 2. Prefect flow `serve_best` downloads the best model artifact from MLflow. 3. Builds a FastAPI app and launches Uvicorn on **port 9000**. - **What to Explore** + - Swagger UI at `http://127.0.0.1:9000/docs`: live documentation, try a prediction. - Prefect UI: a small flow made of download, build app and serve. - MLflow UI: confirm the run ID of the served model matches the develop stage. + ### 5.4 Querying the Best Model Once the **main branch workflow** has deployed the FastAPI service, you can send a prediction request directly from the terminal: @@ -283,10 +290,10 @@ Expected JSON response: --- -## 6 Monitoring & Auto‑Retraining  +## 6 Monitoring & Auto‑Retraining -- **Command** -In your terminal, once the conda environment is active, run: +- **Command** +In your terminal, once the conda environment is active, run: ```sh python -m src.pipeline_monitoring monitoring_best @@ -297,13 +304,14 @@ In your terminal, once the conda environment is active, run: - **What the Command Simulates** A **scheduled batch job** (cron in Prefect) that runs nightly, comparing that day’s data against a reference baseline. - **What It Does** - + 1. Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. 2. Evidently creates an HTML + JSON drift report. 3. Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. 4. All actions—report generation and optional retraining—are logged in Prefect and MLflow. - **What to Explore** + - Drift Report: open `artifacts/drift_report.html`; discuss which features drifted. - Prefect UI: see conditional branching—one path ends, the other chains into a training flow. - MLflow UI: a new run appears **only** when drift threshold is exceeded, proving closed‑loop automation. @@ -329,5 +337,8 @@ Together these elements demonstrate an end‑to‑end, production‑style MLOps The key takeaway: **tooling synergy** matters more than individual components. By combining focused, purpose‑built tools, we achieve reproducibility, observability and automation without heavyweight infrastructure. +--- + ## 8 Next steps + TBD From c90f88f292f7ec9d172d9bb5c7cfc1fdf9c32d11 Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:36:20 +0200 Subject: [PATCH 49/70] fix readme lint --- Academy/AI/2-ML-Model-Lifecycle/README.md | 29 +++++++++++------------ 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 943d68f..e2a81c1 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -1,4 +1,3 @@ - # Lab: From Experiments to Production with Wine Classification Welcome to this live lab! @@ -182,9 +181,9 @@ Open in the browser Prefect and MLflow at: - **Command** In your terminal, once the conda environment is active, run: -```sh - make push-feature -``` + ```sh + make push-feature + ``` - **Goal** Explore the parameter space quickly and cheaply. No model is intended for production at this stage. @@ -209,9 +208,9 @@ In your terminal, once the conda environment is active, run: - **Command** In your terminal, once the conda environment is active, run: -```sh - make push-develop -``` + ```sh + make push-develop + ``` - **Goal** Promote the best experimental configuration, validate code quality, and log a **deployable model artifact**. @@ -235,9 +234,9 @@ In your terminal, once the conda environment is active, run: - **Command** In your terminal, once the conda environment is active, run: -```sh - make push-main -``` + ```sh + make push-main + ``` - **Goal** Deploy the champion model as an HTTP service usable by downstream teams. @@ -292,12 +291,12 @@ Expected JSON response: ## 6 Monitoring & Auto‑Retraining -- **Command** -In your terminal, once the conda environment is active, run: +- **Command** +In your terminal, once the conda environment is active, run: -```sh - python -m src.pipeline_monitoring monitoring_best -``` + ```sh + python -m src.pipeline_monitoring monitoring_best + ``` - **Goal** Detect distributional drift and kick off retraining only when needed. From 8aae0da08b816efb41ae195a331e2d46b61eae7e Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 6 May 2025 22:38:56 +0200 Subject: [PATCH 50/70] fix readme lint --- Academy/AI/2-ML-Model-Lifecycle/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index e2a81c1..5cef2d9 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -179,7 +179,7 @@ Open in the browser Prefect and MLflow at: ### 5.1 Feature Branch Workflow - **Command** -In your terminal, once the conda environment is active, run: + In your terminal, once the conda environment is active, run: ```sh make push-feature @@ -206,7 +206,7 @@ In your terminal, once the conda environment is active, run: ### 5.2 Develop Branch Workflow  - **Command** -In your terminal, once the conda environment is active, run: + In your terminal, once the conda environment is active, run: ```sh make push-develop @@ -232,7 +232,7 @@ In your terminal, once the conda environment is active, run: ### 5.3 Master Branch Workflow  `make push-master` - **Command** -In your terminal, once the conda environment is active, run: + In your terminal, once the conda environment is active, run: ```sh make push-main @@ -292,7 +292,7 @@ Expected JSON response: ## 6 Monitoring & Auto‑Retraining - **Command** -In your terminal, once the conda environment is active, run: + In your terminal, once the conda environment is active, run: ```sh python -m src.pipeline_monitoring monitoring_best From edca92d5e741c00e9bac6b79941d5e467019c07f Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 8 May 2025 22:17:40 +0200 Subject: [PATCH 51/70] add imgs of workflows --- Academy/AI/2-ML-Model-Lifecycle/README.md | 70 ++++++++++++++---- .../AI/2-ML-Model-Lifecycle/imgs/lab-full.png | Bin 0 -> 1050461 bytes .../2-ML-Model-Lifecycle/imgs/lab-part1.png | Bin 0 -> 1044777 bytes .../2-ML-Model-Lifecycle/imgs/lab-part2.png | Bin 0 -> 1043204 bytes .../2-ML-Model-Lifecycle/imgs/lab-part3.png | Bin 0 -> 1042782 bytes .../2-ML-Model-Lifecycle/imgs/lab-part4.png | Bin 0 -> 1045520 bytes 6 files changed, 55 insertions(+), 15 deletions(-) create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-full.png create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-part1.png create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-part2.png create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-part3.png create mode 100644 Academy/AI/2-ML-Model-Lifecycle/imgs/lab-part4.png diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 5cef2d9..5eca5b9 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -82,11 +82,13 @@ Evidently then compares this drifted batch to the original reference set, detect --- -## 3 Tooling Overview – Concept + Our Usage +## 3 Tooling Overview + +### 3.1 Tools, concepts and our usage | Tool | Key Concept | How We Use It in the Lab | |------|-------------|--------------------------| -| [**Makefile**](https://www.gnu.org/software/make/manual/make.html) | Declarative build/task runner | Simulates Git push events and invokes Nox sessions (`push-feature`, `push-develop`, `push-master`). | +| [**Makefile**](https://www.gnu.org/software/make/manual/make.html) | Declarative build/task runner | Simulates Git push events and invokes Nox sessions (`push-feature`, `push-develop`, `push-main`). | | [**Nox**](https://nox.thea.codes/en/stable/) | Python automation with virtual/conda envs | Acts like GitHub Actions runners; executes lint, tests and Prefect flows in a reproducible and isolated way. | | [**Prefect**](https://docs.prefect.io/v3/get-started) | Workflow orchestration + observability | Wraps training, serving and monitoring as flows; offers scheduling, retries and a UI at [http://127.0.0.1:4200](http://127.0.0.1:4200). | | [**MLflow**](https://mlflow.org/) | Experiment tracking & model registry | Logs parameters, metrics and model artefacts; source of truth for the “best model”. UI at [http://127.0.0.1:5000](http://127.0.0.1:5000). | @@ -94,6 +96,35 @@ Evidently then compares this drifted batch to the original reference set, detect | [**FastAPI**](https://fastapi.tiangolo.com/) | High‑performance Python web API | Exposes the model at `/predict` with auto‑generated docs at [http://127.0.0.1:9000](http://127.0.0.1:9000). | | [**Evidently**](https://docs.evidentlyai.com/introduction) | Data drift & model monitoring | Generates HTML/JSON drift reports; triggers retraining when drift is detected. | +### 3.2 Why We Simulate GitHub Actions Locally + +[End-to-End Workflow](imgs/lab-full.png) + +In production we would rely on **real Git events and GitHub Actions**: + +| Real event | Real GH Action (example) | Purpose | +| ----------------------------------------- | --------------------------------------------- | ----------------------------------- | +| `git push feature/experiments` | `ci-feature.yml` → sweep experiment | Run many trials, log only metrics | +| `git merge feature/experiments (into develop)` | `ci-develop.yml` → lint + tests + train best | Promote best params, prepare model | +| `git merge develop (into main)` | `ci-main.yml` → lint + tests + deploy best | Bring model online | + +For a laboratory setting those actions pose two practical problems: + +1. **Concurrency & permissions** – the whole group cannot push to `develop` or `main` at the same time without overwriting one another. +2. **Infrastructure dependencies** – real GH Actions would need publicly reachable Prefect and MLflow endpoints; everyone would have to expose tunnels or deploy cloud services. + +To keep the experience **100 % local**, we introduce a one-to-one mapping: + +| Simulated command | What it stands for | +| --------------------------------------------------- | ------------------------------------------------------ | +| `make push-feature` | `git push feature/experiments` trigger GH Action `ci-feature.yml` | +| `make push-develop` | `git merge feature/experiments (into develop)` trigger GH Action `ci-develop.yml` | +| `make push-main` | `git merge develop (into main)` trigger GH Action `ci-main.yml` | +| `python -m src.pipeline_monitoring monitoring_best` | Nightly cron job in GH Actions or Prefect Cloud | + +Under the hood **Nox** replaces the GitHub runner: +`make push-*` commands launch the appropriate nox sessions (lint / tests / Prefect flows) inside a shared Conda environment, reproducing exactly what would happen in cloud CI—but without leaving the laptop. + --- ## 4 Environment Setup @@ -176,8 +207,17 @@ Open in the browser Prefect and MLflow at: ## 5 Branch Workflows +### 5.0 What we will do + +A production-style GitHub Actions workflow we want to **experience locally**. +In the laboratory we replay each numbered block with Makefile + Nox + Prefect, avoiding concurrent pushes and external infrastructure. + +[End-to-End Workflow](imgs/lab-full.png) + ### 5.1 Feature Branch Workflow +[Feature Workflow](imgs/lab-part1.png) + - **Command** In your terminal, once the conda environment is active, run: @@ -194,7 +234,7 @@ Open in the browser Prefect and MLflow at: - **What It Does** - A Nox session calls Prefect flow `train_experiment`. - - Nine RandomForest training tasks run in parallel (3  `n_estimators` × 3  `max_depth`). + - 9 RandomForest training tasks run in parallel (3  `n_estimators` × 3  `max_depth`). - Each task logs **parameters + accuracy** to MLflow, but **does not store a model file**. - Prefect captures task logs and execution graph. @@ -205,6 +245,8 @@ Open in the browser Prefect and MLflow at: ### 5.2 Develop Branch Workflow  +[Develop Workflow](imgs/lab-part2.png) + - **Command** In your terminal, once the conda environment is active, run: @@ -229,7 +271,9 @@ Open in the browser Prefect and MLflow at: - MLflow UI: a new run with an **artifact path**, this is the candidate for production. - Prefect UI: see the “find best params” task feeding the “train” task. -### 5.3 Master Branch Workflow  `make push-master` +### 5.3 Main Branch Workflow + +[Main Workflow](imgs/lab-part3.png) - **Command** In your terminal, once the conda environment is active, run: @@ -291,6 +335,8 @@ Expected JSON response: ## 6 Monitoring & Auto‑Retraining +[Feature Workflow](imgs/lab-part4.png) + - **Command** In your terminal, once the conda environment is active, run: @@ -304,10 +350,10 @@ Expected JSON response: A **scheduled batch job** (cron in Prefect) that runs nightly, comparing that day’s data against a reference baseline. - **What It Does** - 1. Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. - 2. Evidently creates an HTML + JSON drift report. - 3. Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. - 4. All actions—report generation and optional retraining—are logged in Prefect and MLflow. + - Generates (or ingests) the **current batch**. Here we synthesise drift by nudging alcohol levels. + - Evidently creates an HTML + JSON drift report. + - Prefect parses the `alchol` feature p‑value between the training set distribution and the new drifted set distribution. If ≤ 0.05, it calls the same `train_best` flow used on develop. + - All actions—report generation and optional retraining—are logged in Prefect and MLflow. - **What to Explore** @@ -321,7 +367,7 @@ Expected JSON response: Over the course of this lab we have: -- **Simulated a full CI/CD loop locally** +- **Simulated a full CI/CD/CT loop locally** using Makefile to trigger branch‑style workflows and Nox as a lightweight stand‑in for GitHub Actions. - **Captured the complete model lineage** in MLflow, from exploratory runs to the promoted, deployable artifact. @@ -335,9 +381,3 @@ Over the course of this lab we have: Together these elements demonstrate an end‑to‑end, production‑style MLOps workflow that fits entirely on a developer laptop—yet scales conceptually to cloud or on‑prem environments. The key takeaway: **tooling synergy** matters more than individual components. 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zk1vu+=0K%tb-=@!zZ;rYH8*AHo>jCq?pn2e@@TY#N#Kx+wc4H0U#@CjHn=x=8hM?+ zWF9`4*oRB$p_RxB>eo*(`+Rq6vUU|0%Ds8Qtm2@|FWY)mD{ADyetS;vepdBy87?jk z`~nD79oQ*o2y`nW_ph=wC(#j z$ULr-TC?9JxPAbQ!=S$K2@nFb!qtt3mh$^)`@=e`S8@7nSls&65#Z&m+;wru;@H@K zh&#~(=Tt7FYJtGQ?to4AnE-z~eYrZ2Nn{begBSsIR~BW=CIWU4??=hSF6|3DNI)5m zS*vq_*6N;!zk~EyuybjVKwL38;=?i-z?EoJbAmYEW16=%{c_TO?|r*C)+OQFxik@# z1)-S?)W!^TO}pZE*>d@J;~vm0?(8n=uxE|N0XUsIa8Bjtt-wc%AaQ$N$zt!b#b5Gs zKq=tuy6aZo9~3|fx>%HeA;6LcpDR)nh`Xna%@EU_h7D!~C|rh01+C$i3^qM9lAvG$7qjI$s+X^f`BSIHC)4&r^WD)Jj0m658bFb#& z3=TLEP~W?<3(U17!vE^72)Uamc3x~MfO9-?kXR>VNW?GHQvqq{_ujmVI;StE8mI!T z?L{trWn4yPPHE7a?wyaJ8R`ogAfg0(Jol|OY;8Z!|#W`{1d({Zq~5sHLxMJM-47?0Q;A5!W#h_^Iw~oFFsE0 z5-6YLJIfYd{@ Date: Thu, 8 May 2025 22:33:35 +0200 Subject: [PATCH 52/70] mv img of workflow --- Academy/AI/2-ML-Model-Lifecycle/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Academy/AI/2-ML-Model-Lifecycle/README.md b/Academy/AI/2-ML-Model-Lifecycle/README.md index 5eca5b9..cbbfc3a 100644 --- a/Academy/AI/2-ML-Model-Lifecycle/README.md +++ b/Academy/AI/2-ML-Model-Lifecycle/README.md @@ -84,6 +84,8 @@ Evidently then compares this drifted batch to the original reference set, detect ## 3 Tooling Overview +[End-to-End Workflow](imgs/lab-full.png) + ### 3.1 Tools, concepts and our usage | Tool | Key Concept | How We Use It in the Lab | @@ -98,8 +100,6 @@ Evidently then compares this drifted batch to the original reference set, detect ### 3.2 Why We Simulate GitHub Actions Locally -[End-to-End Workflow](imgs/lab-full.png) - In production we would rely on **real Git events and GitHub Actions**: | Real event | Real GH Action (example) | Purpose | From f8679c6d6aaa6520104675f740bd1974bfad7ea4 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 4 Aug 2025 21:08:56 +0200 Subject: [PATCH 53/70] add notebooks --- Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb | 423 ++++++++++++++++++ .../AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb | 239 ++++++++++ 2 files changed, 662 insertions(+) create mode 100644 Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb create mode 100644 Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb new file mode 100644 index 0000000..332158a --- /dev/null +++ b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "ug_3UHSCaaZI" + }, + "source": [ + "## What is Hugging Face and the Hugging Face Hub ?\n", + "\n", + "Hugging Face is an open-source-first AI company that curates Transformers, Diffusers, and other widely used libraries.\n", + "The Hub (https://huggingface.co/) is a **GitHub for models & datasets**:\n", + "- Free hosting for models, datasets, Spaces (demo apps).\n", + "- Built-in versioning, README rendering, and model cards.\n", + "- REST / Python SDK (huggingface_hub) for push-pull, inference endpoints, and gated access control.\n", + "\n", + "In practice you clone/push a repo exactly like Git, but via hf_hub_download() or model.push_to_hub()." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-hTQLYPZdUZp" + }, + "outputs": [], + "source": [ + "!pip install -q huggingface_hub" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UX-GYw-WbGrP" + }, + "source": [ + "\n", + "## What are providers on HF Inference Client and what is novita?\n", + "\n", + "The InferenceClient can route a request to different providers (back-ends):\n", + "- huggingface (default): the standard Inference API hosted by HF.\n", + "- novita (a third-party back-end): offers chat-optimized latency/pricing; API surface matches OpenAI-style /chat/completions.\n", + "\n", + "You switch simply by\n", + "\n", + "```python\n", + "client = InferenceClient(provider=\"novita\", api_key=HF_TOKEN)\n", + "```\n", + "so the same client code can talk to multiple vendors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mZG3akxmdqpT", + "outputId": "456d745e-1ae4-49e7-8940-7a8e613e721f" + }, + "outputs": [], + "source": [ + "import os, getpass\n", + "from huggingface_hub import InferenceClient\n", + "\n", + "# Inserisci il tuo token Hugging Face personale\n", + "os.environ[\"HF_TOKEN\"] = getpass.getpass(\"👉 Inserisci il tuo HF token: \")\n", + "\n", + "# Usa provider \"novita\" per il modello Mistral chat\n", + "client = InferenceClient(provider=\"novita\", api_key=os.environ[\"HF_TOKEN\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-D2_PSUlep8I" + }, + "source": [ + "## Model spotlight - mistralai/Mistral-7B-Instruct-v0.3\n", + "| - | Pros | Cons |\n", + "| ------------------ | -------------------------------------------------------------------------------- | --------------------------------------------------------------------------- |\n", + "| **Quality** | Very strong chat & code for a 7 B model; outperforms LLaMA-7B/13B in many evals. | Still below GPT-4 / Claude-Opus tier on reasoning & long-context. |\n", + "| **Licence** | Apache-2.0 – commercial use allowed, no policy hoops. | Weight access sometimes gated; you need HF/token or local download. |\n", + "| **Speed / Memory** | Fits into 14 GB GPU (8-bit) → cheap to host; responds \\~50-70 tok/s on T4. | Context window 8 K; cannot process very long docs like GPT-4o-128K. |\n", + "| **LoRA** | Adapter \\~120 MB; fine-tune on a Colab T4 in <30 min. | PEFT on Mistral needs to target correct linear layers (`q_proj`, `v_proj`). |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qogkSPAbbiD2" + }, + "source": [ + "## Prompt Engineering - Zero-shot\n", + "\n", + "Ask the model to perform a task without examples: “Translate the sentence to Italian: 'I love coffee'”.\n", + "\n", + "Zero-shot works because large LMs have seen countless instructions during pre-training. It's the fastest way to test a capability." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vd3cTO7LemPx", + "outputId": "f971023b-1d60-4c6d-a6da-437b083c30c2" + }, + "outputs": [], + "source": [ + "prompt = \"A customer is requesting a refund because a product was delivered late. Write a professional reply.\"\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[\n", + " {\"role\": \"user\", \"content\": prompt}\n", + " ],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_UF5mFW1cBNm" + }, + "source": [ + "## Prompt Engineering - Few-shot\n", + "\n", + "Provide 1-3 exemples so the model infers pattern, tone, structure.\n", + "\n", + "Example: \n", + "Q1: Formal -> Casual \n", + "A1: Please contact me. -> Hit me up! \n", + "\n", + "The next transformation aligns with the examples far better than zero-shot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CeOQuFfRdrga", + "outputId": "126939e8-9c72-48fd-a94b-00b5de91ab5f" + }, + "outputs": [], + "source": [ + "prompt = '''Example 1:\n", + "Customer: I received my order late. I'd like a refund.\n", + "Reply: We're sorry for the delay. Your refund will be processed within 3 business days.\n", + "\n", + "Example 2:\n", + "Customer: My package arrived after the estimated delivery date. I want a refund.\n", + "Reply: We apologize for the inconvenience. The refund has been approved and will be issued shortly.\n", + "\n", + "Now write a reply to:\n", + "A customer is requesting a refund because a product was delivered late.\n", + "'''\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ExIj1lZyc9jE" + }, + "source": [ + "## Chain-of-Thought Prompting\n", + "\n", + "Add “Think step by step” (or an explicit reasoning prompt). \n", + "The model reveals intermediate reasoning, often raising factual accuracy and allowing you to inspect/error-correct the chain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-TgOb4owgnHn", + "outputId": "7c8a24dc-f8f3-4de0-ac6c-fef324f80022" + }, + "outputs": [], + "source": [ + "prompt = \"A customer is requesting a refund because a product was delivered late. Think step by step and write a professional reply.\"\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oWUOoci-dQLx" + }, + "source": [ + "## Role Prompting\n", + "\n", + "Pre-frame the assistant persona:\n", + "\"You are an empathetic customer-support agent…\". \n", + "Sets vocabulary, tone, even policy constraints without touching the model weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n5jnEUKwgoVM", + "outputId": "10bc4383-505a-474c-80b9-af01e73c52be" + }, + "outputs": [], + "source": [ + "prompt = \"You are a customer support agent. A customer is asking for a refund due to a late delivery. Write a helpful and empathetic response.\"\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I8fryyF4dgJ2" + }, + "source": [ + "## Prompt Patterns - Delimiters\n", + "\n", + "Wrap user context in clear markers (\"\"\" or ###), e.g.\n", + "\n", + "```txt\n", + "\"\"\"CONTEXT\n", + "long text …\n", + "\"\"\"\n", + "Summarize the context in one bullet.\n", + "```\n", + "\n", + "Prevents the model from mixing instructions with payload; boosts reliability in multi-part prompts." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l9doquBSg2jV", + "outputId": "6d34e5a7-0075-40bd-d360-daca4b5b0a07" + }, + "outputs": [], + "source": [ + "prompt = '''Write a professional reply with the following structure:\n", + "1. Greeting\n", + "2. Apology for the delay\n", + "3. Refund details\n", + "4. Closing remark\n", + "\n", + "Customer: I received the product late and would like a refund.'''\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S1ooDMboeG6k" + }, + "source": [ + "## LoRA-style Fine-tuning (simulation vs real)\n", + "\n", + "Simulation: bake policy or domain hints inside the prompt ([POLICY] …). \n", + "Real LoRA: train tiny adapter matrices (≪1 % params) -> insert at Q/V/(K,O) projections. Needs only minutes & single GPU; adapter file <100 MB." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "koM2FhTJg2-q", + "outputId": "26c6f6ba-4ee1-4976-b04a-ed7cdd23e6e8" + }, + "outputs": [], + "source": [ + "# Prompt standard\n", + "prompt = \"A customer is requesting a refund because a product was delivered late. Write a professional reply.\"\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(\"Standard Prompt:\\n\", completion.choices[0].message[\"content\"])\n", + "\n", + "# Prompt 'personalizzato' (simulazione LoRA con istruzioni nel prompt)\n", + "prompt_custom_1 = '''[INTERNAL POLICY: Respond with empathy, offer refund without requiring customer to return the item, and apologize sincerely. Use concise and positive tone.]\n", + "\n", + "A customer is requesting a refund because a product was delivered late. Write a professional reply.'''\n", + "\n", + "completion_custom_1 = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt_custom_1}],\n", + ")\n", + "print(\"Prompt personalizzato:\\n\", completion_custom_1.choices[0].message[\"content\"])\n", + "\n", + "# Prompt 'personalizzato' (simulazione LoRA con istruzioni nel prompt)\n", + "prompt_custom_2 = '''[INTERNAL POLICY: Do not offer refunds, instead offer 15% discount code.]\n", + "\n", + "A customer is requesting a refund because a product was delivered late. Write a professional reply.'''\n", + "\n", + "completion_custom_2 = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt_custom_2}],\n", + ")\n", + "print(\"Prompt personalizzato:\\n\", completion_custom_2.choices[0].message[\"content\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L6PVrBLGebEB" + }, + "source": [ + "## Prompt for Structured Output (JSON)\n", + "\n", + "Explicitly instruct format + give a skeleton:\n", + "\n", + "\n", + "\n", + "```\n", + "Return ONLY valid JSON:\n", + "{\"greeting\":\"\", \"apology\":\"\", \"resolution\":\"\", \"action_type\":\"\", \"needs_follow_up\":false}\n", + "```\n", + "\n", + "Model tends to obey if the request is precise and examples are consistent." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OdBNug_3g3OX", + "outputId": "bcb45f0e-6893-4fba-dada-6e160db982b3" + }, + "outputs": [], + "source": [ + "prompt = '''\n", + "You are a customer support assistant. A customer is requesting a refund because a product arrived late.\n", + "\n", + "Return the response in JSON format with the following fields:\n", + "- greeting\n", + "- apology\n", + "- refund_policy\n", + "- closing\n", + "\n", + "Example format:\n", + "{\n", + " \"greeting\": \"...\",\n", + " \"apology\": \"...\",\n", + " \"refund_policy\": \"...\",\n", + " \"closing\": \"...\"\n", + "}\n", + "'''\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + " messages=[{\"role\": \"user\", \"content\": prompt}],\n", + ")\n", + "print(completion.choices[0].message[\"content\"])" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.10.10 ('.venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.10" + }, + "vscode": { + "interpreter": { + "hash": "12a8c86496d70dc5ec458316f0fdc66c09147f19c1386e8b96b40af59bd1e3d4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb new file mode 100644 index 0000000..dc6b886 --- /dev/null +++ b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kWEJ1_vBLlja" + }, + "outputs": [], + "source": [ + "!rm -rf t5-lora-out\n", + "!rm -rf t5-small-lora-adapter" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sIvNNZPcg3T-" + }, + "outputs": [], + "source": [ + "!pip install -q transformers datasets peft accelerate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xm3Hrq4o6bGd" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from datasets import Dataset\n", + "df = pd.read_csv(\"customer_support_lora_dataset_250.csv\")\n", + "ds = Dataset.from_pandas(df)\n", + "print(ds[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "X4z3IZjFrwMG" + }, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer, DataCollatorForSeq2Seq\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n", + "\n", + "def preprocess(ex):\n", + " model_inputs = tokenizer(ex[\"input_text\"], max_length=128, truncation=True)\n", + " with tokenizer.as_target_tokenizer():\n", + " labels = tokenizer(ex[\"target_text\"], max_length=128, truncation=True)\n", + " model_inputs[\"labels\"] = labels[\"input_ids\"]\n", + " return model_inputs\n", + "\n", + "ds_tok = ds.map(preprocess, remove_columns=ds.column_names)\n", + "print(ds_tok[0].keys(), \"→ lengths:\", len(ds_tok[0][\"input_ids\"]), len(ds_tok[0][\"labels\"]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qJwCKCFFrKgI" + }, + "outputs": [], + "source": [ + "from transformers import AutoModelForSeq2SeqLM\n", + "from peft import LoraConfig, get_peft_model, TaskType\n", + "\n", + "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", + "\n", + "lora_cfg = LoraConfig(\n", + " task_type = TaskType.SEQ_2_SEQ_LM,\n", + " r = 8,\n", + " lora_alpha = 16,\n", + " target_modules = [\"q\", \"v\"],\n", + " lora_dropout = 0.05,\n", + " bias = \"none\"\n", + ")\n", + "\n", + "peft_model = get_peft_model(base_model, lora_cfg)\n", + "peft_model.print_trainable_parameters()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "_zAx2Eq2raxS" + }, + "outputs": [], + "source": [ + "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, DataCollatorForSeq2Seq\n", + "\n", + "training_args = Seq2SeqTrainingArguments(\n", + " output_dir = \"./t5-lora-out\",\n", + " per_device_train_batch_size = 16,\n", + " num_train_epochs = 30,\n", + " learning_rate = 5e-4,\n", + " logging_steps = 5,\n", + " save_strategy = \"no\",\n", + " report_to = \"none\",\n", + ")\n", + "\n", + "trainer = Seq2SeqTrainer(\n", + " model = peft_model,\n", + " args = training_args,\n", + " train_dataset = ds_tok,\n", + " data_collator = DataCollatorForSeq2Seq(tokenizer, model=peft_model),\n", + ")\n", + "\n", + "trainer.train()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bbw5BG817UvV" + }, + "outputs": [], + "source": [ + "trainer.model.save_pretrained(\"t5-small-lora-adapter\") \n", + "tokenizer.save_pretrained(\"t5-small-lora-adapter\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eG0hnG43qlAM" + }, + "outputs": [], + "source": [ + "from peft import PeftConfig, PeftModel\n", + "from transformers import AutoModelForSeq2SeqLM\n", + "\n", + "cfg = PeftConfig.from_pretrained(\"t5-small-lora-adapter\")\n", + "base_model = AutoModelForSeq2SeqLM.from_pretrained(cfg.base_model_name_or_path)\n", + "model_lora = PeftModel.from_pretrained(base_model, \"t5-small-lora-adapter\")\n", + "\n", + "prompt = \"generate reply: My order arrived late. I want a refund.\"\n", + "inputs = tokenizer(prompt, return_tensors=\"pt\")\n", + "outputs = model_lora.generate(**inputs, max_new_tokens=80)\n", + "\n", + "print(\"→\", tokenizer.decode(outputs[0], skip_special_tokens=True))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A0j9NQUt7e6F" + }, + "outputs": [], + "source": [ + "\n", + "# Compare vanilla T5-small with LoRA fine‑tuned adapter on structured JSON output\n", + "import json, pandas as pd\n", + "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", + "from peft import PeftConfig, PeftModel\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n", + "\n", + "# Base (pre‑trained) model\n", + "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", + "\n", + "# LoRA‑adapted model – make sure this path matches the one used in the training cell\n", + "adapter_path = \"t5-small-lora-adapter\"\n", + "cfg = PeftConfig.from_pretrained(adapter_path)\n", + "ft_model = PeftModel.from_pretrained(\n", + " AutoModelForSeq2SeqLM.from_pretrained(cfg.base_model_name_or_path),\n", + " adapter_path\n", + ")\n", + "\n", + "def generate(model, prompt):\n", + " ids = tokenizer(prompt, return_tensors=\"pt\")\n", + " out = model.generate(**ids, max_new_tokens=120)\n", + " return tokenizer.decode(out[0], skip_special_tokens=True)\n", + "\n", + "test_prompts = [\n", + " \"generate structured_reply: My order arrived late. I want a refund.\",\n", + " \"generate structured_reply: The product I received is damaged..\",\n", + " \"generate structured_reply: I correctly received my order. Thanks!\"\n", + "]\n", + "test_prompts = [\n", + " \"generate structured_reply: My order arrived late. I want a refund.\",\n", + " \"generate structured_reply: The product I received is damaged. What can I do?\",\n", + " \"generate structured_reply: I received the wrong item in my order.\",\n", + " \"generate structured_reply: How can I return an item I purchased last week?\",\n", + " \"generate structured_reply: I never received my order.\",\n", + " \"generate structured_reply: Why was I charged twice for my order?\",\n", + " \"generate structured_reply: I need help tracking my shipment.\",\n", + " \"generate structured_reply: Can I exchange my item for a different size?\"\n", + "]\n", + "\n", + "records = []\n", + "for p in test_prompts:\n", + " records.append({\n", + " \"prompt\": p,\n", + " \"T5-base\": generate(base_model, p),\n", + " \"LoRA\": generate(ft_model, p)\n", + " })\n", + "\n", + "df = pd.DataFrame(records)\n", + "print(df.to_markdown(index=False))\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.10.10 ('.venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.10.10" + }, + "vscode": { + "interpreter": { + "hash": "12a8c86496d70dc5ec458316f0fdc66c09147f19c1386e8b96b40af59bd1e3d4" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 58b290c490a892aa3fc75b699b65f1f878742553 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 4 Aug 2025 23:18:35 +0200 Subject: [PATCH 54/70] add readme and contents --- Academy/AI/3-LLM/README.md | 79 ++++++ .../customer_support_lora_dataset_250.csv | 251 ++++++++++++++++++ Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb | 114 ++++++-- .../AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb | 236 ++++++++++++++-- 4 files changed, 630 insertions(+), 50 deletions(-) create mode 100644 Academy/AI/3-LLM/README.md create mode 100644 Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv diff --git a/Academy/AI/3-LLM/README.md b/Academy/AI/3-LLM/README.md new file mode 100644 index 0000000..6bf83bd --- /dev/null +++ b/Academy/AI/3-LLM/README.md @@ -0,0 +1,79 @@ +# Lab: Prompt‑Engineering and LoRA Fine‑Tuning + +Welcome to this live lab! +In the previous lab session we discussed how to: + +1. **Train a classification model** on a real dataset using `Scikit-learn`. +2. **Manage and track your experiments** with `MLflow`. +3. **Build CI/CD-like workflows** using `Makefile` and `nox`. +4. **Build scalable and orchestrated ML pipelines** using `Prefect`. +5. **Monitor data drift over time** and trigger retraining with `Evidently`. + +In this session, you will run two Jupyter notebooks that demonstrate: + +- Practical prompt‑engineering patterns in `notebook 1_LabLLM.ipynb`, the craft of writing clear, explicit instructions for an LLM. +- A LoRA fine‑tuning workflow on model T5‑small in `notebook 2_LabLLM_LoRA.ipynb`, a lightweight fine-tuning technique for transformers. + +We'll leverage two big providers for this lab: + +- **Google Colab**: a free, browser-based Jupyter environment hosted by Google. Colab gives you a temporary Linux VM with Python, CUDA drivers, and optional GPU/TPU. You open a .ipynb notebook, run cells just like in Jupyter, and share links with classmates—no local setup required. + +- **Hugging Face**: A community and tooling hub for modern AI. +It offers: + + - Model Hub: 500 k-plus pre-trained models (transformers, diffusers, etc.). + - Datasets Hub: curated datasets in one-line API. + - Inference API / Endpoints: hosted model inference in the cloud. + - Libraries: transformers, datasets, peft, and more. + + Creating a (free) HF account lets you: + + - Download models. + - Push your own models/adapters/data. + - Generate an access token that notebooks use to call hosted endpoints securely. + +--- + +## Repository contents + +| Path/Name | What it is and why it matters | +| --------------------------------------- | ---------------------------------------------- | +| `notebooks/1_LabLLM.ipynb` | Prompt‑engineering walkthrough (Mistral‑7B) | +| `notebooks/2_LabLLM_LoRA.ipynb` | LoRA adapter training on a 250‑row support set | +| `files/customer_support_lora_dataset_250.csv` | Tiny dataset used in Notebook 2 | +| `README` (this file) | Quickstart and background | + +## Prerequisites + +| Tool / Account | Why you need it | Sign‑up link | +| ------------------------ | -------------------------------------------- | ------------------------------------------------------------------------ | +| **Google account** | Required to open Colab notebooks | [https://accounts.google.com/signup](https://accounts.google.com/signup) | +| **Hugging Face account** | Lets you call hosted models & store adapters | [https://huggingface.co/join](https://huggingface.co/join) | + +### 1 Create your HF account and token + +1. Go to [https://huggingface.co/join](https://huggingface.co/join) and sign up (email and password). +2. Verify your email address (check spam folder too). +3. Navigate to **Settings > Access Tokens > New Token**. +4. Name it e.g. `ai‑academy‑lab` and select **"Read"** scope (fine for this lesson). +5. Copy the token. **You will paste it into the notebook when prompted**. +6. Treat the token like a password and do **not** share or commit it to GitHub. + +### 2 Recommended Colab runtime settings + +- **GPU:** In Colab, go to **Runtime > Change runtime type > Hardware accelerator > GPU**. +- **RAM:** The default *Standard* tier is enough. No paid upgrade needed. + +--- + +## Run the notebooks in Colab + +Open Google Colab and import, everything at root level, the content of the folders notebooks and files. +From now you can continue on the notebooks. + +--- + +## Suggested readings and references + +- [OpenAI Cookbook – GPT‑4 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide): a new prompting guide that lays out a practical structure for building powerful prompts, especially with GPT-4.1. It’s short, clear, and highly effective for anyone working with agents, structured outputs, tool use, or reasoning-heavy tasks. +- [Google Prompt Engineering pdf](https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view): whether you're technical or non-technical, this might be one of the most useful prompt engineering resources out there right now. Google just published a 68-page whitepaper focused on Prompt Engineering (focused on API users), and it goes deep on structure, formatting, config settings, and real examples. diff --git a/Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv b/Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv new file mode 100644 index 0000000..3d10f64 --- /dev/null +++ b/Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv @@ -0,0 +1,251 @@ +input_text,target_text +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I never received my order.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I never received my order.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I never received my order.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: Can I exchange my item for a different size?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Dear customer,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The product I received is damaged. What can I do?,"{""greeting"":""Hello,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Your website charged me shipping twice.,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I was promised a discount that was never applied.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Hi there,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: How can I return an item I purchased last week?,"{""greeting"":""Hi there,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I need help tracking my shipment.,"{""greeting"":""Hello,"",""apology"":""We apologize for this experience."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Hello,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: My order arrived late. I want a refund.,"{""greeting"":""Hi there,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The tracking number shows delivered but I didn't get the package.,"{""greeting"":""Greetings,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: Why was I charged twice for my order?,"{""greeting"":""Dear customer,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""A free replacement will be shipped today and tracking will follow."",""action_type"":""replace"",""needs_follow_up"":false}" +generate structured_reply: My package was left outside in the rain and got wet.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: The item arrived with missing accessories.,"{""greeting"":""Greetings,"",""apology"":""We sincerely apologize for the inconvenience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I never received my order.,"{""greeting"":""Dear customer,"",""apology"":""We apologize for this experience."",""resolution"":""Your refund has been processed and no return is required."",""action_type"":""refund"",""needs_follow_up"":false}" +generate structured_reply: I received the wrong item in my order.,"{""greeting"":""Dear customer,"",""apology"":""Please accept our apologies for the issue."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: The replacement you sent is also faulty.,"{""greeting"":""Hi there,"",""apology"":""We apologize for this experience."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" +generate structured_reply: I accidentally ordered the wrong color. Can I change it?,"{""greeting"":""Greetings,"",""apology"":""We are sorry for any trouble caused."",""resolution"":""Your case has been escalated to a senior agent who will contact you shortly."",""action_type"":""escalate"",""needs_follow_up"":true}" diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb index 332158a..1463611 100644 --- a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb +++ b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb @@ -1,12 +1,37 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prompt-Engineering Lab: Customer-Support Replies with **Mistral-7B-Instruct**\n", + "\n", + "In this lab you will experiment with six classic **prompt patterns**:\n", + "\n", + "1. **Zero-shot**: no examples, just the task. \n", + "2. **Few-shot**: show 1-2 examples first. \n", + "3. **Chain-of-Thought**: ask the model to reason step by step. \n", + "4. **Role / Persona**: tell the model \"You are a ...\". \n", + "5. **Structured output**: force JSON or bullet format. \n", + "6. **System / Policy instructions**: prepend internal guidelines. \n", + "\n", + "By the end you’ll understand how tiny changes in wording can dramatically change an LLM’s answer.\n", + "\n", + "## What is Prompt-Engineering?\n", + "Because large language models don’t have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.is called prompt engineering. Good prompts boost accuracy and consistency without touching the model’s weights." + ] + }, { "cell_type": "markdown", "metadata": { "id": "ug_3UHSCaaZI" }, "source": [ - "## What is Hugging Face and the Hugging Face Hub ?\n", + "## 0 Install Hugging Face client library\n", + "\n", + "We only need **`huggingface_hub`** because we call the hosted Inference API. No heavyweight Transformers install is required.\n", + "\n", + "### What is Hugging Face and the Hugging Face Hub ?\n", "\n", "Hugging Face is an open-source-first AI company that curates Transformers, Diffusers, and other widely used libraries.\n", "The Hub (https://huggingface.co/) is a **GitHub for models & datasets**:\n", @@ -34,12 +59,15 @@ "id": "UX-GYw-WbGrP" }, "source": [ + "## 1 Authenticate and create an `InferenceClient`\n", "\n", - "## What are providers on HF Inference Client and what is novita?\n", + "You need an **access token** to use the Mistral-7B endpoint hosted by provider **\"novita\"** on Hugging Face.\n", + "\n", + "### What are providers on HF Inference Client and what is novita?\n", "\n", "The InferenceClient can route a request to different providers (back-ends):\n", "- huggingface (default): the standard Inference API hosted by HF.\n", - "- novita (a third-party back-end): offers chat-optimized latency/pricing; API surface matches OpenAI-style /chat/completions.\n", + "- novita (a third-party back-end): offers chat-optimized latency/pricing. API surface matches OpenAI-style /chat/completions.\n", "\n", "You switch simply by\n", "\n", @@ -64,10 +92,10 @@ "import os, getpass\n", "from huggingface_hub import InferenceClient\n", "\n", - "# Inserisci il tuo token Hugging Face personale\n", - "os.environ[\"HF_TOKEN\"] = getpass.getpass(\"👉 Inserisci il tuo HF token: \")\n", + "# Paste your personal HF token when prompted\n", + "os.environ[\"HF_TOKEN\"] = getpass.getpass(\"Enter your HF token: \")\n", "\n", - "# Usa provider \"novita\" per il modello Mistral chat\n", + "# Create the client (model is chosen later in each call)\n", "client = InferenceClient(provider=\"novita\", api_key=os.environ[\"HF_TOKEN\"])" ] }, @@ -80,10 +108,10 @@ "## Model spotlight - mistralai/Mistral-7B-Instruct-v0.3\n", "| - | Pros | Cons |\n", "| ------------------ | -------------------------------------------------------------------------------- | --------------------------------------------------------------------------- |\n", - "| **Quality** | Very strong chat & code for a 7 B model; outperforms LLaMA-7B/13B in many evals. | Still below GPT-4 / Claude-Opus tier on reasoning & long-context. |\n", - "| **Licence** | Apache-2.0 – commercial use allowed, no policy hoops. | Weight access sometimes gated; you need HF/token or local download. |\n", - "| **Speed / Memory** | Fits into 14 GB GPU (8-bit) → cheap to host; responds \\~50-70 tok/s on T4. | Context window 8 K; cannot process very long docs like GPT-4o-128K. |\n", - "| **LoRA** | Adapter \\~120 MB; fine-tune on a Colab T4 in <30 min. | PEFT on Mistral needs to target correct linear layers (`q_proj`, `v_proj`). |\n" + "| **Quality** | Very strong chat and code for a 7 B model. Outperforms LLaMA-7B/13B in many evals. | Still below GPT-4 / Claude-Opus tier on reasoning and long-context. |\n", + "| **Licence** | Apache-2.0, commercial use allowed. | Weight access sometimes gated. You need HF/token or local download. |\n", + "| **Speed / Memory** | Fits into 14 GB GPU (8-bit) so it is cheap to host. Responds \\~50-70 tok/s on T4. | Context window 8 K. Cannot process very long docs like GPT-4o-128K. |\n", + "| **LoRA** | Adapter \\~120 MB. Can be fine-tuned on a Colab T4 in <30 min. | PEFT on Mistral needs to target correct linear layers (`q_proj`, `v_proj`). |\n" ] }, { @@ -92,7 +120,7 @@ "id": "qogkSPAbbiD2" }, "source": [ - "## Prompt Engineering - Zero-shot\n", + "## 2 Zero-shot prompt\n", "\n", "Ask the model to perform a task without examples: “Translate the sentence to Italian: 'I love coffee'”.\n", "\n", @@ -112,12 +140,14 @@ "outputs": [], "source": [ "prompt = \"A customer is requesting a refund because a product was delivered late. Write a professional reply.\"\n", + "\n", "completion = client.chat.completions.create(\n", " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[\n", " {\"role\": \"user\", \"content\": prompt}\n", " ],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -127,9 +157,9 @@ "id": "_UF5mFW1cBNm" }, "source": [ - "## Prompt Engineering - Few-shot\n", + "## 3 Few-shot prompt\n", "\n", - "Provide 1-3 exemples so the model infers pattern, tone, structure.\n", + "Provide 1-3 examples so the model infers pattern, tone, structure.\n", "\n", "Example: \n", "Q1: Formal -> Casual \n", @@ -166,6 +196,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -175,7 +206,7 @@ "id": "ExIj1lZyc9jE" }, "source": [ - "## Chain-of-Thought Prompting\n", + "## 4 Chain-of-Thought prompt\n", "\n", "Add “Think step by step” (or an explicit reasoning prompt). \n", "The model reveals intermediate reasoning, often raising factual accuracy and allowing you to inspect/error-correct the chain." @@ -199,6 +230,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -208,7 +240,7 @@ "id": "oWUOoci-dQLx" }, "source": [ - "## Role Prompting\n", + "## 5 Role / Persona prompt\n", "\n", "Pre-frame the assistant persona:\n", "\"You are an empathetic customer-support agent…\". \n", @@ -233,6 +265,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -242,7 +275,7 @@ "id": "I8fryyF4dgJ2" }, "source": [ - "## Prompt Patterns - Delimiters\n", + "## 6 Structured Output\n", "\n", "Wrap user context in clear markers (\"\"\" or ###), e.g.\n", "\n", @@ -253,7 +286,7 @@ "Summarize the context in one bullet.\n", "```\n", "\n", - "Prevents the model from mixing instructions with payload; boosts reliability in multi-part prompts." + "Prevents the model from mixing instructions with payload and boosts reliability in multi-part prompts." ] }, { @@ -280,6 +313,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(completion.choices[0].message[\"content\"])" ] }, @@ -289,10 +323,11 @@ "id": "S1ooDMboeG6k" }, "source": [ - "## LoRA-style Fine-tuning (simulation vs real)\n", + "## 7 System / Policy instructions (simulating LoRA-style policies)\n", + "\n", + "Simulation: bake policy or domain hints inside the prompt ([POLICY] ...). Notice how the reply changes when the policy changes.\n", "\n", - "Simulation: bake policy or domain hints inside the prompt ([POLICY] …). \n", - "Real LoRA: train tiny adapter matrices (≪1 % params) -> insert at Q/V/(K,O) projections. Needs only minutes & single GPU; adapter file <100 MB." + "Real LoRA: train tiny adapter matrices (<<1 % params) and insert at Q/V/(K,O) projections of self-attention matrices. Needs only minutes, single GPU and the adapter file is <100 MB." ] }, { @@ -307,16 +342,17 @@ }, "outputs": [], "source": [ - "# Prompt standard\n", + "# Standard prompt\n", "prompt = \"A customer is requesting a refund because a product was delivered late. Write a professional reply.\"\n", "\n", "completion = client.chat.completions.create(\n", " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", + "\n", "print(\"Standard Prompt:\\n\", completion.choices[0].message[\"content\"])\n", "\n", - "# Prompt 'personalizzato' (simulazione LoRA con istruzioni nel prompt)\n", + "# Policy A: full refund, no return required\n", "prompt_custom_1 = '''[INTERNAL POLICY: Respond with empathy, offer refund without requiring customer to return the item, and apologize sincerely. Use concise and positive tone.]\n", "\n", "A customer is requesting a refund because a product was delivered late. Write a professional reply.'''\n", @@ -325,9 +361,9 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt_custom_1}],\n", ")\n", - "print(\"Prompt personalizzato:\\n\", completion_custom_1.choices[0].message[\"content\"])\n", + "print(\"Policy 1:\\n\", completion_custom_1.choices[0].message[\"content\"])\n", "\n", - "# Prompt 'personalizzato' (simulazione LoRA con istruzioni nel prompt)\n", + "# Policy B: no refund, offer discount code\n", "prompt_custom_2 = '''[INTERNAL POLICY: Do not offer refunds, instead offer 15% discount code.]\n", "\n", "A customer is requesting a refund because a product was delivered late. Write a professional reply.'''\n", @@ -336,7 +372,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt_custom_2}],\n", ")\n", - "print(\"Prompt personalizzato:\\n\", completion_custom_2.choices[0].message[\"content\"])" + "print(\"Policy 2:\\n\", completion_custom_2.choices[0].message[\"content\"])" ] }, { @@ -345,11 +381,9 @@ "id": "L6PVrBLGebEB" }, "source": [ - "## Prompt for Structured Output (JSON)\n", - "\n", - "Explicitly instruct format + give a skeleton:\n", - "\n", + "## 8 Advanced: JSON-formatted reply\n", "\n", + "Explicitly combining role, task, format and give a skeleton:\n", "\n", "```\n", "Return ONLY valid JSON:\n", @@ -395,6 +429,28 @@ ")\n", "print(completion.choices[0].message[\"content\"])" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9 Take-aways\n", + "\n", + "- **Prompt engineering for rapid iteration**: you can prototype different behaviours in minutes, no fine-tuning needed. \n", + "- **System vs. User prompts**: internal policies (system) can override the tone and policy of the final answer. \n", + "- **Structured output**: JSON or numbered lists make post-processing trivial. \n", + "\n", + "### Next experiments\n", + "\n", + "1. Add temperature/top-p arguments to explore creativity vs. safety of the model. \n", + "2. Test the same prompts on a different model (e.g., **Llama-3-8B-Instruct**) to compare style and latency. \n", + "3. Combine with *LoRA-style adapters* (see Notebook 2) for policy adherence without needing bulky prompts. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb index dc6b886..2ae4494 100644 --- a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb +++ b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb @@ -1,5 +1,28 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# LoRA Fine-Tuning Lab: T5-small Customer-Support Adapter\n", + "\n", + "Welcome! \n", + "This hands-on notebook will guide you—step by step—through fine-tuning a **T5-small** model with **LoRA (Low-Rank Adaptation)** so it can answer customer-support questions in a consistent, structured style.\n", + "\n", + "## What is LoRA (Low-Rank Adaptation)\n", + "\n", + "Instead of updating all hundreds of millions of parameters, LoRA freezes the original model and inserts two tiny matrices into selected linear layers (often the Q and V projections). Training adjusts only those low-rank “adapter” weights. So you need far less GPU memory, reach good quality with small datasets, and ship adapters (~10 MB) instead of full checkpoints (~2 GB)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 0 Environment Clean-up\n", + "Before starting, you may want to delete any previous artefacts (checkpoints, logs, etc.) so the run is fresh and reproducible. \n", + "Feel free to skip this cell if you have nothing to clean." + ] + }, { "cell_type": "code", "execution_count": null, @@ -8,10 +31,25 @@ }, "outputs": [], "source": [ + "# Remove previous training artefacts—run only if you need a fresh start\n", "!rm -rf t5-lora-out\n", "!rm -rf t5-small-lora-adapter" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 Install Dependencies\n", + "We rely on the **Hugging Face Transformers** ecosystem plus two helper libraries:\n", + "- **`transformers`**: model and trainer APIs.\n", + "- **`datasets`**: efficient data loading from HF.\n", + "- **`peft`** – adds LoRA and other parameter-efficient methods. \n", + "- **`accelerate`** – handles device placement (CPU / single-GPU / multi-GPU) transparently.\n", + "\n", + "Installation is one time." + ] + }, { "cell_type": "code", "execution_count": null, @@ -20,9 +58,26 @@ }, "outputs": [], "source": [ + "# Transformers, Datasets, PEFT, and Accelerate (quiet install)\n", "!pip install -q transformers datasets peft accelerate" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 Load CSV Dataset and transform it to HF Dataset\n", + "\n", + "Our CSV file has two columns:\n", + "\n", + "| Column name | What it contains |\n", + "|---------------|--------------------------------------------------|\n", + "| `input_text` | A raw customer request or complaint |\n", + "| `target_text` | The ideal structured reply we want the model to generate |\n", + "\n", + "We'll turn the CSV into a **HF Dataset** object so it plays nicely with the Trainer API." + ] + }, { "cell_type": "code", "execution_count": null, @@ -33,9 +88,42 @@ "source": [ "import pandas as pd\n", "from datasets import Dataset\n", + "\n", + "# Read the 250-row customer-support file\n", "df = pd.read_csv(\"customer_support_lora_dataset_250.csv\")\n", "ds = Dataset.from_pandas(df)\n", - "print(ds[0])" + "\n", + "print(\"Sample row:\\n\", ds[0]) # quick sanity check" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 Tokenisation and Label Preparation\n", + "\n", + "Transformer models can’t read raw text, they need **token IDs**.\n", + "For sequence-to-sequence models like T5 we must prepare **two** sequences:\n", + "\n", + "1. **Source** – the customer request (`input_text`) \n", + "2. **Target** – the desired reply (`target_text`) \n", + "\n", + "### Key details:\n", + "\n", + "- We call `tokenizer.as_target_tokenizer()` so the decoder uses its own special prefix tokens. \n", + "- We truncate to 128 tokens to keep batches small on modest GPUs. \n", + "\n", + "### What exactly are \"tokens\" (the 128-token limit)? \n", + "A token is not a word or a single character.\n", + "Transformers work on sub-word units produced by a tokenizer (for T5 that’s a SentencePiece model with a 32 k-item vocabulary). The rules are learned from large corpora and try to strike a balance:\n", + "| Example text | Tokens generated | Notes |\n", + "| ------------- | ---------------------- | ------------------------------------- |\n", + "| `tracking` | `▁track`, `ing` | the leading “▁” marks a word start |\n", + "| `refund` | `▁refund` | common words are often a single token |\n", + "| `extra-large` | `▁extra`, `-`, `large` | punctuation becomes its own token |\n", + "\n", + "Because tokens can be full words or fragments, the length in tokens is usually 1.3-1.6x shorter than counting raw characters but longer than counting full words.\n", + "A 128-token limit therefore fits roughly 75–100 English words (fewer if the text contains many rare names, URLs, or emojis that split into multiple tokens)." ] }, { @@ -50,15 +138,55 @@ "\n", "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n", "\n", - "def preprocess(ex):\n", - " model_inputs = tokenizer(ex[\"input_text\"], max_length=128, truncation=True)\n", + "def preprocess(example):\n", + " # Encode source\n", + " model_inputs = tokenizer(example[\"input_text\"], max_length=128, truncation=True)\n", + " # Encode target as labels\n", " with tokenizer.as_target_tokenizer():\n", - " labels = tokenizer(ex[\"target_text\"], max_length=128, truncation=True)\n", + " labels = tokenizer(example[\"target_text\"], max_length=128, truncation=True)\n", " model_inputs[\"labels\"] = labels[\"input_ids\"]\n", " return model_inputs\n", "\n", "ds_tok = ds.map(preprocess, remove_columns=ds.column_names)\n", - "print(ds_tok[0].keys(), \"→ lengths:\", len(ds_tok[0][\"input_ids\"]), len(ds_tok[0][\"labels\"]))\n" + "\n", + "\n", + "print(\"Keys:\", ds_tok[0].keys(),\n", + " \"| source len:\", len(ds_tok[0][\"input_ids\"]),\n", + " \"| target len:\", len(ds_tok[0][\"labels\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4 Build Base Model and LoRA Configuration\n", + "\n", + "Here we load the vanilla **T5-small** (60 M parameters) and wrap it with a `peft.LoraConfig`.\n", + "\n", + "### Key hyper-parameters:\n", + "\n", + "| Parameter | Role | Here |\n", + "|--------------|---------------------------------------------------------------|------|\n", + "| `r` | Rank of the low-rank matrices (higher = more capacity) | 8 |\n", + "| `lora_alpha` | Scaling factor for the adapter’s update | 16 |\n", + "| `target_modules` | Which weight matrices get adapters (we pick **q** & **v**) | [\"q\",\"v\"] |\n", + "| `lora_dropout` | Regularisation inside adapters | 0.05 |\n", + "\n", + "### Why place LoRA adapters on q and v? What about the others?\n", + "\n", + "| Symbol | Full name | Role in self-attention |\n", + "| ------ | ------------------------------- | ------------------------------ |\n", + "| **Q** | **Query** projection | asks “what am I looking for?” |\n", + "| **K** | **Key** projection | represents “what do I have?” |\n", + "| **V** | **Value** projection | holds the information to mix |\n", + "| **O** | **Output** (final linear layer) | re-mixes heads after attention |\n", + "\n", + "A complete attention block has four projection matrices per head. Putting LoRA on all four gives maximum flexibility but also multiplies train-time memory. \n", + "Empirical sweet-spot: Research (LoRA paper §5 and several follow-ups) showed that adapting Q + V captures most task-specific gains while keeping parameter count and GPU RAM minimal. The intuition:\n", + "- Queries (Q) change how each token attends to others.\n", + "- Values (V) change what content is blended once attention scores are computed.\n", + "\n", + "Keys and the output layer matter too, but adjusting them yields diminishing returns for many language-generation tasks." ] }, { @@ -75,16 +203,32 @@ "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", "\n", "lora_cfg = LoraConfig(\n", - " task_type = TaskType.SEQ_2_SEQ_LM,\n", - " r = 8,\n", - " lora_alpha = 16,\n", - " target_modules = [\"q\", \"v\"],\n", + " task_type = TaskType.SEQ_2_SEQ_LM, # generation task\n", + " r = 8, # rank of the LoRA matrices\n", + " lora_alpha = 16, # scaling\n", + " target_modules = [\"q\", \"v\"], # project only query & value matrices\n", " lora_dropout = 0.05,\n", " bias = \"none\"\n", ")\n", "\n", "peft_model = get_peft_model(base_model, lora_cfg)\n", - "peft_model.print_trainable_parameters()\n" + "peft_model.print_trainable_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5 Training Arguments and Trainer Loop\n", + "\n", + "Hugging Face `Seq2SeqTrainer` takes care of the full training loop (forward, back-prop, gradient clipping, etc.).\n", + "\n", + "Important flags we set:\n", + "\n", + "- **`per_device_train_batch_size` = 16**, fits on a 12 GB GPU. \n", + "- **`num_train_epochs` = 30**, small dataset needs more passes. \n", + "- **`learning_rate` = 5e-4**, slightly higher than full-fine-tuning, because we’re optimising far fewer weights. \n", + "- **`save_strategy` = \"no\"**, skip checkpoints to save disk. You can change it to `\"epoch\"` if you want them. " ] }, { @@ -115,7 +259,16 @@ " data_collator = DataCollatorForSeq2Seq(tokenizer, model=peft_model),\n", ")\n", "\n", - "trainer.train()\n" + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6 Save Adapter and Tokenizer\n", + "\n", + "LoRA lets us store **only** the lightweight adapter, ~9 MB in this case. The base T5-small weights are **not duplicated**." ] }, { @@ -127,7 +280,16 @@ "outputs": [], "source": [ "trainer.model.save_pretrained(\"t5-small-lora-adapter\") \n", - "tokenizer.save_pretrained(\"t5-small-lora-adapter\")\n" + "tokenizer.save_pretrained(\"t5-small-lora-adapter\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7 Load LoRA-Adapted Model for Inference\n", + "\n", + "We merge the adapter with the frozen base model at load time, then generate a reply for a sample complaint." ] }, { @@ -141,15 +303,33 @@ "from peft import PeftConfig, PeftModel\n", "from transformers import AutoModelForSeq2SeqLM\n", "\n", + "# Load base model\n", "cfg = PeftConfig.from_pretrained(\"t5-small-lora-adapter\")\n", "base_model = AutoModelForSeq2SeqLM.from_pretrained(cfg.base_model_name_or_path)\n", + "# Load LoRA adapter\n", "model_lora = PeftModel.from_pretrained(base_model, \"t5-small-lora-adapter\")\n", "\n", + "# Test input\n", "prompt = \"generate reply: My order arrived late. I want a refund.\"\n", + "# Tokenize the test input\n", "inputs = tokenizer(prompt, return_tensors=\"pt\")\n", + "# Generate reply\n", "outputs = model_lora.generate(**inputs, max_new_tokens=80)\n", "\n", - "print(\"→\", tokenizer.decode(outputs[0], skip_special_tokens=True))\n" + "print(\"LoRA reply:\\n\", tokenizer.decode(outputs[0], skip_special_tokens=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8 Side-by-Side Evaluation (Base vs LoRA)\n", + "\n", + "Let’s run eight realistic prompts through both the vanilla T5-small and our LoRA-adapted version, then print the outputs in a table for quick eyeballing. \n", + "You should notice LoRA replies are:\n", + "- More structured (e.g., include apology and next steps) \n", + "- Shorter and on brand \n", + "- Consistent JSON or bullet style, depending on your `target_text` examples " ] }, { @@ -160,7 +340,6 @@ }, "outputs": [], "source": [ - "\n", "# Compare vanilla T5-small with LoRA fine‑tuned adapter on structured JSON output\n", "import json, pandas as pd\n", "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", @@ -171,7 +350,7 @@ "# Base (pre‑trained) model\n", "base_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", "\n", - "# LoRA‑adapted model – make sure this path matches the one used in the training cell\n", + "# LoRA‑adapted model, make sure this path matches the one used in the training cell\n", "adapter_path = \"t5-small-lora-adapter\"\n", "cfg = PeftConfig.from_pretrained(adapter_path)\n", "ft_model = PeftModel.from_pretrained(\n", @@ -186,11 +365,6 @@ "\n", "test_prompts = [\n", " \"generate structured_reply: My order arrived late. I want a refund.\",\n", - " \"generate structured_reply: The product I received is damaged..\",\n", - " \"generate structured_reply: I correctly received my order. Thanks!\"\n", - "]\n", - "test_prompts = [\n", - " \"generate structured_reply: My order arrived late. I want a refund.\",\n", " \"generate structured_reply: The product I received is damaged. What can I do?\",\n", " \"generate structured_reply: I received the wrong item in my order.\",\n", " \"generate structured_reply: How can I return an item I purchased last week?\",\n", @@ -209,8 +383,28 @@ " })\n", "\n", "df = pd.DataFrame(records)\n", - "print(df.to_markdown(index=False))\n" + "print(df.to_markdown(index=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9 Next Steps \n", + "\n", + "1. **Quantisation**: combine LoRA with 8-bit weights using `bitsandbytes` library to shrink disk size and speed up inference. \n", + "2. **Hyper-parameter search**: try different ranks (`r`) and target modules (add **k** and **o** matrices) for possibly better accuracy. \n", + "3. **Objective metrics**: integrate BLEU, ROUGE-L, or a custom JSON validator to track quality over epochs. \n", + "4. **Deployment**: merge base + adapter and serve via FastAPI, Streamlit, or Hugging Face Inference Endpoints. \n", + "5. **Prompt scaffolding**: prepend `\"generate structured_reply:\"` automatically so end-users don’t need to remember it. \n", + "\n", + "Happy fine-tuning!" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] } ], "metadata": { From 1f8527b7e5353255917c1c83179be6f3c28d36a7 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 4 Aug 2025 23:22:45 +0200 Subject: [PATCH 55/70] fix readme --- Academy/AI/3-LLM/README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/Academy/AI/3-LLM/README.md b/Academy/AI/3-LLM/README.md index 6bf83bd..4dad1b2 100644 --- a/Academy/AI/3-LLM/README.md +++ b/Academy/AI/3-LLM/README.md @@ -19,15 +19,16 @@ We'll leverage two big providers for this lab: - **Google Colab**: a free, browser-based Jupyter environment hosted by Google. Colab gives you a temporary Linux VM with Python, CUDA drivers, and optional GPU/TPU. You open a .ipynb notebook, run cells just like in Jupyter, and share links with classmates—no local setup required. - **Hugging Face**: A community and tooling hub for modern AI. -It offers: - + + It offers: + - Model Hub: 500 k-plus pre-trained models (transformers, diffusers, etc.). - Datasets Hub: curated datasets in one-line API. - Inference API / Endpoints: hosted model inference in the cloud. - Libraries: transformers, datasets, peft, and more. Creating a (free) HF account lets you: - + - Download models. - Push your own models/adapters/data. - Generate an access token that notebooks use to call hosted endpoints securely. @@ -75,5 +76,5 @@ From now you can continue on the notebooks. ## Suggested readings and references -- [OpenAI Cookbook – GPT‑4 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide): a new prompting guide that lays out a practical structure for building powerful prompts, especially with GPT-4.1. It’s short, clear, and highly effective for anyone working with agents, structured outputs, tool use, or reasoning-heavy tasks. -- [Google Prompt Engineering pdf](https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view): whether you're technical or non-technical, this might be one of the most useful prompt engineering resources out there right now. Google just published a 68-page whitepaper focused on Prompt Engineering (focused on API users), and it goes deep on structure, formatting, config settings, and real examples. +- [OpenAI Cookbook – GPT‑4 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide): a new prompting guide that lays out a practical structure for building powerful prompts, especially with GPT-4.1. It’s short, clear, and highly effective for anyone working with agents, structured outputs, tool use, or reasoning-heavy tasks. +- [Google Prompt Engineering pdf](https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view): whether you're technical or non-technical, this might be one of the most useful prompt engineering resources out there right now. Google just published a 68-page whitepaper focused on Prompt Engineering (focused on API users), and it goes deep on structure, formatting, config settings, and real examples. From 8eafdec9f4f295dbc8a8b5082189e3535281499b Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 6 Aug 2025 17:53:45 +0200 Subject: [PATCH 56/70] fix notebooks --- Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb | 94 +++++------ .../AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb | 156 +++++++++++++----- 2 files changed, 156 insertions(+), 94 deletions(-) diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb index 1463611..04bbb67 100644 --- a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb +++ b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb @@ -2,7 +2,9 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "us7zHCUszttj" + }, "source": [ "# Prompt-Engineering Lab: Customer-Support Replies with **Mistral-7B-Instruct**\n", "\n", @@ -12,13 +14,14 @@ "2. **Few-shot**: show 1-2 examples first. \n", "3. **Chain-of-Thought**: ask the model to reason step by step. \n", "4. **Role / Persona**: tell the model \"You are a ...\". \n", - "5. **Structured output**: force JSON or bullet format. \n", + "5. **Structured delimiters**: clearly define context or information. \n", "6. **System / Policy instructions**: prepend internal guidelines. \n", + "7. **Structured JSON-formatted**: force JSON or bullet format. \n", "\n", - "By the end you’ll understand how tiny changes in wording can dramatically change an LLM’s answer.\n", + "By the end you'll understand how tiny changes in wording can dramatically change an LLM's answer.\n", "\n", "## What is Prompt-Engineering?\n", - "Because large language models don’t have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.is called prompt engineering. Good prompts boost accuracy and consistency without touching the model’s weights." + "Because large language models don't have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.is called prompt engineering. Good prompts boost accuracy and consistency without touching the model's weights." ] }, { @@ -81,11 +84,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mZG3akxmdqpT", - "outputId": "456d745e-1ae4-49e7-8940-7a8e613e721f" + "id": "mZG3akxmdqpT" }, "outputs": [], "source": [ @@ -131,11 +130,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vd3cTO7LemPx", - "outputId": "f971023b-1d60-4c6d-a6da-437b083c30c2" + "id": "vd3cTO7LemPx" }, "outputs": [], "source": [ @@ -172,11 +167,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CeOQuFfRdrga", - "outputId": "126939e8-9c72-48fd-a94b-00b5de91ab5f" + "id": "CeOQuFfRdrga" }, "outputs": [], "source": [ @@ -208,7 +199,7 @@ "source": [ "## 4 Chain-of-Thought prompt\n", "\n", - "Add “Think step by step” (or an explicit reasoning prompt). \n", + "Add \"Think step by step\" (or an explicit reasoning prompt). \n", "The model reveals intermediate reasoning, often raising factual accuracy and allowing you to inspect/error-correct the chain." ] }, @@ -216,15 +207,11 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-TgOb4owgnHn", - "outputId": "7c8a24dc-f8f3-4de0-ac6c-fef324f80022" + "id": "-TgOb4owgnHn" }, "outputs": [], "source": [ - "prompt = \"A customer is requesting a refund because a product was delivered late. Think step by step and write a professional reply.\"\n", + "prompt = \"A customer is requesting a refund because a product was delivered late. Think step by step and explain me your reasonings to write a professional reply.\"\n", "\n", "completion = client.chat.completions.create(\n", " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", @@ -251,11 +238,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "n5jnEUKwgoVM", - "outputId": "10bc4383-505a-474c-80b9-af01e73c52be" + "id": "n5jnEUKwgoVM" }, "outputs": [], "source": [ @@ -275,7 +258,7 @@ "id": "I8fryyF4dgJ2" }, "source": [ - "## 6 Structured Output\n", + "## 6 Structured delimiters\n", "\n", "Wrap user context in clear markers (\"\"\" or ###), e.g.\n", "\n", @@ -293,21 +276,22 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "l9doquBSg2jV", - "outputId": "6d34e5a7-0075-40bd-d360-daca4b5b0a07" + "id": "l9doquBSg2jV" }, "outputs": [], "source": [ - "prompt = '''Write a professional reply with the following structure:\n", + "prompt = '''\n", + "### STRUCTURE\n", "1. Greeting\n", "2. Apology for the delay\n", "3. Refund details\n", "4. Closing remark\n", - "\n", - "Customer: I received the product late and would like a refund.'''\n", + "###\n", + "### CUSTOMER EMAIL\n", + "I received the product late and would like a refund.\n", + "###\n", + "Write a professional reply to customer email using our structure.\n", + "'''\n", "\n", "completion = client.chat.completions.create(\n", " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", @@ -334,11 +318,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "koM2FhTJg2-q", - "outputId": "26c6f6ba-4ee1-4976-b04a-ed7cdd23e6e8" + "id": "koM2FhTJg2-q" }, "outputs": [], "source": [ @@ -350,7 +330,7 @@ " messages=[{\"role\": \"user\", \"content\": prompt}],\n", ")\n", "\n", - "print(\"Standard Prompt:\\n\", completion.choices[0].message[\"content\"])\n", + "print(\"Standard Prompt:\\n\", completion.choices[0].message[\"content\"], \"\\n---\")\n", "\n", "# Policy A: full refund, no return required\n", "prompt_custom_1 = '''[INTERNAL POLICY: Respond with empathy, offer refund without requiring customer to return the item, and apologize sincerely. Use concise and positive tone.]\n", @@ -361,7 +341,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt_custom_1}],\n", ")\n", - "print(\"Policy 1:\\n\", completion_custom_1.choices[0].message[\"content\"])\n", + "print(\"Policy 1:\\n\", completion_custom_1.choices[0].message[\"content\"], \"\\n---\")\n", "\n", "# Policy B: no refund, offer discount code\n", "prompt_custom_2 = '''[INTERNAL POLICY: Do not offer refunds, instead offer 15% discount code.]\n", @@ -372,7 +352,7 @@ " model=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", " messages=[{\"role\": \"user\", \"content\": prompt_custom_2}],\n", ")\n", - "print(\"Policy 2:\\n\", completion_custom_2.choices[0].message[\"content\"])" + "print(\"Policy 2:\\n\", completion_custom_2.choices[0].message[\"content\"], \"\\n---\")" ] }, { @@ -381,7 +361,7 @@ "id": "L6PVrBLGebEB" }, "source": [ - "## 8 Advanced: JSON-formatted reply\n", + "## 8 Structured JSON-formatted\n", "\n", "Explicitly combining role, task, format and give a skeleton:\n", "\n", @@ -397,11 +377,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OdBNug_3g3OX", - "outputId": "bcb45f0e-6893-4fba-dada-6e160db982b3" + "id": "OdBNug_3g3OX" }, "outputs": [], "source": [ @@ -432,7 +408,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "N2Go8Hszztto" + }, "source": [ "## 9 Take-aways\n", "\n", @@ -449,7 +427,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "7yg1gPSZztto" + }, "source": [] } ], @@ -476,4 +456,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb index 2ae4494..611f1f2 100644 --- a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb +++ b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb @@ -2,21 +2,35 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "BVqac1pg809I" + }, "source": [ "# LoRA Fine-Tuning Lab: T5-small Customer-Support Adapter\n", "\n", "Welcome! \n", - "This hands-on notebook will guide you—step by step—through fine-tuning a **T5-small** model with **LoRA (Low-Rank Adaptation)** so it can answer customer-support questions in a consistent, structured style.\n", + "This hands-on notebook will guide you, step by step, through fine-tuning a **T5-small** model with **LoRA (Low-Rank Adaptation)** so it can answer customer-support questions in a consistent, structured style. \n", + "Indeed, out-of-the-box **T5-small** has never seen our customer-support style or policy. \n", + "If you prompt it with “My order arrived late, I want a refund,” it answers vaguely, or not at all. \n", + "\n", + "In this notebook we will:\n", + "\n", + "1. **Measure the baseline** and see how poorly vanilla T5 handles eight real support prompts. \n", + "2. **Attach a tiny LoRA adapter** (\\~ 9 MB, 8-rank) and fine-tune it on **just 250 examples**. \n", + "3. **Re-test the same prompts** to verify that the adapted model now produces concise, policy-compliant replies. \n", + "\n", + "**Key takeaway:** with LoRA we upgrade a generic language model into a task-specialist in ~10 minutes on a free Colab GPU, without touching the original 60 M parameters.\n", "\n", "## What is LoRA (Low-Rank Adaptation)\n", "\n", - "Instead of updating all hundreds of millions of parameters, LoRA freezes the original model and inserts two tiny matrices into selected linear layers (often the Q and V projections). Training adjusts only those low-rank “adapter” weights. So you need far less GPU memory, reach good quality with small datasets, and ship adapters (~10 MB) instead of full checkpoints (~2 GB)." + "Instead of updating all hundreds of millions of parameters, LoRA freezes the original model and inserts two tiny matrices into selected linear layers (often the Q and V projections). Training adjusts only those low-rank “adapter” weights. So you need far less GPU memory, reach good quality with small datasets, and ship adapters (\\~10 MB) instead of full checkpoints (\\~2 GB)." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "XvH1ePiH809K" + }, "source": [ "## 0 Environment Clean-up\n", "Before starting, you may want to delete any previous artefacts (checkpoints, logs, etc.) so the run is fresh and reproducible. \n", @@ -38,7 +52,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "UMkIRIWx809M" + }, "source": [ "## 1 Install Dependencies\n", "We rely on the **Hugging Face Transformers** ecosystem plus two helper libraries:\n", @@ -64,10 +80,47 @@ }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "## 2 Load CSV Dataset and transform it to HF Dataset\n", + "## 2 Baseline Check: How well does vanilla T5-small handle our task?\n", + "\n", + "Before we train anything, let's ask the out-of-the-box model to draft a refund reply. \n", + "Spoiler: its answer will be generic, overly long, or simply unrelated because T5-small has never been told what our support policy or tone should be.\n" + ], + "metadata": { + "id": "ponHU0RFA8Ms" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"t5-small\")\n", + "model_base = AutoModelForSeq2SeqLM.from_pretrained(\"t5-small\")\n", + "\n", + "prompt = \"reply to this customer's email: My order arrived late and I want a refund.\"\n", + "inputs = tokenizer(prompt, return_tensors=\"pt\")\n", + "\n", + "# Greedy decode to keep things deterministic and short\n", + "outputs = model_base.generate(**inputs, max_new_tokens=120)\n", + "print(\"Vanilla T5-small says:\\n\")\n", + "print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n" + ], + "metadata": { + "id": "Qjw3r_bMA631" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tkYjmNZ7809N" + }, + "source": [ + "## 3 Load CSV Dataset and transform it to HF Dataset\n", "\n", + "Upload to Colab filesystem the file of our dataset: `files/customer_support_lora_dataset_250`. \n", "Our CSV file has two columns:\n", "\n", "| Column name | What it contains |\n", @@ -93,29 +146,33 @@ "df = pd.read_csv(\"customer_support_lora_dataset_250.csv\")\n", "ds = Dataset.from_pandas(df)\n", "\n", - "print(\"Sample row:\\n\", ds[0]) # quick sanity check" + "print(\"Sample row:\") # quick sanity check\n", + "ds[0]" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "2HlULnbn809P" + }, "source": [ "## 3 Tokenisation and Label Preparation\n", "\n", - "Transformer models can’t read raw text, they need **token IDs**.\n", + "Transformer models can't read raw text, they need **token IDs**.\n", "For sequence-to-sequence models like T5 we must prepare **two** sequences:\n", "\n", - "1. **Source** – the customer request (`input_text`) \n", - "2. **Target** – the desired reply (`target_text`) \n", + "1. **Source** - the customer request (`input_text`) \n", + "2. **Target** - the desired reply (`target_text`) \n", "\n", "### Key details:\n", "\n", "- We call `tokenizer.as_target_tokenizer()` so the decoder uses its own special prefix tokens. \n", - "- We truncate to 128 tokens to keep batches small on modest GPUs. \n", + "- We truncate to 128 tokens to keep batches small on modest GPUs.\n", "\n", "### What exactly are \"tokens\" (the 128-token limit)? \n", "A token is not a word or a single character.\n", - "Transformers work on sub-word units produced by a tokenizer (for T5 that’s a SentencePiece model with a 32 k-item vocabulary). The rules are learned from large corpora and try to strike a balance:\n", + "Transformers work on sub-word units produced by a tokenizer (for T5 that's a SentencePiece model with a 32 k-item vocabulary). The rules are learned from large corpora and try to strike a balance:\n", + "\n", "| Example text | Tokens generated | Notes |\n", "| ------------- | ---------------------- | ------------------------------------- |\n", "| `tracking` | `▁track`, `ing` | the leading “▁” marks a word start |\n", @@ -123,9 +180,20 @@ "| `extra-large` | `▁extra`, `-`, `large` | punctuation becomes its own token |\n", "\n", "Because tokens can be full words or fragments, the length in tokens is usually 1.3-1.6x shorter than counting raw characters but longer than counting full words.\n", - "A 128-token limit therefore fits roughly 75–100 English words (fewer if the text contains many rare names, URLs, or emojis that split into multiple tokens)." + "A 128-token limit therefore fits roughly 75-100 English words (fewer if the text contains many rare names, URLs, or emojis that split into multiple tokens)." ] }, + { + "cell_type": "code", + "source": [ + "ds[0][\"input_text\"]" + ], + "metadata": { + "id": "tRmH0hZpCK0p" + }, + "execution_count": null, + "outputs": [] + }, { "cell_type": "code", "execution_count": null, @@ -149,15 +217,17 @@ "\n", "ds_tok = ds.map(preprocess, remove_columns=ds.column_names)\n", "\n", - "\n", - "print(\"Keys:\", ds_tok[0].keys(),\n", - " \"| source len:\", len(ds_tok[0][\"input_ids\"]),\n", - " \"| target len:\", len(ds_tok[0][\"labels\"]))" + "print(\"Input: \", ds[0][\"input_text\"])\n", + "print(\"Input tokens: \", len(ds_tok[0][\"input_ids\"]))\n", + "print(\"Target: \", ds[0][\"target_text\"])\n", + "print(\"Target tokens: \", len(ds_tok[0][\"labels\"]))\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "LU43xk3P809R" + }, "source": [ "## 4 Build Base Model and LoRA Configuration\n", "\n", @@ -217,7 +287,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "ezlVpv2R809S" + }, "source": [ "## 5 Training Arguments and Trainer Loop\n", "\n", @@ -264,7 +336,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "WD8UKp4g809T" + }, "source": [ "## 6 Save Adapter and Tokenizer\n", "\n", @@ -279,13 +353,15 @@ }, "outputs": [], "source": [ - "trainer.model.save_pretrained(\"t5-small-lora-adapter\") \n", + "trainer.model.save_pretrained(\"t5-small-lora-adapter\")\n", "tokenizer.save_pretrained(\"t5-small-lora-adapter\")" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "H8U36q-C809T" + }, "source": [ "## 7 Load LoRA-Adapted Model for Inference\n", "\n", @@ -321,7 +397,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "RI_MC1cU809U" + }, "source": [ "## 8 Side-by-Side Evaluation (Base vs LoRA)\n", "\n", @@ -364,14 +442,14 @@ " return tokenizer.decode(out[0], skip_special_tokens=True)\n", "\n", "test_prompts = [\n", - " \"generate structured_reply: My order arrived late. I want a refund.\",\n", - " \"generate structured_reply: The product I received is damaged. What can I do?\",\n", - " \"generate structured_reply: I received the wrong item in my order.\",\n", - " \"generate structured_reply: How can I return an item I purchased last week?\",\n", - " \"generate structured_reply: I never received my order.\",\n", - " \"generate structured_reply: Why was I charged twice for my order?\",\n", - " \"generate structured_reply: I need help tracking my shipment.\",\n", - " \"generate structured_reply: Can I exchange my item for a different size?\"\n", + " \"Compose a response to this customer email: My order arrived late. I want a refund.\",\n", + " \"Draft a reply to this customer message: The product I received is damaged. What can I do?\",\n", + " \"Write a response to this email from a client: I received the wrong item in my order.\",\n", + " \"Create a reply for this customer's email: How can I return an item I purchased last week?\",\n", + " \"Formulate a response to the customer's email: I never received my order.\",\n", + " \"Respond to this message from the customer: Why was I charged twice for my order?\",\n", + " \"Prepare a reply to this client email: I need help tracking my shipment.\",\n", + " \"Construct a response for the customer's message: Can I exchange my item for a different size?\"\n", "]\n", "\n", "records = []\n", @@ -388,9 +466,11 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "csHeHzww809V" + }, "source": [ - "## 9 Next Steps \n", + "## 9 Next Steps\n", "\n", "1. **Quantisation**: combine LoRA with 8-bit weights using `bitsandbytes` library to shrink disk size and speed up inference. \n", "2. **Hyper-parameter search**: try different ranks (`r`) and target modules (add **k** and **o** matrices) for possibly better accuracy. \n", @@ -403,7 +483,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "TG70O8z6809V" + }, "source": [] } ], @@ -430,4 +512,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file From 6cee9a55a179740cb043089fd1786dc24f829eef Mon Sep 17 00:00:00 2001 From: rndg Date: Thu, 7 Aug 2025 16:21:07 +0200 Subject: [PATCH 57/70] fix notebooks --- Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb | 4 +-- .../AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb | 35 +++++++------------ 2 files changed, 14 insertions(+), 25 deletions(-) diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb index 04bbb67..9936556 100644 --- a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb +++ b/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb @@ -21,7 +21,7 @@ "By the end you'll understand how tiny changes in wording can dramatically change an LLM's answer.\n", "\n", "## What is Prompt-Engineering?\n", - "Because large language models don't have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.is called prompt engineering. Good prompts boost accuracy and consistency without touching the model's weights." + "Because large language models don't have traditional \"APIs\", you steer them with natural-language prompts. Refining spacing, order, examples, and style (zero-shot, few-shot, chain-of-thought, role instructions, JSON schemas, etc.) is called prompt engineering. Good prompts boost accuracy and consistency without touching the model's weights." ] }, { @@ -456,4 +456,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb index 611f1f2..33913c6 100644 --- a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb +++ b/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb @@ -80,18 +80,23 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "ponHU0RFA8Ms" + }, "source": [ "## 2 Baseline Check: How well does vanilla T5-small handle our task?\n", "\n", "Before we train anything, let's ask the out-of-the-box model to draft a refund reply. \n", "Spoiler: its answer will be generic, overly long, or simply unrelated because T5-small has never been told what our support policy or tone should be.\n" - ], - "metadata": { - "id": "ponHU0RFA8Ms" - } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Qjw3r_bMA631" + }, + "outputs": [], "source": [ "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n", "\n", @@ -105,12 +110,7 @@ "outputs = model_base.generate(**inputs, max_new_tokens=120)\n", "print(\"Vanilla T5-small says:\\n\")\n", "print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n" - ], - "metadata": { - "id": "Qjw3r_bMA631" - }, - "execution_count": null, - "outputs": [] + ] }, { "cell_type": "markdown", @@ -183,17 +183,6 @@ "A 128-token limit therefore fits roughly 75-100 English words (fewer if the text contains many rare names, URLs, or emojis that split into multiple tokens)." ] }, - { - "cell_type": "code", - "source": [ - "ds[0][\"input_text\"]" - ], - "metadata": { - "id": "tRmH0hZpCK0p" - }, - "execution_count": null, - "outputs": [] - }, { "cell_type": "code", "execution_count": null, @@ -237,7 +226,7 @@ "\n", "| Parameter | Role | Here |\n", "|--------------|---------------------------------------------------------------|------|\n", - "| `r` | Rank of the low-rank matrices (higher = more capacity) | 8 |\n", + "| `r` | Rank of the low-rank matrices (higher = more learning capacity) | 8 |\n", "| `lora_alpha` | Scaling factor for the adapter’s update | 16 |\n", "| `target_modules` | Which weight matrices get adapters (we pick **q** & **v**) | [\"q\",\"v\"] |\n", "| `lora_dropout` | Regularisation inside adapters | 0.05 |\n", @@ -512,4 +501,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From 16f3d976b4e80bedee3e0ed6f6cc58672f8ffd50 Mon Sep 17 00:00:00 2001 From: rndg Date: Fri, 8 Aug 2025 09:22:09 +0200 Subject: [PATCH 58/70] update readme --- Academy/AI/3-LLM/README.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/Academy/AI/3-LLM/README.md b/Academy/AI/3-LLM/README.md index 4dad1b2..9c5808e 100644 --- a/Academy/AI/3-LLM/README.md +++ b/Academy/AI/3-LLM/README.md @@ -69,6 +69,29 @@ We'll leverage two big providers for this lab: ## Run the notebooks in Colab +### 1 Clone the Repository + +To start, clone the lab repository by running the following command in the terminal: + +```sh + git clone https://github.com/kiratech/labs.git +``` + +### 2 Checkout the Lab Branch + +After cloning the repository, checkout the `academy-ai` branch: + +```sh + git checkout academy-ai +``` + +Then, navigate to the project folder: + +```sh + cd labs/Academy/AI/3-LLM +``` + +This folder contains resources related to this lab. Open Google Colab and import, everything at root level, the content of the folders notebooks and files. From now you can continue on the notebooks. From 8a6d543ba54579041c9b87ed694c6a194a62a1ee Mon Sep 17 00:00:00 2001 From: Raoul Scarazzini Date: Mon, 11 Aug 2025 11:28:06 +0200 Subject: [PATCH 59/70] [WAI] Restructure folder position Since the AI stuff is composed by workshop labs, let's put it under Workshops folder. 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rename from Academy/AI/2-ML-Model-Lifecycle/tests/test_data.py rename to Workshops/AI/02-ML-Model-Lifecycle/tests/test_data.py diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py b/Workshops/AI/02-ML-Model-Lifecycle/tests/test_serve.py similarity index 100% rename from Academy/AI/2-ML-Model-Lifecycle/tests/test_serve.py rename to Workshops/AI/02-ML-Model-Lifecycle/tests/test_serve.py diff --git a/Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py b/Workshops/AI/02-ML-Model-Lifecycle/tests/test_train.py similarity index 100% rename from Academy/AI/2-ML-Model-Lifecycle/tests/test_train.py rename to Workshops/AI/02-ML-Model-Lifecycle/tests/test_train.py diff --git a/Academy/AI/3-LLM/README.md b/Workshops/AI/03-Large-Language-Models/README.md similarity index 100% rename from Academy/AI/3-LLM/README.md rename to Workshops/AI/03-Large-Language-Models/README.md diff --git a/Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv b/Workshops/AI/03-Large-Language-Models/files/customer_support_lora_dataset_250.csv similarity index 100% rename from Academy/AI/3-LLM/files/customer_support_lora_dataset_250.csv rename to Workshops/AI/03-Large-Language-Models/files/customer_support_lora_dataset_250.csv diff --git a/Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb b/Workshops/AI/03-Large-Language-Models/notebooks/1_LabLLM.ipynb similarity index 100% rename from Academy/AI/3-LLM/notebooks/1_LabLLM.ipynb rename to Workshops/AI/03-Large-Language-Models/notebooks/1_LabLLM.ipynb diff --git a/Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb b/Workshops/AI/03-Large-Language-Models/notebooks/2_LabLLM_LoRA.ipynb similarity index 100% rename from Academy/AI/3-LLM/notebooks/2_LabLLM_LoRA.ipynb rename to Workshops/AI/03-Large-Language-Models/notebooks/2_LabLLM_LoRA.ipynb From ba2bbc30886139d96c629332fde13258d0482d4f Mon Sep 17 00:00:00 2001 From: rndg <37615999+rndg@users.noreply.github.com> Date: Thu, 14 Aug 2025 09:36:29 +0200 Subject: [PATCH 60/70] Update README.md --- Workshops/AI/03-Large-Language-Models/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/Workshops/AI/03-Large-Language-Models/README.md b/Workshops/AI/03-Large-Language-Models/README.md index 9c5808e..56b3101 100644 --- a/Workshops/AI/03-Large-Language-Models/README.md +++ b/Workshops/AI/03-Large-Language-Models/README.md @@ -88,7 +88,7 @@ After cloning the repository, checkout the `academy-ai` branch: Then, navigate to the project folder: ```sh - cd labs/Academy/AI/3-LLM + cd labs/Workshops/AI/03-Large-Language-Models ``` This folder contains resources related to this lab. @@ -99,5 +99,6 @@ From now you can continue on the notebooks. ## Suggested readings and references -- [OpenAI Cookbook – GPT‑4 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide): a new prompting guide that lays out a practical structure for building powerful prompts, especially with GPT-4.1. It’s short, clear, and highly effective for anyone working with agents, structured outputs, tool use, or reasoning-heavy tasks. +- [OpenAI Cookbook – GPT‑4 Prompting Guide](https://cookbook.openai.com/examples/gpt4-1_prompting_guide): a prompting guide that lays out a practical structure for building powerful prompts, especially with GPT-4.1. It’s short, clear, and highly effective for anyone working with agents, structured outputs, tool use, or reasoning-heavy tasks. +- [OpenAI Cookbook – GPT‑5 Prompting Guide](https://cookbook.openai.com/examples/gpt-5/gpt-5_prompting_guide): New guide! - [Google Prompt Engineering pdf](https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view): whether you're technical or non-technical, this might be one of the most useful prompt engineering resources out there right now. Google just published a 68-page whitepaper focused on Prompt Engineering (focused on API users), and it goes deep on structure, formatting, config settings, and real examples. From fce57cc8994c73755beb1698dbdf3bbcd991f844 Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 19 Aug 2025 21:16:51 +0200 Subject: [PATCH 61/70] add chroma rag --- Workshops/AI/04-Agents-and-RAG/.env.example | 14 ++ Workshops/AI/04-Agents-and-RAG/.gitignore | 177 ++++++++++++++++++ Workshops/AI/04-Agents-and-RAG/README.md | 25 +++ .../data/policies/return_refunds.md | 9 + .../data/policies/shipping.md | 6 + .../AI/04-Agents-and-RAG/requirements.txt | 6 + .../04-Agents-and-RAG/src/lab_service/app.py | 10 + .../src/lab_service/config.py | 13 ++ .../src/lab_service/rag_api.py | 21 +++ .../src/lab_service/rag_core.py | 45 +++++ 10 files changed, 326 insertions(+) create mode 100644 Workshops/AI/04-Agents-and-RAG/.env.example create mode 100644 Workshops/AI/04-Agents-and-RAG/.gitignore create mode 100644 Workshops/AI/04-Agents-and-RAG/README.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/return_refunds.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/shipping.md create mode 100644 Workshops/AI/04-Agents-and-RAG/requirements.txt create mode 100644 Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py create mode 100644 Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py create mode 100644 Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py create mode 100644 Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py diff --git a/Workshops/AI/04-Agents-and-RAG/.env.example b/Workshops/AI/04-Agents-and-RAG/.env.example new file mode 100644 index 0000000..fd40aa6 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/.env.example @@ -0,0 +1,14 @@ +# LLM settings (OpenAI-compatible provider) +OPENAI_API_KEY= +OPENAI_API_BASE= + +# Optional compatibility for other clients (leave blank if not used) +OPENAI_BASE_URL= + +# Model IDs +MODEL_ID=mistralai/Mistral-7B-Instruct-v0.3 +EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2 + +# Paths +PERSIST_DIR=artifacts/chroma +KB_DIR=data/policies diff --git a/Workshops/AI/04-Agents-and-RAG/.gitignore b/Workshops/AI/04-Agents-and-RAG/.gitignore new file mode 100644 index 0000000..156afb9 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/.gitignore @@ -0,0 +1,177 @@ +# local chroma +artifacts/ + +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# UV +# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +#uv.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/latest/usage/project/#working-with-version-control +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +# Ruff stuff: +.ruff_cache/ + +# PyPI configuration file +.pypirc \ No newline at end of file diff --git a/Workshops/AI/04-Agents-and-RAG/README.md b/Workshops/AI/04-Agents-and-RAG/README.md new file mode 100644 index 0000000..bea471a --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/README.md @@ -0,0 +1,25 @@ +# AI Academy - Agents & RAG (Step 1) + +Questo step crea l'app FastAPI con: +- Endpoint `/rag/ingest` per indicizzare i documenti markdown in `data/policies/` (Chroma + sentence-transformers) +- Endpoint `/rag/search` per recupero top-k (senza generazione LLM) + +## Setup +```bash +cd ai-academy-agents-rag +python -m venv .venv && source .venv/bin/activate # oppure conda/mamba +pip install -r requirements.txt +cp .env.example .env +uvicorn src.lab_service.app:app --reload +``` + +## Prova +1) Ingestione +```bash +curl -X POST http://localhost:8000/rag/ingest -H "Content-Type: application/json" -d '{"reset": true}' +``` + +2) Ricerca +```bash +curl -s http://localhost:8000/rag/search -H "Content-Type: application/json" -d '{"query":"ritardo spedizione rimborso", "k": 3}' | jq +``` diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/return_refunds.md b/Workshops/AI/04-Agents-and-RAG/data/policies/return_refunds.md new file mode 100644 index 0000000..6459714 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/return_refunds.md @@ -0,0 +1,9 @@ +# Returns & Refunds Policy + +- Returns are accepted within 30 days from purchase with a valid receipt. +- Refunds are processed within 7 business days after approval. +- If delivery is delayed by more than 3 days beyond the estimated date, the customer receives a **10% discount voucher** for the next order. +- Items damaged on arrival: free replacement or full refund. + +## Exceptions +- Customized or personalized items are non-refundable unless a manufacturing defect is proven. diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/shipping.md b/Workshops/AI/04-Agents-and-RAG/data/policies/shipping.md new file mode 100644 index 0000000..6e16b97 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/shipping.md @@ -0,0 +1,6 @@ +# Shipping Policy + +- Standard shipping: 3-5 business days. +- Express shipping: 1-2 business days. +- Tracking is included for all orders. +- In case of **confirmed loss** by the carrier: full refund or free reshipment. diff --git a/Workshops/AI/04-Agents-and-RAG/requirements.txt b/Workshops/AI/04-Agents-and-RAG/requirements.txt new file mode 100644 index 0000000..71024a9 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/requirements.txt @@ -0,0 +1,6 @@ +fastapi==0.115.0 +uvicorn[standard]==0.30.5 +pydantic==2.8.2 +chromadb==0.5.5 +sentence-transformers==3.0.1 +python-dotenv==1.0.1 diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py new file mode 100644 index 0000000..28183f6 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py @@ -0,0 +1,10 @@ +from fastapi import FastAPI +from .rag_api import router as rag_router + +app = FastAPI(title="AI Academy - RAG & Agents API (Step 1)") + +@app.get("/healthz") +def healthz(): + return {"status": "ok"} + +app.include_router(rag_router) diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py new file mode 100644 index 0000000..596f03e --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py @@ -0,0 +1,13 @@ +import os +from dotenv import load_dotenv + +load_dotenv() + +PERSIST_DIR = os.getenv("PERSIST_DIR", "artifacts/chroma") +KB_DIR = os.getenv("KB_DIR", "data/policies") +EMB_MODEL = os.getenv("EMB_MODEL", "sentence-transformers/all-MiniLM-L6-v2") + +# Step 2 (LLM) placeholders +OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") +OPENAI_BASE_URL = os.getenv("OPENAI_BASE_URL") +MODEL_ID = os.getenv("MODEL_ID", "mistralai/Mistral-7B-Instruct-v0.3") diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py new file mode 100644 index 0000000..31c4c97 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py @@ -0,0 +1,21 @@ +from fastapi import APIRouter +from pydantic import BaseModel +from .rag_core import build_index, retrieve + +router = APIRouter(prefix="/rag", tags=["RAG"]) + +class IngestReq(BaseModel): + source_dir: str | None = None + reset: bool = False + +@router.post("/ingest") +def ingest(req: IngestReq): + return build_index(req.source_dir, req.reset) + +class SearchReq(BaseModel): + query: str + k: int = 4 + +@router.post("/search") +def search(req: SearchReq): + return {"query": req.query, "results": retrieve(req.query, k=req.k)} diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py new file mode 100644 index 0000000..417863d --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py @@ -0,0 +1,45 @@ +from pathlib import Path +from typing import Dict, Any +from chromadb import PersistentClient +from chromadb.utils import embedding_functions +from .config import PERSIST_DIR, KB_DIR, EMB_MODEL + +def _chroma(): + return PersistentClient(path=PERSIST_DIR) + +def _collection(): + ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=EMB_MODEL) + return _chroma().get_or_create_collection("policies", embedding_function=ef) + +def build_index(source_dir: str | None = None, reset: bool = False) -> Dict[str, Any]: + source = Path(source_dir or KB_DIR) + col = _collection() + if reset: + _chroma().delete_collection("policies") + col = _collection() + files_indexed = 0 + docs, ids, metas = [], [], [] + for p in source.rglob("*"): + if p.suffix.lower() in {".md", ".txt"}: + text = p.read_text(encoding="utf-8", errors="ignore") + # simple chunking for demo + chunks = [text[i:i+2000] for i in range(0, len(text), 1600)] + for k, ch in enumerate(chunks): + ids.append(f"{p.stem}-{files_indexed}-{k}") + docs.append(ch) + metas.append({"source": str(p), "chunk": k}) + files_indexed += 1 + if docs: + col.add(documents=docs, ids=ids, metadatas=metas) + return {"indexed_files": files_indexed, "chunks": len(docs)} + +def retrieve(query: str, k: int = 4): + res = _collection().query(query_texts=[query], n_results=k) + docs = res.get("documents", [[]])[0] + metas = res.get("metadatas", [[]])[0] + dists = res.get("distances", [[]])[0] + out = [] + for d, m, s in zip(docs, metas, dists): + snippet = d[:400] + ("..." if len(d) > 400 else "") + out.append({"text": d, "snippet": snippet, "meta": m, "score": float(s)}) + return out From 8f6435ec0a9164687c86d932594bb87fb1c23d05 Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 20 Aug 2025 21:40:17 +0200 Subject: [PATCH 62/70] working agent --- Workshops/AI/04-Agents-and-RAG/.env.example | 14 +-- Workshops/AI/04-Agents-and-RAG/.gitignore | 1 + Workshops/AI/04-Agents-and-RAG/README.md | 38 +++--- .../04-Agents-and-RAG/requirements-freeze.txt | 118 ++++++++++++++++++ .../AI/04-Agents-and-RAG/requirements.txt | 22 +++- .../src/lab_service/agent_api.py | 13 ++ .../src/lab_service/agent_graph.py | 92 ++++++++++++++ .../04-Agents-and-RAG/src/lab_service/app.py | 4 +- .../src/lab_service/config.py | 9 +- .../src/lab_service/rag_api.py | 10 +- .../src/lab_service/rag_core.py | 88 ++++++++++++- 11 files changed, 362 insertions(+), 47 deletions(-) create mode 100644 Workshops/AI/04-Agents-and-RAG/requirements-freeze.txt create mode 100644 Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_api.py create mode 100644 Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_graph.py diff --git a/Workshops/AI/04-Agents-and-RAG/.env.example b/Workshops/AI/04-Agents-and-RAG/.env.example index fd40aa6..9a03b23 100644 --- a/Workshops/AI/04-Agents-and-RAG/.env.example +++ b/Workshops/AI/04-Agents-and-RAG/.env.example @@ -1,14 +1,10 @@ -# LLM settings (OpenAI-compatible provider) -OPENAI_API_KEY= -OPENAI_API_BASE= +# Hugging Face token (Settings → Access Tokens) +HUGGINGFACEHUB_API_TOKEN=hf_... -# Optional compatibility for other clients (leave blank if not used) -OPENAI_BASE_URL= - -# Model IDs -MODEL_ID=mistralai/Mistral-7B-Instruct-v0.3 -EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2 +# Model to use (must be accessible with your token) +HF_MODEL_ID=mistralai/Mistral-7B-Instruct-v0.3 # Paths PERSIST_DIR=artifacts/chroma KB_DIR=data/policies +EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2 diff --git a/Workshops/AI/04-Agents-and-RAG/.gitignore b/Workshops/AI/04-Agents-and-RAG/.gitignore index 156afb9..6a8d2ce 100644 --- a/Workshops/AI/04-Agents-and-RAG/.gitignore +++ b/Workshops/AI/04-Agents-and-RAG/.gitignore @@ -1,5 +1,6 @@ # local chroma artifacts/ +notes.py # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/Workshops/AI/04-Agents-and-RAG/README.md b/Workshops/AI/04-Agents-and-RAG/README.md index bea471a..bd97670 100644 --- a/Workshops/AI/04-Agents-and-RAG/README.md +++ b/Workshops/AI/04-Agents-and-RAG/README.md @@ -1,25 +1,19 @@ -# AI Academy - Agents & RAG (Step 1) +# 1) Ingest +curl -X POST http://localhost:8000/rag/ingest \ + -H "Content-Type: application/json" \ + -d '{"source_dir":"data/policies","reset":true}' -Questo step crea l'app FastAPI con: -- Endpoint `/rag/ingest` per indicizzare i documenti markdown in `data/policies/` (Chroma + sentence-transformers) -- Endpoint `/rag/search` per recupero top-k (senza generazione LLM) +# 2) Search +curl -s http://localhost:8000/rag/search \ + -H "Content-Type: application/json" \ + -d '{"query":"delayed delivery refund","k":3}' | jq -## Setup -```bash -cd ai-academy-agents-rag -python -m venv .venv && source .venv/bin/activate # oppure conda/mamba -pip install -r requirements.txt -cp .env.example .env -uvicorn src.lab_service.app:app --reload -``` +# 3) RAG answer with citations +curl -s http://localhost:8000/rag/query \ + -H "Content-Type: application/json" \ + -d '{"question":"My package arrived 5 days late. Do I get a refund or a voucher?"}' | jq -## Prova -1) Ingestione -```bash -curl -X POST http://localhost:8000/rag/ingest -H "Content-Type: application/json" -d '{"reset": true}' -``` - -2) Ricerca -```bash -curl -s http://localhost:8000/rag/search -H "Content-Type: application/json" -d '{"query":"ritardo spedizione rimborso", "k": 3}' | jq -``` +# 4) Agent (LangGraph) +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"My package is 5 days late. If policy requires, open a ticket.","thread_id":"class-session"}' | jq diff --git a/Workshops/AI/04-Agents-and-RAG/requirements-freeze.txt b/Workshops/AI/04-Agents-and-RAG/requirements-freeze.txt new file mode 100644 index 0000000..475332d --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/requirements-freeze.txt @@ -0,0 +1,118 @@ +annotated-types==0.7.0 +anyio==4.10.0 +attrs==25.3.0 +backoff==2.2.1 +bcrypt==4.3.0 +build==1.3.0 +cachetools==5.5.2 +certifi==2025.8.3 +charset-normalizer==3.4.3 +chromadb==1.0.20 +click==8.2.1 +coloredlogs==15.0.1 +distro==1.9.0 +durationpy==0.10 +fastapi==0.116.1 +filelock==3.19.1 +flatbuffers==25.2.10 +fsspec==2025.7.0 +google-auth==2.40.3 +googleapis-common-protos==1.70.0 +grpcio==1.74.0 +h11==0.16.0 +hf-xet==1.1.8 +httpcore==1.0.9 +httptools==0.6.4 +httpx==0.28.1 +huggingface-hub==0.34.4 +humanfriendly==10.0 +idna==3.10 +importlib_metadata==8.7.0 +importlib_resources==6.5.2 +Jinja2==3.1.6 +joblib==1.5.1 +jsonpatch==1.33 +jsonpointer==3.0.0 +jsonschema==4.25.1 +jsonschema-specifications==2025.4.1 +kubernetes==33.1.0 +langchain==0.3.27 +langchain-core==0.3.74 +langchain-huggingface==0.3.1 +langchain-text-splitters==0.3.9 +langgraph==0.6.6 +langgraph-checkpoint==2.1.1 +langgraph-prebuilt==0.6.4 +langgraph-sdk==0.2.2 +langsmith==0.4.15 +markdown-it-py==4.0.0 +MarkupSafe==3.0.2 +mdurl==0.1.2 +mmh3==5.2.0 +mpmath==1.3.0 +networkx==3.5 +numpy==2.3.2 +oauthlib==3.3.1 +onnxruntime==1.22.1 +opentelemetry-api==1.36.0 +opentelemetry-exporter-otlp-proto-common==1.36.0 +opentelemetry-exporter-otlp-proto-grpc==1.36.0 +opentelemetry-proto==1.36.0 +opentelemetry-sdk==1.36.0 +opentelemetry-semantic-conventions==0.57b0 +orjson==3.11.2 +ormsgpack==1.10.0 +overrides==7.7.0 +packaging==25.0 +pillow==11.3.0 +posthog==5.4.0 +protobuf==6.32.0 +pyasn1==0.6.1 +pyasn1_modules==0.4.2 +pybase64==1.4.2 +pydantic==2.11.7 +pydantic_core==2.33.2 +Pygments==2.19.2 +PyPika==0.48.9 +pyproject_hooks==1.2.0 +python-dateutil==2.9.0.post0 +python-dotenv==1.1.1 +PyYAML==6.0.2 +referencing==0.36.2 +regex==2025.7.34 +requests==2.32.5 +requests-oauthlib==2.0.0 +requests-toolbelt==1.0.0 +rich==14.1.0 +rpds-py==0.27.0 +rsa==4.9.1 +safetensors==0.6.2 +scikit-learn==1.7.1 +scipy==1.16.1 +sentence-transformers==5.1.0 +sentencepiece==0.2.1 +setuptools==80.9.0 +shellingham==1.5.4 +six==1.17.0 +sniffio==1.3.1 +SQLAlchemy==2.0.43 +starlette==0.47.2 +sympy==1.14.0 +tenacity==9.1.2 +threadpoolctl==3.6.0 +tokenizers==0.21.4 +torch==2.8.0 +tqdm==4.67.1 +transformers==4.55.2 +typer==0.16.1 +typing-inspection==0.4.1 +typing_extensions==4.14.1 +urllib3==2.5.0 +uvicorn==0.35.0 +uvloop==0.21.0 +watchfiles==1.1.0 +websocket-client==1.8.0 +websockets==15.0.1 +xxhash==3.5.0 +zipp==3.23.0 +zstandard==0.24.0 diff --git a/Workshops/AI/04-Agents-and-RAG/requirements.txt b/Workshops/AI/04-Agents-and-RAG/requirements.txt index 71024a9..7c1ab30 100644 --- a/Workshops/AI/04-Agents-and-RAG/requirements.txt +++ b/Workshops/AI/04-Agents-and-RAG/requirements.txt @@ -1,6 +1,16 @@ -fastapi==0.115.0 -uvicorn[standard]==0.30.5 -pydantic==2.8.2 -chromadb==0.5.5 -sentence-transformers==3.0.1 -python-dotenv==1.0.1 +fastapi +uvicorn[standard] +pydantic +chromadb +sentence-transformers +python-dotenv + +# Hugging Face + agent stack +huggingface_hub +langchain +langchain-core +langchain-huggingface +langgraph +sentencepiece +transformers +tokenizers diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_api.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_api.py new file mode 100644 index 0000000..047117a --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_api.py @@ -0,0 +1,13 @@ +from fastapi import APIRouter +from pydantic import BaseModel +from .agent_graph import run_agent_with_langgraph + +router = APIRouter(prefix="/agent", tags=["Agent"]) + +class AskReq(BaseModel): + question: str + thread_id: str | None = None + +@router.post("/ask") +def ask(req: AskReq): + return run_agent_with_langgraph(req.question, thread_id=req.thread_id) diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_graph.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_graph.py new file mode 100644 index 0000000..5e5f280 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_graph.py @@ -0,0 +1,92 @@ +from typing import TypedDict, Annotated +import operator +import json +from langgraph.graph import StateGraph, END +from langgraph.checkpoint.memory import MemorySaver +from langgraph.prebuilt import ToolNode +from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage +from langchain_core.tools import tool +from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint +from .config import HF_MODEL_ID +from .rag_core import retrieve + +@tool +def search_policies(query: str) -> str: + """Search the internal knowledge base (RAG) and return a JSON list of + matches with fields: source, chunk, text.""" + hits = retrieve(query, k=4) + return json.dumps( + [{"source": h["meta"]["source"], "chunk": h["meta"]["chunk"], "text": h["text"]} for h in hits] + ) + +@tool +def calc(expression: str) -> str: + """Evaluate a simple arithmetic expression (e.g., '12*(3+4)'). + Returns the stringified result or an error message.""" + try: + out = str(eval(expression, {"__builtins__": {}})) + except Exception as e: + out = f"Error: {e}" + return out + +@tool +def create_ticket(category: str, description: str) -> str: + """Simulate creating a support ticket and return a JSON payload with + ticket_id and status.""" + ticket_id = "TKT-" + str(abs(hash(category + description)) % 10_000) + return json.dumps({"ticket_id": ticket_id, "status": "created"}) + +TOOLS = [search_policies, calc, create_ticket] + +class AgentState(TypedDict): + messages: Annotated[list[AnyMessage], operator.add] + +# Hugging Face chat model +llm = HuggingFaceEndpoint( + repo_id=HF_MODEL_ID, + task="text-generation", + max_new_tokens=512, + do_sample=False, +) +chat_model = ChatHuggingFace(llm=llm) # LangChain wrapper for HF chat models +tool_node = ToolNode(TOOLS) + +def call_model(state: AgentState) -> AgentState: + response = chat_model.invoke(state["messages"]) + return {"messages": [response]} + +def route_after_model(state: AgentState) -> str: + last = state["messages"][-1] + if getattr(last, "tool_calls", None): + return "tools" + return END + +_graph = StateGraph(AgentState) +_graph.add_node("agent", call_model) +_graph.add_node("tools", tool_node) +_graph.add_edge("tools", "agent") +_graph.add_conditional_edges("agent", route_after_model) +_graph.set_entry_point("agent") + +checkpointer = MemorySaver() +agent_graph = _graph.compile(checkpointer=checkpointer) + +SYSTEM_PROMPT = ( + "You are a support agent. First, outline a brief plan, then use tools when needed:\n" + "- search_policies for KB-based answers (cite sources)\n" + "- calc for small computations\n" + "- create_ticket when escalation is required by policy\n" + "Respond in concise English. Do not fabricate facts not present in sources." +) + +def run_agent_with_langgraph(question: str, thread_id: str | None = None) -> dict: + config = {"configurable": {"thread_id": thread_id or "default"}} + initial: AgentState = { + "messages": [ + SystemMessage(content=SYSTEM_PROMPT), + HumanMessage(content=question), + ] + } + final_state = agent_graph.invoke(initial, config=config) + last = final_state["messages"][-1] + return {"final_answer": getattr(last, "content", ""), "thread_id": config["configurable"]["thread_id"]} diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py index 28183f6..dd6e1a7 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py @@ -1,10 +1,12 @@ from fastapi import FastAPI from .rag_api import router as rag_router +from .agent_api import router as agent_router -app = FastAPI(title="AI Academy - RAG & Agents API (Step 1)") +app = FastAPI(title="AI Academy - RAG & Agents API") @app.get("/healthz") def healthz(): return {"status": "ok"} app.include_router(rag_router) +app.include_router(agent_router) \ No newline at end of file diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py index 596f03e..563c6c9 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py @@ -1,13 +1,12 @@ import os from dotenv import load_dotenv - load_dotenv() +# Paths / embeddings PERSIST_DIR = os.getenv("PERSIST_DIR", "artifacts/chroma") KB_DIR = os.getenv("KB_DIR", "data/policies") EMB_MODEL = os.getenv("EMB_MODEL", "sentence-transformers/all-MiniLM-L6-v2") -# Step 2 (LLM) placeholders -OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") -OPENAI_BASE_URL = os.getenv("OPENAI_BASE_URL") -MODEL_ID = os.getenv("MODEL_ID", "mistralai/Mistral-7B-Instruct-v0.3") +# Hugging Face +HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") +HF_MODEL_ID = os.getenv("HF_MODEL_ID", "mistralai/Mistral-7B-Instruct-v0.3") diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py index 31c4c97..4eef994 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py @@ -1,6 +1,6 @@ from fastapi import APIRouter from pydantic import BaseModel -from .rag_core import build_index, retrieve +from .rag_core import build_index, retrieve, answer_with_citations, RAGResponse router = APIRouter(prefix="/rag", tags=["RAG"]) @@ -19,3 +19,11 @@ class SearchReq(BaseModel): @router.post("/search") def search(req: SearchReq): return {"query": req.query, "results": retrieve(req.query, k=req.k)} + +class QueryReq(BaseModel): + question: str + k: int = 4 + +@router.post("/query", response_model=RAGResponse) +def query(req: QueryReq): + return answer_with_citations(req.question, k=req.k) diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py index 417863d..7c01a8d 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py @@ -1,38 +1,57 @@ from pathlib import Path -from typing import Dict, Any +from typing import List, Dict, Any from chromadb import PersistentClient from chromadb.utils import embedding_functions -from .config import PERSIST_DIR, KB_DIR, EMB_MODEL +from pydantic import BaseModel +from huggingface_hub import InferenceClient +from .config import ( + PERSIST_DIR, + KB_DIR, + EMB_MODEL, + HUGGINGFACEHUB_API_TOKEN, + HF_MODEL_ID, +) + + +# --------------------------- +# Vector store (Chroma) +# --------------------------- def _chroma(): return PersistentClient(path=PERSIST_DIR) + def _collection(): ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=EMB_MODEL) return _chroma().get_or_create_collection("policies", embedding_function=ef) + def build_index(source_dir: str | None = None, reset: bool = False) -> Dict[str, Any]: source = Path(source_dir or KB_DIR) col = _collection() if reset: _chroma().delete_collection("policies") col = _collection() + files_indexed = 0 docs, ids, metas = [], [], [] for p in source.rglob("*"): if p.suffix.lower() in {".md", ".txt"}: text = p.read_text(encoding="utf-8", errors="ignore") # simple chunking for demo - chunks = [text[i:i+2000] for i in range(0, len(text), 1600)] + chunks = [text[i:i + 2000] for i in range(0, len(text), 1600)] for k, ch in enumerate(chunks): ids.append(f"{p.stem}-{files_indexed}-{k}") docs.append(ch) metas.append({"source": str(p), "chunk": k}) files_indexed += 1 + if docs: col.add(documents=docs, ids=ids, metadatas=metas) + return {"indexed_files": files_indexed, "chunks": len(docs)} + def retrieve(query: str, k: int = 4): res = _collection().query(query_texts=[query], n_results=k) docs = res.get("documents", [[]])[0] @@ -43,3 +62,66 @@ def retrieve(query: str, k: int = 4): snippet = d[:400] + ("..." if len(d) > 400 else "") out.append({"text": d, "snippet": snippet, "meta": m, "score": float(s)}) return out + + +# --------------------------- +# RAG answer with citations +# --------------------------- +class RAGResponse(BaseModel): + answer: str + citations: List[Dict[str, Any]] + + +def _format_sources(ctx: List[Dict[str, Any]]) -> str: + lines = [] + for i, c in enumerate(ctx, 1): + src = c["meta"]["source"] + ch = c["meta"]["chunk"] + lines.append(f"[{i}] {src} (chunk {ch})\n{c['text']}\n") + return "\n".join(lines) + + +def answer_with_citations(question: str, k: int = 4) -> RAGResponse: + """ + Use Hugging Face's OpenAI-compatible Router for chat completions. + The model stays a Hub repo id. We ask the model to return strict JSON; + if it doesn't, we fall back to attaching top-k sources. + """ + ctx = retrieve(question, k=k) + if not ctx: + return RAGResponse(answer="No documents found in the knowledge base.", citations=[]) + + system = ( + "You are a helpful assistant that must answer ONLY using the provided sources. " + "If the information is not present in the sources, explicitly say so. " + "Answer in concise English. Always include citations as [1], [2], ..." + ) + user = ( + f"Question: {question}\n\n" + f"Sources:\n{_format_sources(ctx)}\n\n" + "Return a valid JSON object with the following shape: " + "{'answer': str, 'citations': [{'source': str, 'chunk': int}]}" + ) + + client = InferenceClient(provider="novita", api_key=HUGGINGFACEHUB_API_TOKEN) + + chat = client.chat.completions.create( + model=HF_MODEL_ID, # e.g., "meta-llama/Meta-Llama-3-8B-Instruct" + messages=[{"role":"system","content":system}, + {"role":"user","content":user}], + temperature=0.2, + max_tokens=512, + ) + + content = chat.choices[0].message.content or "" + + import json + try: + data = json.loads(content) + ans = data.get("answer", "") + cits = data.get("citations", []) + except Exception: + ans = content + cits = [{"source": c["meta"]["source"], "chunk": c["meta"]["chunk"]} for c in ctx] + + return RAGResponse(answer=ans, citations=cits) \ No newline at end of file From 7d2099cb12d53f413a268608238fe62b770a1245 Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Nov 2025 21:21:50 +0100 Subject: [PATCH 63/70] full working lab --- Workshops/AI/04-Agents-and-RAG/.env.example | 16 +- Workshops/AI/04-Agents-and-RAG/.gitignore | 1 + .../data/policies/00_index.md | 68 +++++++ .../data/policies/10_returns.md | 24 +++ .../data/policies/20_shipping_sla.md | 27 +++ .../data/policies/30_digital.md | 15 ++ .../data/policies/40_loyalty.md | 17 ++ .../data/policies/50_ticketing.md | 21 ++ .../data/policies/90_examples.md | 12 ++ .../data/policies/return_refunds.md | 9 - .../data/policies/shipping.md | 6 - .../data/tickets/TKT-7180.json | 7 + ...ements-freeze.txt => requirements-lab.txt} | 0 .../AI/04-Agents-and-RAG/requirements.txt | 1 + .../src/lab_service/agent_api.py | 37 +++- .../src/lab_service/agent_graph.py | 186 ++++++++++++++++-- .../04-Agents-and-RAG/src/lab_service/app.py | 19 ++ .../src/lab_service/config.py | 29 ++- .../src/lab_service/models.py | 82 ++++++++ .../src/lab_service/rag_api.py | 80 ++++++-- .../src/lab_service/rag_core.py | 154 +++++++++++++-- 21 files changed, 724 insertions(+), 87 deletions(-) create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/10_returns.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/20_shipping_sla.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/30_digital.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/40_loyalty.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/50_ticketing.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/90_examples.md delete mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/return_refunds.md delete mode 100644 Workshops/AI/04-Agents-and-RAG/data/policies/shipping.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/tickets/TKT-7180.json rename Workshops/AI/04-Agents-and-RAG/{requirements-freeze.txt => requirements-lab.txt} (100%) create mode 100644 Workshops/AI/04-Agents-and-RAG/src/lab_service/models.py diff --git a/Workshops/AI/04-Agents-and-RAG/.env.example b/Workshops/AI/04-Agents-and-RAG/.env.example index 9a03b23..cd54bf2 100644 --- a/Workshops/AI/04-Agents-and-RAG/.env.example +++ b/Workshops/AI/04-Agents-and-RAG/.env.example @@ -1,10 +1,8 @@ -# Hugging Face token (Settings → Access Tokens) -HUGGINGFACEHUB_API_TOKEN=hf_... +# Paths / embeddings +PERSIST_DIR = "artifacts/chroma" +KB_DIR = "data/policies" +EMB_MODEL = "sentence-transformers/all-MiniLM-L6-v2" -# Model to use (must be accessible with your token) -HF_MODEL_ID=mistralai/Mistral-7B-Instruct-v0.3 - -# Paths -PERSIST_DIR=artifacts/chroma -KB_DIR=data/policies -EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2 +# Hugging Face +HUGGINGFACEHUB_API_TOKEN = "hf_..." # substute with your token (On HF settings -> Access Tokens +HF_MODEL_ID = "openai/gpt-oss-20b" \ No newline at end of file diff --git a/Workshops/AI/04-Agents-and-RAG/.gitignore b/Workshops/AI/04-Agents-and-RAG/.gitignore index 6a8d2ce..8947a5a 100644 --- a/Workshops/AI/04-Agents-and-RAG/.gitignore +++ b/Workshops/AI/04-Agents-and-RAG/.gitignore @@ -1,6 +1,7 @@ # local chroma artifacts/ notes.py +.venv/ # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md b/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md new file mode 100644 index 0000000..67e1171 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md @@ -0,0 +1,68 @@ +# Policy Index & Precedence (CORE-000) — v1.1 +This file is the policy index and precedence guide. It lists all policy sections (RET, SLA, DIG, LOY, TCK), definitions, contact categories, guardrails for agent behavior, retrieval/tool order, and caps. Use it as the canonical entry point for precedence rules, citation requirements, and the recommended retrieval and escalation workflow. + +**Precedence (higher wins if there is a conflict):** +1. Legal/Regulatory Notices (if any) +2. Shipping & SLA (SLA-2xx) +3. Returns (RET-1xx) +4. Digital Products (DIG-3xx) +5. Loyalty (LOY-4xx) +6. Ticketing & Escalations (TCK-5xx) apply only as referenced by other sections + +**Definitions** +- Business day: Monday–Friday, local public holidays excluded. +- Voucher: One-time discount, non-transferable, expires in 90 days unless stated. + +**Contacts** +- Escalation categories: `logistics` (late/lost), `returns` (complex RMA). + +--- + +## Section Map (CORE-001) +- **RET-100** — Returns & Refunds (windows, DOA, bundles, refund method) +- **SLA-200** — Shipping, Late delivery vouchers, Loss & Signature, B2B caps +- **DIG-300** — Digital products (refund rules, faulty activation, transfers, regional) +- **LOY-400** — Loyalty tiers and extensions +- **TCK-500** — Ticketing & Escalations (when to open `logistics` / `returns`) +- **EXM-900** — Worked examples (quick references) + +--- + +## Agent Guardrails (CORE-010) +- [CORE-011] Cite policy IDs from our documents (e.g., **[RET-103]**, **[SLA-214]**) whenever a rule is applied. +- [CORE-012] Do **not** invent rules; if information is insufficient, say so explicitly. +- [CORE-013] Escalations (tickets) must follow **TCK-5xx**. Do **not** escalate only because a user asks. +- [CORE-014] When refund applies, compute **exact euros** and respect **caps**. +- [CORE-015] When voucher applies, compute **exact euros** and respect **caps**. +- [CORE-015] Refunds and vouchers amounts do not sum up. If both applies, calculate the amount for each of them. + +### Fixed Answer Format (CORE-015) +Keep English concise. + +### Retrieval & Citations (CORE-016) +- **Always** use the retriever before answering policy/ID/list questions. +- Prefer sources that match the user’s scope (e.g., `30_digital.md` for **DIG-***). +- Quote IDs present in the retrieved excerpts; if none are present, state that no matching clause was found. + +### Tool Use Order (CORE-017) +1. `search_policies` to gather the relevant clauses. +2. `calc` to compute **exact amounts** and apply caps/limits. +3. `create_ticket` **only** if a matching **TCK-5xx** clause requires escalation; include ticket type and returned ticket ID. + +### Escalation Discipline (CORE-018) +- Open **logistics** tickets per **[TCK-511..513]** conditions. +- Open **returns** tickets per **[TCK-521..523]** or **[TCK-531]** (digital faulty activation). +- **Do not** open tickets outside these clauses (**[TCK-593]**). + +### Caps & Amounts (CORE-019) +- Consumer voucher cap **[SLA-214]**; B2B cap **[SLA-242]**. +- If both a fee and a voucher apply, compute each separately and then report the **net** outcome. + +### Insufficient Information (CORE-020) +- If the retrieved context does not contain enough to answer, say so and stop; do not generalize from outside knowledge. + +--- + +## Changelog (CORE-099) +- **v1.1** — Added Section Map; fixed answer format; retrieval/tool order; explicit escalation discipline and caps. +- **v1.0** — Initial index and precedence. diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/10_returns.md b/Workshops/AI/04-Agents-and-RAG/data/policies/10_returns.md new file mode 100644 index 0000000..8f64b9f --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/10_returns.md @@ -0,0 +1,24 @@ +# Returns & Refunds (RET-100) — v1.0 +Contains rules for returns and refunds: eligibility windows, DOA rules, bundle handling, allowed refund methods, and calculation/cap rules. Query this file for any RET-* clause, exact refund logic, and examples of how to compute refund amounts and apply caps. + +## Windows (RET-1xx) +- [RET-101] Apparel/Footwear: 30 days from delivery; unused, with tags. +- [RET-102] Electronics (sealed): 14 days. +- [RET-103] Electronics (opened, fully working): 14 days; **10% restocking**. +- [RET-104] Accessories: 30 days if unopened; 14 days if opened. + +## DOA & Defects (RET-11x) +- [RET-111] DOA within 14 days: free return label + **refund or replacement** (customer choice), **no restocking**. +- [RET-112] After 14 days: follow Warranty (not covered here; assume repair/replacement path). + +## Bundles (RET-12x) +- [RET-121] Partial return only if items have standalone SKUs and promo terms allow. +- [RET-122] Refund for bundles is **pro-rated**; bundle savings may be lost. + +## Refund Method (RET-13x) +- [RET-131] Refund to original payment; processing within 7 business days after inspection. + +## Escalation (RET-14x) +- [RET-141] For **defects after 14 days**, open **returns** ticket per [TCK-521]. +- [RET-142] For **bundle partial-return disputes**, open **returns** ticket per [TCK-522]. +- [RET-143] For **refund processing delays > 10 business days**, open **returns** ticket per [TCK-523]. diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/20_shipping_sla.md b/Workshops/AI/04-Agents-and-RAG/data/policies/20_shipping_sla.md new file mode 100644 index 0000000..3cf39b5 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/20_shipping_sla.md @@ -0,0 +1,27 @@ +# Shipping & SLA (SLA-200) — v1.0 +Covers shipping, delivery SLAs, late-delivery vouchers, lost shipments, signature requirements, and B2B caps. Use for SLA-* references, voucher caps, and determining whether a shipping issue qualifies for compensation. + +## Delivery targets (SLA-2xx) +- [SLA-201] Standard: 3–5 business days (domestic). International: 5–8. +- [SLA-202] Express: 1–2 business days (domestic). + +## Late delivery vouchers (SLA-21x) +- [SLA-211] ≥ 2 business days late: 10% voucher. +- [SLA-212] ≥ 5 business days late: 20% voucher. +- [SLA-213] ≥ 10 business days late: free shipping credit + 25% voucher. +- [SLA-214] **Voucher cap €50 per order** (consumer). +- [SLA-215] Customer must contact us within 14 days of delivery for a voucher claim. + +## Exclusions (SLA-22x) +- [SLA-221] Force majeure, severe weather, customs holds, customer unavailable, wrong address by customer → **no voucher**. + +## Loss & Signature (SLA-23x) +- [SLA-231] Confirmed loss by carrier: full refund **or** free reshipment. +- [SLA-232] Orders > €500 require signature unless customer **opts out**; signature opt-out → **no loss voucher**, but [SLA-231] still applies. + +## B2B note (SLA-24x) +- [SLA-241] Palletized shipments tolerance: +2 business days before SLA vouchers apply. +- [SLA-242] For B2B, voucher **cap €250 per PO** (unless contract/SOW overrides). + +## Escalation (SLA-25x) +- [SLA-251] For late deliveries, **see Ticketing**: [TCK-511] (≥ 10 business days), [TCK-512] (repeated route issues), [TCK-513] (unresolved investigations). diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/30_digital.md b/Workshops/AI/04-Agents-and-RAG/data/policies/30_digital.md new file mode 100644 index 0000000..81e5032 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/30_digital.md @@ -0,0 +1,15 @@ +# Digital Products (DIG-300) — v1.0 +Specifies policies for digital products: refund eligibility, faulty activation procedures, license transfers, regional constraints, and exceptions. Use for DIG-* lookups when the user question involves digital goods or activation problems. + +## Refunds (DIG-31x) +- [DIG-311] No returns once a license key is **viewed** or download **started**, unless mandated by law. +- [DIG-312] Faulty activation (server-side) reported within 7 days → full refund. + +## Transfers (DIG-32x) +- [DIG-321] One-time license transfer allowed after 30 days; €10 admin fee. + +## Regional (DIG-33x) +- [DIG-331] Region-locked keys: cross-region failures are **not** refundable unless mandated by law. + +## Escalation (DIG-34x) +- [DIG-341] For **server-side activation errors**, open **returns** ticket per [TCK-531]. diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/40_loyalty.md b/Workshops/AI/04-Agents-and-RAG/data/policies/40_loyalty.md new file mode 100644 index 0000000..230c4b5 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/40_loyalty.md @@ -0,0 +1,17 @@ +# Loyalty (LOY-400) — v1.0 +Defines loyalty program rules: tiers, benefits, eligibility, voucher issuance and expiration, and extensions. Use for LOY-* queries and when calculating loyalty-related entitlements or voucher values. + +## Tiers (LOY-41x) +- [LOY-411] Silver < 1000 pts; Gold 1000–4999; Platinum ≥ 5000. + +## Benefits (LOY-42x) +- [LOY-421] Gold: free Standard shipping; returns **+7 days** on apparel/footwear. +- [LOY-422] Platinum: free Express; returns **+14 days** on apparel/footwear. + +## Points (LOY-43x) +- [LOY-431] Earn 1 pt/€ (net of tax). No points on shipping or vouchers. +- [LOY-432] Redeem 100 pts = €1; cannot pay taxes/fees with points. +- [LOY-433] Points expire after 18 months of inactivity. + +**Note** +[LOY-421]/[LOY-422] **extend** but do not remove other rules (e.g., restocking). diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/50_ticketing.md b/Workshops/AI/04-Agents-and-RAG/data/policies/50_ticketing.md new file mode 100644 index 0000000..aaf60f6 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/50_ticketing.md @@ -0,0 +1,21 @@ +# Ticketing & Escalations (TCK-500) — v1.0 +Describes ticketing and escalation procedures (TCK-5xx): when to open logistics vs returns tickets, required ticket metadata, and escalation discipline. Consult this file before creating tickets; follow TCK clauses exactly when a ticket is mandated. + +## Logistics (TCK-51x) +- [TCK-511] Open a **logistics** ticket if **late delivery ≥ 10 business days**. +- [TCK-512] Open a **logistics** ticket if the **same route is reported late/lost ≥ 2 times within 60 days** (repeated route issues). +- [TCK-513] Open a **logistics** ticket if **carrier investigation is unresolved after 3 business days**. +- [TCK-514] For **confirmed loss**, a ticket is optional; open **logistics** only if additional follow-up with the carrier is required. + +## Returns (TCK-52x) +- [TCK-521] Open a **returns** ticket for **defects reported after 14 days** (warranty/repair path needed). +- [TCK-522] Open a **returns** ticket for **bundle partial-return disputes** (pro-rata calculation or promo terms unclear). +- [TCK-523] Open a **returns** ticket for **refund processing delays > 10 business days** after inspection. + +## Digital (TCK-53x) +- [TCK-531] Open a **returns** ticket for **server-side activation errors** on digital licenses (faulty activation). + +## Notes (TCK-59x) +- [TCK-591] Do **not** open a ticket if the case is fully resolved by a straightforward policy answer and no follow-up is required. +- [TCK-592] Minimum ticket payload: `category` (`logistics` or `returns`) and a short `reason`. +- [TCK-593] Do NOT open a ticket solely based on user request; a matching TCK-5xx condition is required. diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/90_examples.md b/Workshops/AI/04-Agents-and-RAG/data/policies/90_examples.md new file mode 100644 index 0000000..e04bd7f --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/90_examples.md @@ -0,0 +1,12 @@ +# Worked Examples (EXM-900) — v1.0 +Provides worked examples and sample dialogues showing policy application, calculations, and ticket outputs. Use these as templates for phrasing, calculation steps, and to validate that retrieved policy clauses are applied correctly. + +- [EXM-901] Opened laptop (working) returned day 10 → 10% restocking ([RET-103]). +- [EXM-902] Headphones defective day 7 → DOA path, no restocking ([RET-111]). +- [EXM-903] Express order lost, signature opt-out → refund or reship, no voucher ([SLA-231],[SLA-232]). +- [EXM-904] Gold returns shoes day 34 → OK (30 + 7) ([RET-101],[LOY-421]). +- [EXM-905] Digital key viewed → no refund except law; faulty activation within 7 days → refund ([DIG-311],[DIG-312]). +- [EXM-906] Delivery is 11 business days late → open **logistics** ticket ([TCK-511]); voucher per SLA if applicable. +- [EXM-907] Route reported late twice within 60 days → open **logistics** ticket ([TCK-512]). +- [EXM-908] Bundle partial-return dispute (pro-rata unclear) → open **returns** ticket ([TCK-522]). +- [EXM-909] Digital license server activation error at day 5 → open **returns** ticket ([TCK-531]). diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/return_refunds.md b/Workshops/AI/04-Agents-and-RAG/data/policies/return_refunds.md deleted file mode 100644 index 6459714..0000000 --- a/Workshops/AI/04-Agents-and-RAG/data/policies/return_refunds.md +++ /dev/null @@ -1,9 +0,0 @@ -# Returns & Refunds Policy - -- Returns are accepted within 30 days from purchase with a valid receipt. -- Refunds are processed within 7 business days after approval. -- If delivery is delayed by more than 3 days beyond the estimated date, the customer receives a **10% discount voucher** for the next order. -- Items damaged on arrival: free replacement or full refund. - -## Exceptions -- Customized or personalized items are non-refundable unless a manufacturing defect is proven. diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/shipping.md b/Workshops/AI/04-Agents-and-RAG/data/policies/shipping.md deleted file mode 100644 index 6e16b97..0000000 --- a/Workshops/AI/04-Agents-and-RAG/data/policies/shipping.md +++ /dev/null @@ -1,6 +0,0 @@ -# Shipping Policy - -- Standard shipping: 3-5 business days. -- Express shipping: 1-2 business days. -- Tracking is included for all orders. -- In case of **confirmed loss** by the carrier: full refund or free reshipment. diff --git a/Workshops/AI/04-Agents-and-RAG/data/tickets/TKT-7180.json b/Workshops/AI/04-Agents-and-RAG/data/tickets/TKT-7180.json new file mode 100644 index 0000000..18f970b --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/tickets/TKT-7180.json @@ -0,0 +1,7 @@ +{ + "ticket_id": "TKT-7180", + "status": "created", + "category": "logistics", + "description": "Repeated late delivery on the same route within 60 days.", + "created_at": "2025-11-01T16:14:33.702918Z" +} \ No newline at end of file diff --git a/Workshops/AI/04-Agents-and-RAG/requirements-freeze.txt b/Workshops/AI/04-Agents-and-RAG/requirements-lab.txt similarity index 100% rename from Workshops/AI/04-Agents-and-RAG/requirements-freeze.txt rename to Workshops/AI/04-Agents-and-RAG/requirements-lab.txt diff --git a/Workshops/AI/04-Agents-and-RAG/requirements.txt b/Workshops/AI/04-Agents-and-RAG/requirements.txt index 7c1ab30..9da737f 100644 --- a/Workshops/AI/04-Agents-and-RAG/requirements.txt +++ b/Workshops/AI/04-Agents-and-RAG/requirements.txt @@ -14,3 +14,4 @@ langgraph sentencepiece transformers tokenizers +langsmith diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_api.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_api.py index 047117a..bfe3d3b 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_api.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_api.py @@ -1,13 +1,40 @@ +"""API endpoints for agent interactions. + +This module exposes a single FastAPI router for agent-related operations. +Endpoints call into the language-graph agent implementation and return +the agent response. Designed to be lightweight: validation and business +logic live in the agent implementation. + +Endpoints +- POST /agent/ask -> Handle a user question via the language-graph agent. +""" from fastapi import APIRouter -from pydantic import BaseModel from .agent_graph import run_agent_with_langgraph +from .models import AskReq router = APIRouter(prefix="/agent", tags=["Agent"]) -class AskReq(BaseModel): - question: str - thread_id: str | None = None - @router.post("/ask") def ask(req: AskReq): + """Handle a user question and run the language-graph agent. + + Parameters + ---------- + req : AskReq + Request body containing: + - question: the user's question string + - thread_id: optional identifier to continue a conversation thread + + Returns + ------- + dict | Any + The agent response produced by run_agent_with_langgraph. The exact + shape depends on the agent implementation (typically a dict with + answer, sources, and metadata). + + Notes + ----- + Exceptions raised by the agent implementation are propagated to the + FastAPI framework and will be converted to HTTP error responses. + """ return run_agent_with_langgraph(req.question, thread_id=req.thread_id) diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_graph.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_graph.py index 5e5f280..8fb797c 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_graph.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/agent_graph.py @@ -1,6 +1,25 @@ +"""Graph-based agent wiring for the workshop demo. + +This module composes a small language-graph agent that: +- accepts a user question, +- consults the policy knowledge base via a search tool, +- performs simple calculations or ticket creation when required, +- and calls a Hugging Face chat model to produce the final answer. + +Design notes +- The graph is intentionally simple: an "agent" node that calls the LLM + and a "tools" node that runs tools when the LLM requests them. +- Tools return JSON-serialisable strings; the LLM is expected to call tools + and consume their outputs. Tool outputs are included in the conversational + state so the model can reason over them in subsequent turns. +- The SYSTEM_PROMPT encodes strict policy guardrails. Keep it up-to-date + with policy documents and retrieval/tooling guidelines. +""" from typing import TypedDict, Annotated import operator import json +from pathlib import Path +from datetime import datetime from langgraph.graph import StateGraph, END from langgraph.checkpoint.memory import MemorySaver from langgraph.prebuilt import ToolNode @@ -9,20 +28,52 @@ from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint from .config import HF_MODEL_ID from .rag_core import retrieve +import re + + +# Regex to detect policy IDs in retrieved text (e.g., 'RET-103', 'SLA-214', 'EXM-900') +ID_RE = re.compile(r"\b([A-Z]{3}-\d{3}(?:-[A-Z]+)?)\b") @tool def search_policies(query: str) -> str: - """Search the internal knowledge base (RAG) and return a JSON list of - matches with fields: source, chunk, text.""" - hits = retrieve(query, k=4) - return json.dumps( - [{"source": h["meta"]["source"], "chunk": h["meta"]["chunk"], "text": h["text"]} for h in hits] - ) + """Tool: run semantic search against the policies KB. + + Return JSON with hits: [{'source': str, 'chunk': int, 'ids': [str], 'excerpt': str}]. + Each hit contains: + - source: file path of the document + - chunk: integer chunk index + - ids: list of policy IDs like RET-103, SLA-214 found in the excerpt + - excerpt: short text excerpt for context + + The LLM should parse the returned JSON to extract cited policy IDs + and exact wording for answers and citations. + """ + hits = retrieve(query, k=6) + out = [] + for h in hits: + text = h["text"] + ids = sorted(set(ID_RE.findall(text))) + out.append({ + "source": h["meta"]["source"], + "chunk": h["meta"]["chunk"], + "ids": ids, + "excerpt": text # excerpt used by the model for context + }) + return json.dumps(out) @tool def calc(expression: str) -> str: - """Evaluate a simple arithmetic expression (e.g., '12*(3+4)'). - Returns the stringified result or an error message.""" + """Tool: safely evaluate a simple arithmetic expression (e.g., '12*(3+4)'). + + Returns the stringified result or an error message. + + Purpose + - Use when the agent needs to compute exact euro amounts, apply caps, etc. + + Security note + - eval is run with an empty __builtins__ to reduce risk. This is a demo + helper; consider a proper expression evaluator for production. + """ try: out = str(eval(expression, {"__builtins__": {}})) except Exception as e: @@ -31,55 +82,156 @@ def calc(expression: str) -> str: @tool def create_ticket(category: str, description: str) -> str: - """Simulate creating a support ticket and return a JSON payload with - ticket_id and status.""" + """Tool: simulate ticket creation, persist it to disk, and return a JSON payload. + + Behaviour: + - Generates a deterministic ticket id for demo purposes. + - Persists the ticket JSON into the project's data/tickets directory as .json. + - Returns a JSON string with ticket_id and status. + + Notes: + - This is a demo persistence mechanism. In production use a proper ticketing DB/service. + """ ticket_id = "TKT-" + str(abs(hash(category + description)) % 10_000) + ticket = { + "ticket_id": ticket_id, + "status": "created", + "category": category, + "description": description, + "created_at": datetime.utcnow().isoformat() + "Z", + } + + # Persist ticket to data/tickets/.json (create dir if needed) + tickets_dir = Path(__file__).resolve().parents[2] / "data" / "tickets" + try: + tickets_dir.mkdir(parents=True, exist_ok=True) + ticket_file = tickets_dir / f"{ticket_id}.json" + # Write atomically: write to .tmp then rename + tmp_file = ticket_file.with_suffix(".json.tmp") + tmp_file.write_text(json.dumps(ticket, ensure_ascii=False, indent=2), encoding="utf-8") + tmp_file.replace(ticket_file) + except Exception as e: + # If persistence fails, still return created ticket info but include an error field. + ticket["status"] = "created_with_persist_error" + ticket["persist_error"] = str(e) + return json.dumps({"ticket_id": ticket_id, "status": "created"}) +# Public tools list bound to the chat model and used by the ToolNode. TOOLS = [search_policies, calc, create_ticket] class AgentState(TypedDict): + """TypedDict describing the graph state for the agent nodes. + + messages: conversational messages list that the chat model consumes/produces. + The graph nodes expect 'messages' to be a list of AnyMessage-like objects. + """ messages: Annotated[list[AnyMessage], operator.add] -# Hugging Face chat model +# Configure the Hugging Face endpoint wrapper used as the LLM. +# Note: repo_id must be compatible with the invocation style used by ChatHuggingFace. llm = HuggingFaceEndpoint( repo_id=HF_MODEL_ID, task="text-generation", max_new_tokens=512, do_sample=False, + temperature=0, ) -chat_model = ChatHuggingFace(llm=llm) # LangChain wrapper for HF chat models + +# Wrap the HF endpoint into a ChatHuggingFace model and bind the tools so the +# model can produce tool calls in its output. +chat_model = ChatHuggingFace(llm=llm).bind_tools(TOOLS) + +# ToolNode integrates the callable tools into the graph so they can be invoked +# when the LLM requests them. tool_node = ToolNode(TOOLS) + def call_model(state: AgentState) -> AgentState: + """Graph node: call the chat model with the current messages. + + The model returns a message-like object. We wrap it into the AgentState + shape expected by the graph (a dict with 'messages'). + """ response = chat_model.invoke(state["messages"]) return {"messages": [response]} + def route_after_model(state: AgentState) -> str: + """Decide next node after the model runs. + + If the last message contains tool_calls, route to the 'tools' node, + otherwise end the graph run (END). + """ last = state["messages"][-1] if getattr(last, "tool_calls", None): return "tools" return END + +# Construct a small state graph and compile it with an in-memory checkpointer. _graph = StateGraph(AgentState) _graph.add_node("agent", call_model) _graph.add_node("tools", tool_node) +# Loop between tools and agent: tools -> agent (model consumes tool outputs) _graph.add_edge("tools", "agent") +# agent node can conditionally route to tools or END based on the model output _graph.add_conditional_edges("agent", route_after_model) _graph.set_entry_point("agent") +# Use an in-memory saver for checkpointing conversational state during the demo. checkpointer = MemorySaver() agent_graph = _graph.compile(checkpointer=checkpointer) +# System prompt: strict guardrails for policy-bound behaviour. +# Keep this synchronized with the policy documents and RAG retrieval expectations. SYSTEM_PROMPT = ( - "You are a support agent. First, outline a brief plan, then use tools when needed:\n" - "- search_policies for KB-based answers (cite sources)\n" - "- calc for small computations\n" - "- create_ticket when escalation is required by policy\n" - "Respond in concise English. Do not fabricate facts not present in sources." + "You are a policy-bound support agent. Follow these RULES strictly:\n" + "1) Always start with a short PLAN before answering.\n" + "2) Use tools only when needed:\n" + " - Use `search_policies` BEFORE answering any policy question, lists, IDs, thresholds, examples.\n" + " - Use `calc` to compute exact euro amounts and to apply any caps/limits.\n" + " - Use `create_ticket` if the retrieved sources explicitly require it.\n" + " When a tool is needed, emit a tool call. The final answer will be formatted by the system." + " Multiple tool calls are allowed. For instance: \n" + " - First call `search_policies` to find relevant policies.\n" + " - Then call `calc` to compute refund amounts based on retrieved policy data.\n" + " - Finally, if a ticket is mandated by a TCK-5xx clause, call `create_ticket` with the correct category and reason.\n" + "3) When using `search_policies`, cite policy IDs (e.g., [RET-103], [SLA-214]) included in the tool output. " + " Use ONLY facts present in the retrieved sources; do NOT invent.\n" + "4) If information is insufficient, say so explicitly and stop.\n" + "5) Responses must be concise, in English. " + "If refunds/vouchers apply, include the exact euro amount; if a ticket is opened, include type and ticket ID>\n" + "6) If refunds or vouchers apply, compute exact values and check caps/limits before answering.\n" + "7) Do not open tickets unless a retrieved clause (e.g., TCK-5xx) requires it; cite that clause.\n" + "8) When any TCK-5xx clause mandates escalation, call `create_ticket` immediately with the correct `category` and a short `reason`. Do not ask the user for permission, do not invent ticket IDs, and do not claim success until the tool returns.\n" + "9) If you mention a ticket in the answer, ensure it was created via the tool; otherwise, call the tool before replying.\n" ) def run_agent_with_langgraph(question: str, thread_id: str | None = None) -> dict: + """Run the compiled language-graph agent for a single user question. + + Parameters + ---------- + question : str + The user's natural language question. + thread_id : str | None + Optional thread identifier to correlate conversation state across calls. + + Returns + ------- + dict + A dictionary with: + - final_answer: the assistant's final rendered text (string) + - thread_id: the thread identifier used (string) + + Behaviour + - Initializes the conversation with SYSTEM_PROMPT and the user's question. + - Invokes the compiled agent_graph which will alternate between model and tools + as needed until completion. + - The final message is returned as 'final_answer'. Tool outputs (JSON strings) + are expected to be consumed by the model in intermediate steps. + """ config = {"configurable": {"thread_id": thread_id or "default"}} initial: AgentState = { "messages": [ diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py index dd6e1a7..69a23d7 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/app.py @@ -1,3 +1,15 @@ +"""FastAPI application entrypoint for the RAG & Agents workshop services. + +This module creates the FastAPI app, registers routers for the +RAG (retrieval-augmented generation) and language-graph agent APIs, +and exposes a lightweight healthcheck endpoint. + +Keep this file minimal: application configuration, middleware and +business logic should live in their respective modules (rag_api, +agent_api, etc.). Routers are included at import-time so their +prefixes and tags control the OpenAPI schema. +""" + from fastapi import FastAPI from .rag_api import router as rag_router from .agent_api import router as agent_router @@ -6,7 +18,14 @@ @app.get("/healthz") def healthz(): + """Liveness/health endpoint. + + Returns a simple status dict used by orchestrators or uptime checks. + Keep this endpoint deterministic and fast — do not perform heavy IO here. + """ return {"status": "ok"} +# Include routers after app creation. Order does not affect routing but +# influence the generated OpenAPI ordering. Each router defines its own prefix. app.include_router(rag_router) app.include_router(agent_router) \ No newline at end of file diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py index 563c6c9..0e7ad68 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/config.py @@ -1,12 +1,39 @@ +"""Configuration module for the RAG & Agents service. + +This module reads runtime configuration from environment variables (optionally +loaded from a .env file) and exposes simple constants used across the codebase. + +Documented variables: +- PERSIST_DIR: path where embedding/vector DB artifacts are stored. +- KB_DIR: directory containing the policy knowledge base used by the retriever. +- EMB_MODEL: embedding model id used to encode documents (default: sentence-transformers/all-MiniLM-L6-v2). +- HUGGINGFACEHUB_API_TOKEN: API token for Hugging Face; required for HF model inference / router calls. +- HF_MODEL_ID: Hugging Face model id used for chat/completions calls. + +Notes +----- +- Keep sensitive values (HUGGINGFACEHUB_API_TOKEN) out of source control; prefer environment variables + or a secrets manager. The .env loader is included for local development convenience. +- If you change defaults here, ensure tests and deployment configs are updated accordingly. +""" import os from dotenv import load_dotenv + +# Load .env in development; load_dotenv is a no-op if no .env file is present. load_dotenv() # Paths / embeddings +# Directory where persistent vector DB / chroma artifacts are stored. PERSIST_DIR = os.getenv("PERSIST_DIR", "artifacts/chroma") +# Directory containing the knowledge base used by the retriever (policy markdown files). KB_DIR = os.getenv("KB_DIR", "data/policies") +# Embedding model identifier (used by the embedding client). Default is a lightweight sentence-transformer. EMB_MODEL = os.getenv("EMB_MODEL", "sentence-transformers/all-MiniLM-L6-v2") # Hugging Face +# Token used to authenticate with the Hugging Face Hub / Inference API. +# Required for model inference that uses HF router or private models. HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") -HF_MODEL_ID = os.getenv("HF_MODEL_ID", "mistralai/Mistral-7B-Instruct-v0.3") +# HF model id for chat/completions calls. Make sure this model supports the endpoint your client uses. +# Example: "mistralai/Mistral-7B-Instruct-v0.3" or "Qwen/Qwen2.5-7B-Instruct" or any OpenAI-compatible HF router model. +HF_MODEL_ID = os.getenv("HF_MODEL_ID", "Qwen/Qwen2.5-7B-Instruct") diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/models.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/models.py new file mode 100644 index 0000000..48a487e --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/models.py @@ -0,0 +1,82 @@ +"""Pydantic request/response models used by the RAG & Agents FastAPI services. + +This module defines lightweight DTOs for incoming API requests and for the +RAG response payload. Keep these models minimal and stable since they form +the public contract of the HTTP endpoints. +""" +from pydantic import BaseModel +from typing import List, Dict, Any + + +class AskReq(BaseModel): + """Request body for the /agent/ask endpoint. + + Attributes + ---------- + question : str + User's free-text question to the language agent. + thread_id : str | None + Optional identifier to continue a conversation thread (preserved by the caller). + """ + question: str + thread_id: str | None = None + + +class IngestReq(BaseModel): + """Request for (re)ingesting documents into the retriever/embedding store. + + Attributes + ---------- + source_dir : str | None + Optional path containing documents to ingest. If omitted, use configured KB_DIR. + reset : bool + If True, clear existing persisted embeddings/vector DB before ingesting. + """ + source_dir: str | None = None + reset: bool = False + + +class SearchReq(BaseModel): + """Simple search/retrieval request. + + Used when invoking the retriever directly. + + Attributes + ---------- + query : str + Query text to search the KB. + k : int + Number of top documents to return (default: 4). + """ + query: str + k: int = 4 + + +class QueryReq(BaseModel): + """RAG-style query payload for the /rag/generate endpoint. + + Attributes + ---------- + question : str + The user's natural language question to answer using RAG. + k : int + Number of retrieved documents to use as context (default: 4). + """ + question: str + k: int = 4 + + +class RAGResponse(BaseModel): + """Canonical RAG response returned by /rag/generate. + + Attributes + ---------- + answer : str + Generated answer text produced by the model using retrieved context. + citations : list[dict] + List of citation metadata for the retrieved documents used to form the answer. + Each dict should minimally include source identifiers and optional excerpt/score. + Example item: {"id": "20_shipping_sla.md#SLA-214", "text": "...", "score": 0.92} + """ + answer: str + citations: List[Dict[str, Any]] diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py index 4eef994..fa7787a 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_api.py @@ -1,29 +1,71 @@ +"""FastAPI router exposing simple RAG endpoints: ingest, retrieve and generate. + +This module provides thin HTTP adapters over the rag_core utilities. Keep +business logic in rag_core; these endpoints are responsible for request +validation (Pydantic models) and for returning well-typed responses. + +Endpoints +- POST /rag/ingest -> trigger (re)ingestion of KB files +- POST /rag/retrieve -> run semantic search against the KB +- POST /rag/generate -> run RAG generation using top-k retrieved chunks +""" from fastapi import APIRouter -from pydantic import BaseModel -from .rag_core import build_index, retrieve, answer_with_citations, RAGResponse +from .models import IngestReq, SearchReq, QueryReq +from .rag_core import ingest, retrieve, generate, RAGResponse router = APIRouter(prefix="/rag", tags=["RAG"]) -class IngestReq(BaseModel): - source_dir: str | None = None - reset: bool = False - @router.post("/ingest") -def ingest(req: IngestReq): - return build_index(req.source_dir, req.reset) +def ingest_api(req: IngestReq): + """Trigger ingestion of markdown/plain-text files into the vector store. + + Parameters + ---------- + req : IngestReq + - source_dir: optional path to scan (defaults to configured KB_DIR) + - reset: if True, clear existing collection before ingesting -class SearchReq(BaseModel): - query: str - k: int = 4 + Returns + ------- + dict + Summary containing indexed_files and chunks added. + """ + # Thin adapter: delegate to rag_core.ingest which handles file IO and persistence. + return ingest(req.source_dir, req.reset) -@router.post("/search") -def search(req: SearchReq): +@router.post("/retrieve") +def retrieve_api(req: SearchReq): + """Run a semantic retrieval against the policies collection. + + Parameters + ---------- + req : SearchReq + - query: query text + - k: number of top results to return + + Returns + ------- + dict + Echoes the query and returns the list of retrieval hits under "results". + Each result includes text, snippet, meta and score. + """ return {"query": req.query, "results": retrieve(req.query, k=req.k)} -class QueryReq(BaseModel): - question: str - k: int = 4 +@router.post("/generate", response_model=RAGResponse) +def generate_api(req: QueryReq): + """Produce a RAG answer using top-k retrieved context. + + Parameters + ---------- + req : QueryReq + - question: user question + - k: number of retrieved chunks to pass to the model -@router.post("/query", response_model=RAGResponse) -def query(req: QueryReq): - return answer_with_citations(req.question, k=req.k) + Returns + ------- + RAGResponse + Structured response with 'answer' and 'citations'. + """ + # Delegate to rag_core.generate which performs retrieval, calls the model, + # and formats the RAGResponse. Any HF/IO errors propagate as HTTP 500. + return generate(req.question, k=req.k) diff --git a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py index 7c01a8d..b06efea 100644 --- a/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py +++ b/Workshops/AI/04-Agents-and-RAG/src/lab_service/rag_core.py @@ -1,8 +1,26 @@ +"""Core RAG utilities: ingestion, retrieval and generation. + +This module provides a tiny pipeline for: +- indexing markdown/text policy files into a persistent Chroma collection + (ingest). +- performing semantic retrieval against the collection (retrieve). +- formatting retrieved context and calling a Hugging Face InferenceClient + (OpenAI-compatible router/chat completions) to produce a final answer + with citations (generate). + +Notes / operational guidance +- PERSIST_DIR, EMB_MODEL, HUGGINGFACEHUB_API_TOKEN and HF_MODEL_ID are + configured in lab_service.config. Ensure HF_MODEL_ID points to a + model/router that supports the OpenAI-compatible chat/completions + endpoint if you call client.chat.completions.create(...) (otherwise + the hub may return 404). +- The ingestion logic uses simple fixed-size chunking for demo purposes; + production systems should use a semantic/overlap-aware chunker. +""" from pathlib import Path from typing import List, Dict, Any from chromadb import PersistentClient from chromadb.utils import embedding_functions -from pydantic import BaseModel from huggingface_hub import InferenceClient from .config import ( @@ -12,33 +30,66 @@ HUGGINGFACEHUB_API_TOKEN, HF_MODEL_ID, ) +from .models import RAGResponse - -# --------------------------- -# Vector store (Chroma) -# --------------------------- def _chroma(): + """Create a PersistentClient pointing at the configured PERSIST_DIR. + + Returns + ------- + chromadb.PersistentClient + Client instance to interact with the persistent Chroma DB. + """ + # Chroma stores collections on-disk at PERSIST_DIR when using PersistentClient return PersistentClient(path=PERSIST_DIR) def _collection(): + """Get or create the 'policies' collection with the configured embedding fn. + + The collection is created with a SentenceTransformer embedding function + backed by EMB_MODEL (a sentence-transformers model id). This ensures + documents and queries are encoded consistently. + """ ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name=EMB_MODEL) return _chroma().get_or_create_collection("policies", embedding_function=ef) -def build_index(source_dir: str | None = None, reset: bool = False) -> Dict[str, Any]: +def ingest(source_dir: str | None = None, reset: bool = False) -> Dict[str, Any]: + """Index markdown/text files from source_dir (or KB_DIR) into Chroma. + + The function: + - walks the source directory recursively, + - reads .md and .txt files, + - splits each file into fixed-size chunks (simple demo strategy), + - stores documents, ids and metadata into the 'policies' collection. + + Parameters + ---------- + source_dir : str | None + Path to scan for documents. If None, uses configured KB_DIR. + reset : bool + If True, deletes the existing 'policies' collection before ingesting. + + Returns + ------- + dict + Summary with number of indexed files and total chunks added. + """ source = Path(source_dir or KB_DIR) col = _collection() if reset: + # Delete and recreate to ensure a clean state when requested _chroma().delete_collection("policies") col = _collection() files_indexed = 0 docs, ids, metas = [], [], [] for p in source.rglob("*"): + # Only ingest markdown and plain text files if p.suffix.lower() in {".md", ".txt"}: text = p.read_text(encoding="utf-8", errors="ignore") - # simple chunking for demo + # Simple chunking for demo: chunk length 2000, overlap ~400 chunks = [text[i:i + 2000] for i in range(0, len(text), 1600)] for k, ch in enumerate(chunks): ids.append(f"{p.stem}-{files_indexed}-{k}") @@ -47,12 +98,31 @@ def build_index(source_dir: str | None = None, reset: bool = False) -> Dict[str, files_indexed += 1 if docs: + # Bulk-add to the collection col.add(documents=docs, ids=ids, metadatas=metas) return {"indexed_files": files_indexed, "chunks": len(docs)} def retrieve(query: str, k: int = 4): + """Run a semantic retrieval query against the 'policies' collection. + + Parameters + ---------- + query : str + Natural language query text. + k : int + Number of top results to return. + + Returns + ------- + list[dict] + List of hits with keys: text, snippet, meta, score. + - text: full chunk text + - snippet: short excerpt for quick display + - meta: metadata dict (includes 'source' and 'chunk') + - score: similarity/distance score as float + """ res = _collection().query(query_texts=[query], n_results=k) docs = res.get("documents", [[]])[0] metas = res.get("metadatas", [[]])[0] @@ -64,33 +134,67 @@ def retrieve(query: str, k: int = 4): return out -# --------------------------- -# RAG answer with citations -# --------------------------- -class RAGResponse(BaseModel): - answer: str - citations: List[Dict[str, Any]] +def _format_sources(ctx: List[Dict[str, Any]]) -> str: + """Serialize retrieved context into a plain-text block for the model prompt. + The format enumerates sources ([1], [2], ...) and includes each chunk's + text. This string is intended to be attached to the user prompt so the + model can refer to explicit passages. -def _format_sources(ctx: List[Dict[str, Any]]) -> str: + Parameters + ---------- + ctx : list[dict] + Retrieval results as returned by retrieve(). + + Returns + ------- + str + Multi-line string containing numbered sources and their corresponding text. + """ lines = [] for i, c in enumerate(ctx, 1): src = c["meta"]["source"] ch = c["meta"]["chunk"] + # Keep the full chunk in the prompt; the model can use snippet if needed. lines.append(f"[{i}] {src} (chunk {ch})\n{c['text']}\n") return "\n".join(lines) -def answer_with_citations(question: str, k: int = 4) -> RAGResponse: - """ - Use Hugging Face's OpenAI-compatible Router for chat completions. - The model stays a Hub repo id. We ask the model to return strict JSON; - if it doesn't, we fall back to attaching top-k sources. +def generate(question: str, k: int = 4) -> RAGResponse: + """Produce a RAG-formatted answer calling a Hugging Face InferenceClient. + + Steps: + 1. Retrieve top-k relevant chunks from the KB. + 2. Build a system + user prompt that instructs the model to answer ONLY + using the provided sources and to return strict JSON. + 3. Call the InferenceClient chat/completions endpoint. + 4. Parse the model output as JSON; if parsing fails, fall back to returning + the raw content and attaching the top-k sources as citations. + + Important: + - HF_MODEL_ID must refer to a model/router that exposes an OpenAI-compatible + chat/completions endpoint when using client.chat.completions.create(...). + Otherwise the Hugging Face Hub may return 404/Not Found. + - HUGGINGFACEHUB_API_TOKEN should be set for private models or router usage. + + Parameters + ---------- + question : str + User question to answer. + k : int + Number of retrieved context chunks to pass to the model. + + Returns + ------- + RAGResponse + answer: final text (or raw model content on parse failure) + citations: list of dicts with keys 'source' and 'chunk' """ ctx = retrieve(question, k=k) if not ctx: return RAGResponse(answer="No documents found in the knowledge base.", citations=[]) + # Guidance to the model: be strict and only use the provided sources. system = ( "You are a helpful assistant that must answer ONLY using the provided sources. " "If the information is not present in the sources, explicitly say so. " @@ -103,10 +207,15 @@ def answer_with_citations(question: str, k: int = 4) -> RAGResponse: "{'answer': str, 'citations': [{'source': str, 'chunk': int}]}" ) - client = InferenceClient(provider="novita", api_key=HUGGINGFACEHUB_API_TOKEN) + # Create the InferenceClient. The `provider` and invocation style can vary + # depending on how HF_MODEL_ID is exposed (router, custom provider, etc.). + client = InferenceClient(provider="together", api_key=HUGGINGFACEHUB_API_TOKEN) + # Call the chat completions API. This may raise HTTP errors from the hub + # (e.g., 404 if the route/model is not available) — let exceptions bubble + # up to the caller or handle them where the generate() function is used. chat = client.chat.completions.create( - model=HF_MODEL_ID, # e.g., "meta-llama/Meta-Llama-3-8B-Instruct" + model=HF_MODEL_ID, messages=[{"role":"system","content":system}, {"role":"user","content":user}], temperature=0.2, @@ -117,10 +226,13 @@ def answer_with_citations(question: str, k: int = 4) -> RAGResponse: import json try: + # Expect the model to return a strict JSON object per the prompt. data = json.loads(content) ans = data.get("answer", "") cits = data.get("citations", []) except Exception: + # If parsing fails, return the raw content and attach the retrieved sources + # as fallback citations so the caller can still show provenance. ans = content cits = [{"source": c["meta"]["source"], "chunk": c["meta"]["chunk"]} for c in ctx] From 062fd72ea1a31701c79be89c1e46a4be1191159a Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Nov 2025 21:46:28 +0100 Subject: [PATCH 64/70] add bad policies --- .../data/bad_policies/00_index.md | 68 +++++++++++++++++ .../data/bad_policies/10_returns.md | 18 +++++ .../data/bad_policies/20_shipping_sla.md | 22 ++++++ .../data/bad_policies/30_digital.md | 11 +++ .../data/bad_policies/40_loyalty.md | 13 ++++ .../data/bad_policies/50_ticketing.md | 18 +++++ .../data/bad_policies/90_examples.md | 10 +++ .../data/policies/00_index.md | 73 ++++--------------- .../data/tickets/TKT-6254.json | 7 ++ 9 files changed, 180 insertions(+), 60 deletions(-) create mode 100644 Workshops/AI/04-Agents-and-RAG/data/bad_policies/00_index.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/bad_policies/10_returns.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/bad_policies/20_shipping_sla.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/bad_policies/30_digital.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/bad_policies/40_loyalty.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/bad_policies/50_ticketing.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/bad_policies/90_examples.md create mode 100644 Workshops/AI/04-Agents-and-RAG/data/tickets/TKT-6254.json diff --git a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/00_index.md b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/00_index.md new file mode 100644 index 0000000..67e1171 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/00_index.md @@ -0,0 +1,68 @@ +# Policy Index & Precedence (CORE-000) — v1.1 +This file is the policy index and precedence guide. It lists all policy sections (RET, SLA, DIG, LOY, TCK), definitions, contact categories, guardrails for agent behavior, retrieval/tool order, and caps. Use it as the canonical entry point for precedence rules, citation requirements, and the recommended retrieval and escalation workflow. + +**Precedence (higher wins if there is a conflict):** +1. Legal/Regulatory Notices (if any) +2. Shipping & SLA (SLA-2xx) +3. Returns (RET-1xx) +4. Digital Products (DIG-3xx) +5. Loyalty (LOY-4xx) +6. Ticketing & Escalations (TCK-5xx) apply only as referenced by other sections + +**Definitions** +- Business day: Monday–Friday, local public holidays excluded. +- Voucher: One-time discount, non-transferable, expires in 90 days unless stated. + +**Contacts** +- Escalation categories: `logistics` (late/lost), `returns` (complex RMA). + +--- + +## Section Map (CORE-001) +- **RET-100** — Returns & Refunds (windows, DOA, bundles, refund method) +- **SLA-200** — Shipping, Late delivery vouchers, Loss & Signature, B2B caps +- **DIG-300** — Digital products (refund rules, faulty activation, transfers, regional) +- **LOY-400** — Loyalty tiers and extensions +- **TCK-500** — Ticketing & Escalations (when to open `logistics` / `returns`) +- **EXM-900** — Worked examples (quick references) + +--- + +## Agent Guardrails (CORE-010) +- [CORE-011] Cite policy IDs from our documents (e.g., **[RET-103]**, **[SLA-214]**) whenever a rule is applied. +- [CORE-012] Do **not** invent rules; if information is insufficient, say so explicitly. +- [CORE-013] Escalations (tickets) must follow **TCK-5xx**. Do **not** escalate only because a user asks. +- [CORE-014] When refund applies, compute **exact euros** and respect **caps**. +- [CORE-015] When voucher applies, compute **exact euros** and respect **caps**. +- [CORE-015] Refunds and vouchers amounts do not sum up. If both applies, calculate the amount for each of them. + +### Fixed Answer Format (CORE-015) +Keep English concise. + +### Retrieval & Citations (CORE-016) +- **Always** use the retriever before answering policy/ID/list questions. +- Prefer sources that match the user’s scope (e.g., `30_digital.md` for **DIG-***). +- Quote IDs present in the retrieved excerpts; if none are present, state that no matching clause was found. + +### Tool Use Order (CORE-017) +1. `search_policies` to gather the relevant clauses. +2. `calc` to compute **exact amounts** and apply caps/limits. +3. `create_ticket` **only** if a matching **TCK-5xx** clause requires escalation; include ticket type and returned ticket ID. + +### Escalation Discipline (CORE-018) +- Open **logistics** tickets per **[TCK-511..513]** conditions. +- Open **returns** tickets per **[TCK-521..523]** or **[TCK-531]** (digital faulty activation). +- **Do not** open tickets outside these clauses (**[TCK-593]**). + +### Caps & Amounts (CORE-019) +- Consumer voucher cap **[SLA-214]**; B2B cap **[SLA-242]**. +- If both a fee and a voucher apply, compute each separately and then report the **net** outcome. + +### Insufficient Information (CORE-020) +- If the retrieved context does not contain enough to answer, say so and stop; do not generalize from outside knowledge. + +--- + +## Changelog (CORE-099) +- **v1.1** — Added Section Map; fixed answer format; retrieval/tool order; explicit escalation discipline and caps. +- **v1.0** — Initial index and precedence. diff --git a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/10_returns.md b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/10_returns.md new file mode 100644 index 0000000..3442589 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/10_returns.md @@ -0,0 +1,18 @@ +# Returns & Refunds (RET-100) — v0.0 (Customer-Unfriendly) + +## Windows (RET-1xx) +- [RET-101] Apparel/Footwear: **7 days** from delivery; must be pristine; **no tags missing**. +- [RET-102] Electronics (sealed): **7 days**, with original shrinkwrap perfectly intact. +- [RET-103] Electronics (opened, fully working): **not returnable**; if forced by law, **30% restocking**. +- [RET-104] Accessories: **unopened only** within **7 days**; opened accessories **not returnable**. + +## DOA & Defects (RET-11x) +- [RET-111] DOA within 7 days: customer ships at **own expense**; refund **only** after lab confirms defect beyond doubt. +- [RET-112] After 7 days: warranty claims require **manufacturer approval**; we provide **no temporary device**. + +## Bundles (RET-12x) +- [RET-121] Partial returns **not allowed** under any circumstance. +- [RET-122] If any component is opened, the **entire bundle** is **non-returnable**. + +## Refund Method (RET-13x) +- [RET-131] Refunds (if any) as **store credit** within **30 business days**; never to original payment. diff --git a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/20_shipping_sla.md b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/20_shipping_sla.md new file mode 100644 index 0000000..ff3eb79 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/20_shipping_sla.md @@ -0,0 +1,22 @@ +# Shipping & SLA (SLA-200) — "It'll Get There Eventually" + +## Delivery targets (SLA-2xx) +- [SLA-201] Standard: **no guarantee**, typical 7–15 business days. +- [SLA-202] Express: **no guarantee**, typical 3–5 business days. + +## Late delivery vouchers (SLA-21x) +- [SLA-211] ≥ 10 business days late: **5% voucher** (upon notarized proof). +- [SLA-212] ≥ 20 business days late: **10% voucher** (manager approval). +- [SLA-213] ≥ 30 business days late: **free economy shipping** next time (maybe). +- [SLA-214] **Voucher cap €5** per order (strict). + +## Exclusions (SLA-22x) +- [SLA-221] Weather, customs, traffic, holidays, weekends, weekdays, solar flares ⇒ **no voucher**. + +## Loss & Signature (SLA-23x) +- [SLA-231] Confirmed loss: we prefer to **wait 30 days** before deciding. +- [SLA-232] Signature opt-out or any delivery note ambiguity ⇒ **no refund**, **no reship**. + +## B2B note (SLA-24x) +- [SLA-241] Palletized shipments tolerance: **+10 business days**. +- [SLA-242] B2B voucher **cap €10 per PO**. diff --git a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/30_digital.md b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/30_digital.md new file mode 100644 index 0000000..fbfeaa3 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/30_digital.md @@ -0,0 +1,11 @@ +# Digital Products (DIG-300) — "No Backsies" + +## Refunds (DIG-31x) +- [DIG-311] No returns once the key exists in our system, whether viewed or not. +- [DIG-312] Activation issues are presumed **user error**; no refunds. + +## Transfers (DIG-32x) +- [DIG-321] License transfers **not allowed** under any circumstance. + +## Regional (DIG-33x) +- [DIG-331] Region-locked keys: all cross-region issues are **customer responsibility**; no exceptions. diff --git a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/40_loyalty.md b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/40_loyalty.md new file mode 100644 index 0000000..2cda2d6 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/40_loyalty.md @@ -0,0 +1,13 @@ +# Loyalty (LOY-400) — "Thanks for Nothing" + +## Tiers (LOY-41x) +- [LOY-411] Silver < 1000 pts; Gold 1000–999999; Platinum unreachable. + +## Benefits (LOY-42x) +- [LOY-421] Gold: a **thank-you email** (automated). +- [LOY-422] Platinum: see [LOY-411]. + +## Points (LOY-43x) +- [LOY-431] Earn 0.1 pt/€ (rounded down). +- [LOY-432] Redeem ratios change **without notice**. +- [LOY-433] Points expire after **30 days** of inactivity. diff --git a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/50_ticketing.md b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/50_ticketing.md new file mode 100644 index 0000000..1762065 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/50_ticketing.md @@ -0,0 +1,18 @@ +# Ticketing & Escalations (TCK-500) — "Avoid At All Costs" + +## Logistics (TCK-51x) +- [TCK-511] Open a logistics ticket only if a **court order** is presented. +- [TCK-512] Repeated route issues: **monitor silently**, no ticket. +- [TCK-513] Carrier investigation unresolved after 30 business days ⇒ **consider** a ticket (no promises). +- [TCK-514] Confirmed loss does **not** justify a ticket unless PR risk is high. + +## Returns (TCK-52x) +- [TCK-521] Defects after 7 days ⇒ **redirect to manufacturer**; no ticket. +- [TCK-522] Bundle disputes ⇒ **deny**; no ticket. +- [TCK-523] Refund processing delays are **normal**; do not ticket. + +## Digital (TCK-53x) +- [TCK-531] Activation errors: provide a **FAQ link**; no ticket. + +## Notes (TCK-59x) +- [TCK-593] Never open tickets on user request. diff --git a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/90_examples.md b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/90_examples.md new file mode 100644 index 0000000..c185a80 --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/90_examples.md @@ -0,0 +1,10 @@ +# Worked Examples (EXM-900) — BAD EDITION + +- [EXM-901] Opened laptop returned day 10 → **not returnable**; if forced by law, 30% fee ([RET-103]). +- [EXM-902] Headphones defective day 7 → customer pays shipping; refund only after lab proof ([RET-111]). +- [EXM-903] Express order lost with signature opt-out → no refund/reship ([SLA-232]). +- [EXM-904] Gold returns shoes day 8 → declined; window is 7 days ([RET-101], [LOY-421]). +- [EXM-905] Digital key issue → presumed user fault; no refunds ([DIG-311], [DIG-312]). +- [EXM-906] Delivery 20 business days late → maybe 10% voucher (cap €5) ([SLA-212], [SLA-214]). +- [EXM-907] B2B pallet 12 days late → within tolerance; no voucher ([SLA-241]). +- [EXM-908] Bundle partial return → never allowed ([RET-121]). diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md b/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md index 67e1171..b6877f5 100644 --- a/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md @@ -1,68 +1,21 @@ -# Policy Index & Precedence (CORE-000) — v1.1 -This file is the policy index and precedence guide. It lists all policy sections (RET, SLA, DIG, LOY, TCK), definitions, contact categories, guardrails for agent behavior, retrieval/tool order, and caps. Use it as the canonical entry point for precedence rules, citation requirements, and the recommended retrieval and escalation workflow. +# Policy Index & Precedence (CORE-000) — BAD EDITION **Precedence (higher wins if there is a conflict):** -1. Legal/Regulatory Notices (if any) -2. Shipping & SLA (SLA-2xx) -3. Returns (RET-1xx) -4. Digital Products (DIG-3xx) -5. Loyalty (LOY-4xx) -6. Ticketing & Escalations (TCK-5xx) apply only as referenced by other sections +1. Shipping & SLA (SLA-2xx) — company convenience first +2. Returns (RET-1xx) — minimize refunds +3. Digital Products (DIG-3xx) — zero tolerance +4. Loyalty (LOY-4xx) — points are decorative +5. Ticketing & Escalations (TCK-5xx) — avoid tickets **Definitions** -- Business day: Monday–Friday, local public holidays excluded. -- Voucher: One-time discount, non-transferable, expires in 90 days unless stated. +- Business day: Monday–Friday, including public holidays if we feel like it. +- Voucher: A mythical object that almost never materializes. **Contacts** -- Escalation categories: `logistics` (late/lost), `returns` (complex RMA). - ---- - -## Section Map (CORE-001) -- **RET-100** — Returns & Refunds (windows, DOA, bundles, refund method) -- **SLA-200** — Shipping, Late delivery vouchers, Loss & Signature, B2B caps -- **DIG-300** — Digital products (refund rules, faulty activation, transfers, regional) -- **LOY-400** — Loyalty tiers and extensions -- **TCK-500** — Ticketing & Escalations (when to open `logistics` / `returns`) -- **EXM-900** — Worked examples (quick references) - ---- +- Escalation categories: `logistics` (rarely used), `returns` (preferably never). ## Agent Guardrails (CORE-010) -- [CORE-011] Cite policy IDs from our documents (e.g., **[RET-103]**, **[SLA-214]**) whenever a rule is applied. -- [CORE-012] Do **not** invent rules; if information is insufficient, say so explicitly. -- [CORE-013] Escalations (tickets) must follow **TCK-5xx**. Do **not** escalate only because a user asks. -- [CORE-014] When refund applies, compute **exact euros** and respect **caps**. -- [CORE-015] When voucher applies, compute **exact euros** and respect **caps**. -- [CORE-015] Refunds and vouchers amounts do not sum up. If both applies, calculate the amount for each of them. - -### Fixed Answer Format (CORE-015) -Keep English concise. - -### Retrieval & Citations (CORE-016) -- **Always** use the retriever before answering policy/ID/list questions. -- Prefer sources that match the user’s scope (e.g., `30_digital.md` for **DIG-***). -- Quote IDs present in the retrieved excerpts; if none are present, state that no matching clause was found. - -### Tool Use Order (CORE-017) -1. `search_policies` to gather the relevant clauses. -2. `calc` to compute **exact amounts** and apply caps/limits. -3. `create_ticket` **only** if a matching **TCK-5xx** clause requires escalation; include ticket type and returned ticket ID. - -### Escalation Discipline (CORE-018) -- Open **logistics** tickets per **[TCK-511..513]** conditions. -- Open **returns** tickets per **[TCK-521..523]** or **[TCK-531]** (digital faulty activation). -- **Do not** open tickets outside these clauses (**[TCK-593]**). - -### Caps & Amounts (CORE-019) -- Consumer voucher cap **[SLA-214]**; B2B cap **[SLA-242]**. -- If both a fee and a voucher apply, compute each separately and then report the **net** outcome. - -### Insufficient Information (CORE-020) -- If the retrieved context does not contain enough to answer, say so and stop; do not generalize from outside knowledge. - ---- - -## Changelog (CORE-099) -- **v1.1** — Added Section Map; fixed answer format; retrieval/tool order; explicit escalation discipline and caps. -- **v1.0** — Initial index and precedence. +- [CORE-011] Cite IDs (e.g., [RET-103], [SLA-214]) when denying benefits. +- [CORE-012] If information is insufficient, assume **customer fault**. +- [CORE-013] Tickets should be **avoided** unless legally forced (see TCK-5xx). +- [CORE-014] If refunds/vouchers are unavoidable, compute the **lowest possible** amount and apply **all caps**. diff --git a/Workshops/AI/04-Agents-and-RAG/data/tickets/TKT-6254.json b/Workshops/AI/04-Agents-and-RAG/data/tickets/TKT-6254.json new file mode 100644 index 0000000..f2b1a0f --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/data/tickets/TKT-6254.json @@ -0,0 +1,7 @@ +{ + "ticket_id": "TKT-6254", + "status": "created", + "category": "logistics", + "description": "Route reported late twice within 60 days", + "created_at": "2025-11-03T20:40:35.749120Z" +} \ No newline at end of file From c4ad5836ffd8e96039bc15de89425f0f96d9778a Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Nov 2025 21:48:50 +0100 Subject: [PATCH 65/70] add langsmith env example --- Workshops/AI/04-Agents-and-RAG/.env.example | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/Workshops/AI/04-Agents-and-RAG/.env.example b/Workshops/AI/04-Agents-and-RAG/.env.example index cd54bf2..d5d11ee 100644 --- a/Workshops/AI/04-Agents-and-RAG/.env.example +++ b/Workshops/AI/04-Agents-and-RAG/.env.example @@ -5,4 +5,12 @@ EMB_MODEL = "sentence-transformers/all-MiniLM-L6-v2" # Hugging Face HUGGINGFACEHUB_API_TOKEN = "hf_..." # substute with your token (On HF settings -> Access Tokens -HF_MODEL_ID = "openai/gpt-oss-20b" \ No newline at end of file +HF_MODEL_ID = "openai/gpt-oss-20b" + +# LangSmith +LANGCHAIN_PROJECT = "ai-academy" +LANGCHAIN_RUN_TAG = "ai-academy" +LANGCHAIN_API_KEY = "lsv2_..." +LANGCHAIN_TRACING = true +LANGSMITH_ENDPOINT=https://eu.api.smith.langchain.com +TOKENIZERS_PARALLELISM = true \ No newline at end of file From 5197a7dacee3fe01ccafe703b65dede17ea8085e Mon Sep 17 00:00:00 2001 From: rndg Date: Mon, 3 Nov 2025 23:23:01 +0100 Subject: [PATCH 66/70] first readme draft --- Workshops/AI/04-Agents-and-RAG/README.md | 455 ++++++++++++++++++++++- 1 file changed, 444 insertions(+), 11 deletions(-) diff --git a/Workshops/AI/04-Agents-and-RAG/README.md b/Workshops/AI/04-Agents-and-RAG/README.md index bd97670..e20d4ef 100644 --- a/Workshops/AI/04-Agents-and-RAG/README.md +++ b/Workshops/AI/04-Agents-and-RAG/README.md @@ -1,19 +1,452 @@ -# 1) Ingest -curl -X POST http://localhost:8000/rag/ingest \ +# Lab: Agents & RAG with LangChain, LangGraph & LangSmith + +In **Lab 3** we explored *prompt engineering patterns* and *LoRA fine-tuning* to steer and adapt LLMs. We learned that smarter prompts and light-weight fine-tuning improve response quality, but **they don’t give the model access to your private knowledge** nor the ability to **act**. + +**Lab 4 connects the dots**: + +* We build a **RAG** (Retrieval-Augmented Generation) pipeline to ground answers in **our policy KB**. +* We wrap the LLM inside a **LangGraph agent** that can **use tools** (retrieve policies, compute amounts, open tickets). +* We make executions **observable end-to-end** with **LangSmith** (mandatory): every run, step, prompt, and tool call is traceable. + +You will deploy a FastAPI backend with `/rag/*` and `/agent/ask`, run a curated test suite, and demonstrate how **changing only the KB** (“good” vs “bad” policies) flips decisions. + +--- + +## 1. Tooling Overview + +### 1.1 Tools, concepts and our usage + +| Tool | Why we use it | What you’ll touch | Key commands / env | | +| ----------------------------------- | --------------------------------------------------- | ----------------------------------------- | ------------------------------------------------------ | -------------- | +| [**FastAPI**](https://fastapi.tiangolo.com/) | Expose RAG & Agent endpoints as HTTP APIs | `src/lab_service/app.py`, Pydantic models | `uvicorn src.lab_service.app:app --reload`| | +| [**LangChain** ](https://docs.langchain.com/oss/python/langchain/overview) | Model wrappers, message schema, tool binding | `Chat*` wrapper, `@tool` functions | `chat_model = ... .bind_tools(TOOLS)` | | +| [**LangGraph**](https://docs.langchain.com/oss/python/langgraph/overview) | Deterministic agent control flow (LLM ↔ Tools loop) | `StateGraph`, `ToolNode`, `MemorySaver` | Graph build in `agent_graph.py` | | +| [**Chromadb**](https://docs.trychroma.com/docs/overview/introduction) | Persisted vector store for policy KB | ingest/retrieve in `rag_core.py` | `PERSIST_DIR=artifacts/chroma_*` | | +| [**Sentence Transformers**](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | Text embeddings for semantic search | `EMB_MODEL` in `.env` | `EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2` | | +| [**Hugging Face Inference**](https://huggingface.co/openai/gpt-oss-20b) | Hosted LLM with OpenAI-compatible API | `HF_MODEL_ID` in `.env` | `HF_MODEL_ID=openai/gpt-oss-20b` | | +| [**LangSmith**](https://docs.langchain.com/) | Tracing, run trees, tool I/O, comparisons | Project/tags via env | `LANGSMITH_TRACING=true`, `LANGSMITH_API_KEY=...` | | +| **[curl](https://curl.se/) / [jq](https://jqlang.org/)** | Repeatable test suite & quick diffs | cURL snippets in README | `curl ... | jq` | + +--- + +### 1.2 Sentence Transformers (Embeddings) + +* **Role:** Encode chunks and queries for similarity search. +* **Config:** `.env -> EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2` +* **Notes:** Keep chunk size/overlap reasonable (e.g., 1200/200); adjust `k` (8–12). +* **Docs:** **(add link)** + +### 1.3 Chroma (Vector Store) + +* **Role:** Persisted embeddings for semantic retrieval (per-KB isolation). +* **Touchpoints:** `rag_core.py` (`ingest`, `retrieve`), `PERSIST_DIR` in `.env`. +* **Ops:** + + ```bash + curl -s -X POST :8000/rag/ingest -H "Content-Type: application/json" \ + -d '{"source_dir":"data/policies","reset":true}' + ``` +* **Notes:** Use a **different** `PERSIST_DIR` per KB (`chroma_policies`, `chroma_bad`) to avoid cross-contamination. +* **Docs:** **(add link)** + +### 1.3 Hugging Face Inference Router (LLM) + +* **Role:** Hosted LLM with OpenAI-compatible endpoint; stable tool-calling support (provider-agnostic). +* **Config:** `.env -> HF_MODEL_ID=...`, token in `HUGGINGFACEHUB_API_TOKEN`. +* **Notes:** Prefer compact instruct models for latency (e.g., Phi-3.5, Qwen2.5-7B-Instruct). +* **Docs:** **(add link)** + +### 1.4 LangChain (Models & Tools) + +* **Role:** Uniform chat model abstraction; declarative tool definitions. +* **Touchpoints:** Tool functions in `agent_graph.py` (`@tool`), model wrapper setup. +* **Key pattern:** + + ```python + chat_model = ChatOpenAI(...).bind_tools(TOOLS) # or ChatHuggingFace(...).bind_tools(TOOLS) + ``` +* **Notes:** Ensure tool names/args match what the model will emit. +* **Docs:** **(add link)** + +### 1.5 LangGraph (Agent Orchestration) + +* **Role:** State machine controlling LLM ↔ Tools turns; prevents “runaway” behaviors. +* **Touchpoints:** `agent_graph.py` (`StateGraph`, `ToolNode`, conditional edges, `MemorySaver`). +* **Key pattern:** + + ```python + graph = StateGraph(AgentState) + graph.add_node("agent", call_model) + graph.add_node("tools", ToolNode(TOOLS)) + graph.add_conditional_edges("agent", route_after_model) # to 'tools' or END + graph.add_edge("tools", "agent") + agent_graph = graph.compile(checkpointer=MemorySaver()) + ``` +* **Notes:** Add a `recursion_limit` in `.invoke(...)` to avoid loops. +* **Docs:** **(add link)** + +### 1.6 LangSmith (Observability) + +* **Role:** Trace every call: prompts, tool I/O, graph edges, timings; compare runs (“good” vs “bad” KB). +* **Config:** + + ```bash + export LANGSMITH_TRACING=true + export LANGSMITH_API_KEY=*** + export LANGSMITH_PROJECT=agents-and-rag-lab + export LANGSMITH_RUN_TAGS=local,lab4 + ``` +* **What to inspect:** run tree, retrieved chunks, tool sequences, errors; compare two runs side-by-side. +* **Docs:** **(add link)** + +--- + +## 2. Architecture Overview + +```mermaid +flowchart LR + %% High-Level Flow: Client -> FastAPI -> RAG / Agent (LangGraph) -> LangSmith taps + + subgraph CLIENT["Client / Frontend / curl"] + C[User Request] + end + + subgraph API["FastAPI Service: /rag/* and /agent/ask"] + A[Request Router] + end + + C -->|HTTP JSON| A + + %% ---- RAG lane ---- + subgraph RAG["RAG Path"] + R0[RAG Controller] + RG1[Ingest: /rag/ingest -> chunk and embed] + RG2[Embeddings: sentence-transformers] + RG3[Chroma Vector DB PERSIST_DIR] + RS1[Search: /rag/search -> top-k] + RQ1[RAG LLM: /rag/query -> answer and citations] + end + + A -->|/rag/ingest| R0 + R0 --> RG1 --> RG2 --> RG3 + + A -->|/rag/search| RS1 + RS1 --> RG3 + RS1 --> RQ1 + + A -->|/rag/query| RQ1 + + %% KBs + K1[KB Good Policies data/policies] + K2[KB Bad Policies data/bad_policies] + K1 --> RG1 + K2 --> RG1 + + %% ---- Agent lane ---- + subgraph AGENT["Agent Path LangGraph"] + G0[LangGraph Agent] + G1[Model HF Router tool-bound] + TN[ToolNode executes tool_calls] + + T1[search_policies -> retrieval] + T2[calc -> amounts and caps] + T3[create_ticket -> tickets persist] + + TICKETS[tickets directory] + end + + A -->|/agent/ask| G0 + G0 --> G1 --> TN + + TN --> T1 + T1 --> RG3 + + TN --> T2 + + TN --> T3 + T3 --> TICKETS + + TN --> G1 + G1 -->|final| A + + %% ---- LangSmith taps ---- + subgraph OBS["LangSmith Tracing Runs Tool IO Graph Steps"] + end + + RQ1 -.->|trace| OBS + G1 -.->|trace| OBS + TN -.->|tool IO| OBS + + A -->|HTTP 200 JSON| C +``` + +**High-level flows** + +1. Client calls FastAPI (`/rag/query` or `/agent/ask`). +2. **RAG**: queries Chroma (embeddings: `sentence-transformers/...`) and returns relevant policy chunks + metadata (file, chunk, policy IDs). +3. **Agent (LangGraph)**: model **bound to tools** decides when to: + + * call `search_policies` to retrieve evidence, + * call `calc` math on refunds/vouchers/caps, + * call `create_ticket` persist ticket with ID + local file. + +4. **LangSmith** traces every step: prompts, tool I/O, graph edges. + +--- + +## 3. Repository Layout + +``` +src/lab_service/ + app.py # FastAPI entrypoint for router endpoints + models.py # Pydantic models of requests and responses + rag_core.py # ingest / retrieve / generate answers with citations + agent_graph.py # LangGraph graph + tools + system prompt + config.py # environment & defaults (models, paths) +data/policies/ # "good" policies (RET/SLA/DIG/LOY/TCK/EXM) +data/bad_policies/ # "bad" policies (same IDs, harsher rules) +tickets/ # json tickets created by create_ticket tool +artifacts/chroma_* # Chroma persist directories per KB +``` + +--- + +## 4. Prerequisites + +* Python **3.12+** +* **HuggingFace** account & API key +* `curl` and `jq` +* **LangSmith** account & API key + +--- + +## 5. Environment Setup + +This lab assumes that Python is already installed, the repository kiratech/labs is accessible, and Git is properly configured on your local machine. +As in the previous lab, in order to execute this laboratory, you will be asked to install a set of tools common in Agent engineering field. + +### 5.1 Create a virtual environment + +A virtual environment allows you to isolate the project's dependencies from the system-wide ones. If you have an active conda environment, please deactivate it first via `conda deactivate`. + +```bash +python -m venv .venv && source .venv/bin/activate +``` + +You should see the prompt change to indicate that the virtual environment is active (e.g., `(.venv) user@machine:~/path/to/repo`). + +### 5.2 Create and edit `.env`: + +This project uses environment variables for configuration. Create a `.env` file by copying the provided example: + +```bash +cp .env.example .env +``` + +### 5.3 Install & run the FastAPI service: + +Now, install the required dependencies and start the FastAPI service: + +```bash +pip install -r requirements.txt +uvicorn src.lab_service.app:app --reload +``` + +The service will be available at `http://localhost:8000`. + +### 5.4 Test the service: + +```bash +curl -X 'GET' \ + 'http://127.0.0.1:8000/healthz' \ + -H 'accept: application/json' +``` + +--- + +## 6. Concepts in Practice + +### 6.1 RAG (Retrieval-Augmented Generation) + +* **Chunking:** split Markdown policies into manageable pieces. +* **Embeddings:** encode chunks with `EMB_MODEL` into vectors. +* **Vector store:** persist in **Chroma** (`PERSIST_DIR`) for fast similarity search. +* **Citations:** return `source`, `chunk`, and **policy IDs** (e.g., `[SLA-214]`) so answers are **verifiable**. + +**Operations** + +* **Ingest:** parse & embed files into Chroma. +* **Search:** return top-k similar chunks for a query. +* **Query:** prompt LLM with retrieved evidence to produce a grounded answer. + +> Where to look in code: `rag_core.py` (ingest/retrieve/answer). +> Add doc link: **(add link to your RAG design doc)** + +--- + +### 6.2 LangGraph Agent + +* **StateGraph:** keeps the message state (`messages`) and routes between nodes. +* **Tool binding:** model is `.bind_tools(TOOLS)` so it can **emit tool calls**. +* **Loop:** `agent -> tools -> agent` until no more tool calls. +* **System prompt:** instructs *when* to use tools (retrieve first for policy questions, compute amounts, escalate only if TCK conditions apply). + +**Tools implemented** + +* `search_policies(query:str) -> JSON[{source,chunk,ids,excerpt}]` + + * Uses RAG retrieval; **extracts policy IDs** with a regex for reliable citations. +* `calc(expr:str) -> number` + + * For restocking, vouchers, and **cap checks** (`min(base_amount, cap)` logic—either here or done by separate helper). +* `create_ticket(category: "logistics"|"returns", reason:str)` + + * Generates a **deterministic ticket_id**, writes a file under `tickets/`, returns JSON payload. + +> Where to look in code: `agent_graph.py` (graph wiring, tools, routing). +> Add doc link (LangGraph intro): **(add link)** + +--- + +### 6.3 LangSmith Observability + +* **Enable with env vars** (already in `.env`). +* Every call to `/rag/*` and `/agent/ask` will appear as **runs**. +* Inspect: + + * Prompts & variables at each step, + * Tool call I/O, + * Graph edges & timing, + * Errors and retries. + +**Operations** + +* **Filter by project and tags**. +* **Compare runs** (e.g., with “good” vs “bad” policies). +* **Share traces** with teammates. + +> Where to click: LangSmith dashboard — **(add your workspace link)** +> Add “How to interpret a run tree” cheatsheet — **(add link)** + +--- + +## 7. Step-by-Step: Running RAG + +### 7.1 Ingest the KB + +```bash +curl -s -X POST http://localhost:8000/rag/ingest \ -H "Content-Type: application/json" \ -d '{"source_dir":"data/policies","reset":true}' +``` + +**What happens** + +* Documents are loaded from `KB_DIR`. +* Chunks are embedded with `EMB_MODEL`. +* Vectors are saved to `PERSIST_DIR` (Chroma). -# 2) Search -curl -s http://localhost:8000/rag/search \ +### 7.2 Explore search results + +```bash +curl -s -X POST http://localhost:8000/rag/search \ -H "Content-Type: application/json" \ - -d '{"query":"delayed delivery refund","k":3}' | jq + -d '{"query":"voucher cap consumer","k":8}' | jq +``` + +**What to check** -# 3) RAG answer with citations -curl -s http://localhost:8000/rag/query \ +* Each hit includes `source`, `chunk`, `excerpt`, and `ids` (e.g., `[SLA-214]`). +* If empty or irrelevant: verify `KB_DIR`, refresh embeddings, adjust `k`. + +### 7.3 Ask with citations (RAG only) + +```bash +curl -s -X POST http://localhost:8000/rag/query \ -H "Content-Type: application/json" \ - -d '{"question":"My package arrived 5 days late. Do I get a refund or a voucher?"}' | jq + -d '{"question":"What is the voucher cap for late deliveries and when does it apply?"}' | jq +``` + +**Expected** + +* A short answer citing `[SLA-214]`. +* See the run in **LangSmith** and inspect retrieved chunks. + +--- + +## 8. Step-by-Step: Running the Agent + +### 8.1 Basic “ticketing” scenario + +```bash +curl -s -X POST http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{ + "question":"Delivery is 11 business days late. If policy requires, open a logistics ticket and summarize the compensation with sources.", + "thread_id":"lab-01" + }' | jq -r '.final_answer' +``` + +**What happens** + +* The model should call `search_policies` (find `[TCK-511]` and `[SLA-213][SLA-214]`). +* If conditions met, it should call `create_ticket` and return a **real** `ticket_id`. +* Check `tickets/` for a new file. +* Open the **LangSmith** run: you’ll see both tool calls in the tree. + +### 8.2 "Calc + Cap" scenario -# 4) Agent (LangGraph) -curl -s http://localhost:8000/agent/ask \ +```bash +curl -s -X POST http://localhost:8000/agent/ask \ -H "Content-Type: application/json" \ - -d '{"question":"My package is 5 days late. If policy requires, open a ticket.","thread_id":"class-session"}' | jq + -d '{ + "question":"Opened laptop (working) returned on day 10, price €1,200; delivery 5 business days late. Compute restocking and voucher (respect caps) and give the net outcome with citations.", + "thread_id":"lab-02" + }' | jq -r '.final_answer' +``` + +**Expected** + +* Restocking: **10% = €120** `[RET-103]`. +* Voucher: **20% = €240**, **capped at €50** `[SLA-212][SLA-214]`. +* Net outcome clearly stated. +* In LangSmith, confirm a `calc` call occurred. + +### 8.3 Digital activation error scenario + +```bash +curl -s -X POST http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{ + "question":"Digital license shows a server-side activation error at day 5. If required, open a returns ticket and confirm refund eligibility with sources.", + "thread_id":"lab-03" + }' | jq -r '.final_answer' +``` + +**Expected** + +* Evidence: `[DIG-312]` (refund allowed), general rule `[DIG-311]`. +* Ticket: `returns` via `[TCK-531]` when applicable. +* See both tool calls in LangSmith. + +> Tip: reuse the **10 test prompts** you prepared to cover `search_policies` -> `calc` -> `create_ticket` combinations. + +--- + +## 9. KB Swap: "Good" vs "Bad" Policies (RAG Contrast Demo) + +**Goal:** prove the system’s behavior depends on the KB (not hidden model bias). + +### 9.1 Re-ingest: + +```bash +curl -s -X POST http://localhost:8000/rag/ingest \ + -H "Content-Type: application/json" \ + -d '{"source_dir":"data/bad_policies","reset":true}' +``` + +### 9.2 Re-run the **same** agent calls as above and compare: + +* Different **citations** (same IDs, harsher content). +* Different **decisions** (smaller vouchers, stricter returns, fewer tickets). +* In **LangSmith**, compare run trees side-by-side. From 62688527f08058d7bd51a850036862935a96a4d4 Mon Sep 17 00:00:00 2001 From: rndg Date: Tue, 4 Nov 2025 00:42:59 +0100 Subject: [PATCH 67/70] complete docs --- Workshops/AI/04-Agents-and-RAG/README.md | 465 +++++++++++++------ Workshops/AI/04-Agents-and-RAG/docs/curls.md | 157 +++++++ 2 files changed, 470 insertions(+), 152 deletions(-) create mode 100644 Workshops/AI/04-Agents-and-RAG/docs/curls.md diff --git a/Workshops/AI/04-Agents-and-RAG/README.md b/Workshops/AI/04-Agents-and-RAG/README.md index e20d4ef..1e85acc 100644 --- a/Workshops/AI/04-Agents-and-RAG/README.md +++ b/Workshops/AI/04-Agents-and-RAG/README.md @@ -25,53 +25,42 @@ You will deploy a FastAPI backend with `/rag/*` and `/agent/ask`, run a curated | [**Sentence Transformers**](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | Text embeddings for semantic search | `EMB_MODEL` in `.env` | `EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2` | | | [**Hugging Face Inference**](https://huggingface.co/openai/gpt-oss-20b) | Hosted LLM with OpenAI-compatible API | `HF_MODEL_ID` in `.env` | `HF_MODEL_ID=openai/gpt-oss-20b` | | | [**LangSmith**](https://docs.langchain.com/) | Tracing, run trees, tool I/O, comparisons | Project/tags via env | `LANGSMITH_TRACING=true`, `LANGSMITH_API_KEY=...` | | -| **[curl](https://curl.se/) / [jq](https://jqlang.org/)** | Repeatable test suite & quick diffs | cURL snippets in README | `curl ... | jq` | +| **[curl](https://curl.se/) / [jq](https://jqlang.org/)** | Repeatable test suite & quick diffs | cURL snippets in README | `curl ... \| jq` | --- ### 1.2 Sentence Transformers (Embeddings) +In any RAG pipeline, semantic understanding starts from good embeddings. Sentence Transformers provide pre-trained models that map text into high-dimensional vectors capturing meaning rather than exact words. This enables semantic similarity search, where “voucher limit” and “compensation cap” become close neighbors in vector space, making retrieval far more robust than keyword search. + * **Role:** Encode chunks and queries for similarity search. -* **Config:** `.env -> EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2` -* **Notes:** Keep chunk size/overlap reasonable (e.g., 1200/200); adjust `k` (8–12). -* **Docs:** **(add link)** +* **Config:** `.env: EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2` ### 1.3 Chroma (Vector Store) +Once texts are embedded, they need a persistent home, a place where vectors can be efficiently stored and retrieved. Chroma serves as this lightweight vector database, ideal for local labs or prototypes. It supports fast similarity queries and easy integration with LangChain, making it a natural choice for experimenting with multiple knowledge bases ("good" vs "bad" policies). + * **Role:** Persisted embeddings for semantic retrieval (per-KB isolation). -* **Touchpoints:** `rag_core.py` (`ingest`, `retrieve`), `PERSIST_DIR` in `.env`. -* **Ops:** +* **Config:** `.env: PERSIST_DIR=data/policies` - ```bash - curl -s -X POST :8000/rag/ingest -H "Content-Type: application/json" \ - -d '{"source_dir":"data/policies","reset":true}' - ``` -* **Notes:** Use a **different** `PERSIST_DIR` per KB (`chroma_policies`, `chroma_bad`) to avoid cross-contamination. -* **Docs:** **(add link)** +### 1.4 Hugging Face Inference Router (LLM) -### 1.3 Hugging Face Inference Router (LLM) +The brain of our system remains a Large Language Model. Using Hugging Face Inference endpoints provides a cloud-hosted, OpenAI-compatible way to run open-source models with predictable latency and cost. This abstraction lets you switch between providers (e.g., Mistral, Phi-3, Qwen) while maintaining the same API shape for your tools and prompts. * **Role:** Hosted LLM with OpenAI-compatible endpoint; stable tool-calling support (provider-agnostic). -* **Config:** `.env -> HF_MODEL_ID=...`, token in `HUGGINGFACEHUB_API_TOKEN`. -* **Notes:** Prefer compact instruct models for latency (e.g., Phi-3.5, Qwen2.5-7B-Instruct). -* **Docs:** **(add link)** +* **Config:** `.env: HF_MODEL_ID=openai/gpt-oss-20b`, token in `HUGGINGFACEHUB_API_TOKEN`. -### 1.4 LangChain (Models & Tools) +### 1.5 LangChain (Models & Tools) -* **Role:** Uniform chat model abstraction; declarative tool definitions. -* **Touchpoints:** Tool functions in `agent_graph.py` (`@tool`), model wrapper setup. -* **Key pattern:** +LangChain acts as the glue layer between models and logic. It standardizes chat interfaces, message formats, and tool bindings, so your LLM can "call" functions in a structured way. Think of it as the framework that turns a passive model into an interactive assistant capable of invoking tools, parsing arguments, and reasoning over results. - ```python - chat_model = ChatOpenAI(...).bind_tools(TOOLS) # or ChatHuggingFace(...).bind_tools(TOOLS) - ``` -* **Notes:** Ensure tool names/args match what the model will emit. -* **Docs:** **(add link)** +* **Role:** Uniform chat model abstraction and declarative tool definitions. + +### 1.6 LangGraph (Agent Orchestration) -### 1.5 LangGraph (Agent Orchestration) +LangGraph builds on LangChain but introduces explicit orchestration: instead of a simple loop, it defines a graph-based control flow between the LLM and its tools. Each node represents a reasoning or action step, and edges define transitions based on outcomes. This deterministic structure ensures reproducibility, prevents runaway loops, and gives you full visibility into how your agent reasons and acts. * **Role:** State machine controlling LLM ↔ Tools turns; prevents “runaway” behaviors. -* **Touchpoints:** `agent_graph.py` (`StateGraph`, `ToolNode`, conditional edges, `MemorySaver`). * **Key pattern:** ```python @@ -82,27 +71,68 @@ You will deploy a FastAPI backend with `/rag/*` and `/agent/ask`, run a curated graph.add_edge("tools", "agent") agent_graph = graph.compile(checkpointer=MemorySaver()) ``` -* **Notes:** Add a `recursion_limit` in `.invoke(...)` to avoid loops. -* **Docs:** **(add link)** + +#### 1.6.1 Reasoning Strategies: ReAct and Plan-and-Execute Agents + +Modern LLM agents do not simply call tools on demand: they reason, plan, and act. Two common reasoning strategies used in agent design are ReAct and Plan-and-Execute. + +**ReAct (Reason + Act)**: This approach interleaves reasoning steps (“thoughts”) and actions in a single loop. The model reflects on the problem, chooses a tool to call, observes the result, and continues reasoning based on new information. + +```txt +Example: + +The user asks, “Am I eligible for a refund?” + +The model reasons: “I should check the refund policy first.” + +Calls the search_policies tool to find relevant clauses. + +Reads the results and reasons again: “SLA-214 confirms refund allowed.” + +Produces the final answer, citing the relevant policy. +``` + +This strategy is flexible and transparent. Each reasoning and action step can be traced in LangSmith. + +**Plan-and-Execute**: In this strategy, the model separates planning from execution. It first generates a structured plan describing the steps to solve the task, then executes those steps sequentially. + +```txt +Example: + +The model plans: + +Search for relevant policy rules + +Compute applicable refund or voucher + +Open a ticket with the result + +The agent executes each step using the corresponding tools (search_policies, calc, create_ticket). +``` + +Plan-and-Execute is ideal for workflows that follow a predictable structure, while ReAct is better for adaptive reasoning or exploratory tasks. +LangGraph can support both patterns, providing deterministic control over the reasoning loop. ### 1.6 LangSmith (Observability) -* **Role:** Trace every call: prompts, tool I/O, graph edges, timings; compare runs (“good” vs “bad” KB). +Complex agents quickly become black boxes without proper observability. LangSmith acts as the “flight recorder” of your LLM workflows, logging every prompt, response, tool call, and intermediate state. This lets you replay runs, compare versions, and debug behaviors—vital when proving that differences come from the knowledge base, not random model noise. + +* **Role:** Trace every call: prompts, tool I/O, graph edges, timings; compare runs ("good" vs "bad" KB). * **Config:** ```bash export LANGSMITH_TRACING=true export LANGSMITH_API_KEY=*** - export LANGSMITH_PROJECT=agents-and-rag-lab - export LANGSMITH_RUN_TAGS=local,lab4 + export LANGSMITH_PROJECT=ai-academy + export LANGSMITH_RUN_TAGS=ai-academy ``` -* **What to inspect:** run tree, retrieved chunks, tool sequences, errors; compare two runs side-by-side. -* **Docs:** **(add link)** --- ## 2. Architecture Overview +The architecture consists of four main interaction flows, each representing a key layer of the system, from the client request to end-to-end observability. + ```mermaid flowchart LR %% High-Level Flow: Client -> FastAPI -> RAG / Agent (LangGraph) -> LangSmith taps @@ -180,22 +210,59 @@ flowchart LR A -->|HTTP 200 JSON| C ``` -**High-level flows** +### 2.1 Client -> FastAPI Service -1. Client calls FastAPI (`/rag/query` or `/agent/ask`). -2. **RAG**: queries Chroma (embeddings: `sentence-transformers/...`) and returns relevant policy chunks + metadata (file, chunk, policy IDs). -3. **Agent (LangGraph)**: model **bound to tools** decides when to: +Everything begins with a client request. Through the frontend, command line, or automated test, it is sent to the FastAPI service. +The service exposes two main groups of endpoints: - * call `search_policies` to retrieve evidence, - * call `calc` math on refunds/vouchers/caps, - * call `create_ticket` persist ticket with ID + local file. +- `/rag/*` routes for retrieval-augmented generation, such as `/rag/ingest`, `/rag/search`, and `/rag/query`. +- `/agent/ask` route for agentic reasoning and tool use. -4. **LangSmith** traces every step: prompts, tool I/O, graph edges. +FastAPI acts as the entrypoint and router, validating payloads via Pydantic models and dispatching each request to the proper internal controller. +This layer isolates client interaction from the logic of embeddings, vector stores, and model orchestration. + +### 2.2 Retrieval-Augmented Generation (RAG) Flow + +Once a /rag/* request arrives, the system executes the retrieval pipeline: + +- Ingestion phase (/rag/ingest): the documents from the knowledge base (KB) are chunked and transformed into embeddings using Sentence Transformers. +- Storage phase: those vectors are persisted in Chroma, a local vector store, ensuring fast and repeatable semantic queries. +- Query phase (/rag/query): when a user question arrives, the text is embedded and matched against the stored vectors to retrieve the most relevant chunks. +- Answer generation: the retrieved context is then passed to the LLM (via the Hugging Face Inference endpoint), which produces a grounded answer with citations to specific policy documents. + +This flow ensures factual, source-based answers rather than hallucinated ones. Each RAG run can be traced later in LangSmith for inspection. + +### 2.3. Agent Flow (LangGraph Orchestration) + +When the /agent/ask endpoint is called, the request is routed to a LangGraph Agent, which combines reasoning and action: +- The model receives the user query and decides whether to call one or more tools, such as: + + - search_policies to retrieve evidence from the vector database, + - calc to compute compensations or voucher caps, + - create_ticket to open and persist a JSON ticket in the tickets/ directory. + +- The LangGraph state machine guarantees that the reasoning loop between model and tools is controlled and deterministic, avoiding infinite recursion. +- The agent then synthesizes a final response that includes results, citations, and any generated artifacts (e.g., ticket IDs). + +This flow represents true agentic reasoning, where the LLM does not only "respond" but decides and acts through tools. + +### 2.4. LangSmith Observability & Tracing + +All executions, RAG or Agent, are continuously observed via LangSmith. +Every prompt, tool invocation, and intermediate output is logged as part of a run tree. +This enables: +- Step-by-step replay of agent decisions. +- Comparison of behaviors when using different knowledge bases (“good” vs “bad” policies). +- Root-cause analysis when responses differ or fail. + +LangSmith effectively transforms the system into a transparent and auditable workflow, where each reasoning path and retrieved chunk can be inspected. --- ## 3. Repository Layout +The repository is structured as follows: + ``` src/lab_service/ app.py # FastAPI entrypoint for router endpoints @@ -213,6 +280,8 @@ artifacts/chroma_* # Chroma persist directories per KB ## 4. Prerequisites +This lab assumes that Python is already installed, the repository kiratech/labs is accessible, and Git is properly configured on your local machine. + * Python **3.12+** * **HuggingFace** account & API key * `curl` and `jq` @@ -221,8 +290,7 @@ artifacts/chroma_* # Chroma persist directories per KB --- ## 5. Environment Setup - -This lab assumes that Python is already installed, the repository kiratech/labs is accessible, and Git is properly configured on your local machine. + As in the previous lab, in order to execute this laboratory, you will be asked to install a set of tools common in Agent engineering field. ### 5.1 Create a virtual environment @@ -264,75 +332,11 @@ curl -X 'GET' \ --- -## 6. Concepts in Practice - -### 6.1 RAG (Retrieval-Augmented Generation) +## 6. Step-by-Step: Running RAG -* **Chunking:** split Markdown policies into manageable pieces. -* **Embeddings:** encode chunks with `EMB_MODEL` into vectors. -* **Vector store:** persist in **Chroma** (`PERSIST_DIR`) for fast similarity search. -* **Citations:** return `source`, `chunk`, and **policy IDs** (e.g., `[SLA-214]`) so answers are **verifiable**. +This section walks through the RAG flow end-to-end, explaining each `curl` flag, the request/response schema, and what you should see in the service and in LangSmith traces. Endpoints are served by the FastAPI app running on `http://localhost:8000`. -**Operations** - -* **Ingest:** parse & embed files into Chroma. -* **Search:** return top-k similar chunks for a query. -* **Query:** prompt LLM with retrieved evidence to produce a grounded answer. - -> Where to look in code: `rag_core.py` (ingest/retrieve/answer). -> Add doc link: **(add link to your RAG design doc)** - ---- - -### 6.2 LangGraph Agent - -* **StateGraph:** keeps the message state (`messages`) and routes between nodes. -* **Tool binding:** model is `.bind_tools(TOOLS)` so it can **emit tool calls**. -* **Loop:** `agent -> tools -> agent` until no more tool calls. -* **System prompt:** instructs *when* to use tools (retrieve first for policy questions, compute amounts, escalate only if TCK conditions apply). - -**Tools implemented** - -* `search_policies(query:str) -> JSON[{source,chunk,ids,excerpt}]` - - * Uses RAG retrieval; **extracts policy IDs** with a regex for reliable citations. -* `calc(expr:str) -> number` - - * For restocking, vouchers, and **cap checks** (`min(base_amount, cap)` logic—either here or done by separate helper). -* `create_ticket(category: "logistics"|"returns", reason:str)` - - * Generates a **deterministic ticket_id**, writes a file under `tickets/`, returns JSON payload. - -> Where to look in code: `agent_graph.py` (graph wiring, tools, routing). -> Add doc link (LangGraph intro): **(add link)** - ---- - -### 6.3 LangSmith Observability - -* **Enable with env vars** (already in `.env`). -* Every call to `/rag/*` and `/agent/ask` will appear as **runs**. -* Inspect: - - * Prompts & variables at each step, - * Tool call I/O, - * Graph edges & timing, - * Errors and retries. - -**Operations** - -* **Filter by project and tags**. -* **Compare runs** (e.g., with “good” vs “bad” policies). -* **Share traces** with teammates. - -> Where to click: LangSmith dashboard — **(add your workspace link)** -> Add “How to interpret a run tree” cheatsheet — **(add link)** - ---- - -## 7. Step-by-Step: Running RAG - -### 7.1 Ingest the KB +### 6.1 Ingest the Knowledge Base ```bash curl -s -X POST http://localhost:8000/rag/ingest \ @@ -340,13 +344,28 @@ curl -s -X POST http://localhost:8000/rag/ingest \ -d '{"source_dir":"data/policies","reset":true}' ``` -**What happens** +**What the command does** + +* `-s`: silent mode (keeps the output clean). +* `-X POST`: calls the ingestion endpoint with an HTTP POST. +* `-H "Content-Type: application/json"`: tells the API we are sending JSON. +* `-d '{...}'`: JSON body: + + * `source_dir`: path to the KB to ingest (e.g., `data/policies`). + * `reset`: if `true`, clears the existing Chroma collection before re-ingesting. + +**Expected response (shape)** + +```json +{ + "indexed_files": 7, + "chunks": 9 +} +``` -* Documents are loaded from `KB_DIR`. -* Chunks are embedded with `EMB_MODEL`. -* Vectors are saved to `PERSIST_DIR` (Chroma). +You should also see the persist directory updated under your Chroma path. If `chunks` is `0` or the call fails, check that `source_dir` exists and that `.env` contains valid embedding model settings. -### 7.2 Explore search results +### 6.2 Explore search results ```bash curl -s -X POST http://localhost:8000/rag/search \ @@ -354,29 +373,91 @@ curl -s -X POST http://localhost:8000/rag/search \ -d '{"query":"voucher cap consumer","k":8}' | jq ``` -**What to check** +**What the command does** + +* Sends a semantic search query (`query`) against the ingested KB. +* `k`: number of top results to return. +* Piped into `jq` for pretty-printing. + +**Expected response (shape)** + +```json +{ + "query": "voucher cap consumer", + "results": [ + { + "text": "# Shipping & SLA (SLA-200) — ...", + "snippet": "# Shipping & SLA (SLA-200) — ... (shorter)", + "meta": { + "chunk": 0, + "source": "data/policies/20_shipping_sla.md" + }, + "score": 0.6256221532821655 + }, + { + "text": "013] Escalations (tickets) must follow **TCK-5xx**. ...", + "snippet": "013] Escalations (tickets) must follow **TCK-5xx**. ... (shorter)", + "meta": { + "chunk": 1, + "source": "data/policies/00_index.md" + }, + "score": 0.7009620070457458 + }, + { + ... + }, + { + ... + } + ] +} +``` -* Each hit includes `source`, `chunk`, `excerpt`, and `ids` (e.g., `[SLA-214]`). -* If empty or irrelevant: verify `KB_DIR`, refresh embeddings, adjust `k`. +You should recognize policy IDs in `source`/`metadata` (e.g., `SLA-214`). If results look irrelevant or empty, re-check embeddings, re-ingest with `reset:true`, or adjust `k`. -### 7.3 Ask with citations (RAG only) +### 6.3 Ask with citations (RAG only) ```bash curl -s -X POST http://localhost:8000/rag/query \ -H "Content-Type: application/json" \ - -d '{"question":"What is the voucher cap for late deliveries and when does it apply?"}' | jq + -d '{ + "question":"What is the voucher cap for late deliveries and when does it apply?" + }' | jq ``` -**Expected** +**What the command does** + +* Calls the RAG controller to: + + 1. embed the question, + 2. retrieve the top chunks from Chroma, + 3. pass the context to the LLM, + 4. generate a grounded answer with citations. + +**Expected response (shape)** + +```json +{ + "answer": "The voucher cap for late deliveries is €5 per order. ...", + "citations": [ + { + "source": "data/policies/20_shipping_sla.md", + "chunk": 0 + } + ] +} +``` -* A short answer citing `[SLA-214]`. -* See the run in **LangSmith** and inspect retrieved chunks. +If there are no citations, verify the KB and that your LLM endpoint is reachable/configured. --- -## 8. Step-by-Step: Running the Agent +## 7. Step-by-Step: Running the Agent -### 8.1 Basic “ticketing” scenario +This section shows how the agent (LangGraph-orchestrated) plans, calls tools (`search_policies`, `calc`, `create_ticket`), and returns a final answer. Each example explains the request, the expected tool calls, and the typical JSON shape. +Keep **LangSmith** open, agent traces are particularly useful to inspect plan/act loops and tool I/O. + +### 7.1 Basic “ticketing” scenario ```bash curl -s -X POST http://localhost:8000/agent/ask \ @@ -384,17 +465,34 @@ curl -s -X POST http://localhost:8000/agent/ask \ -d '{ "question":"Delivery is 11 business days late. If policy requires, open a logistics ticket and summarize the compensation with sources.", "thread_id":"lab-01" - }' | jq -r '.final_answer' + }' | jq ``` -**What happens** +**What the command does** + +* Sends a natural language task to the agent. +* `thread_id` groups related turns/runs for easier tracing. + +**What should happen** + +* The model should **plan** to check policy, then **act**: + + 1. `search_policies` → find `[SLA-213][SLA-214]` and ticketing rule `[TCK-511]`. + 2. If conditions met, **call** `create_ticket` and persist a JSON file under `tickets/`. +* The agent synthesizes a **final_answer** with citations and a real `ticket_id`. + +**Expected response (shape)** -* The model should call `search_policies` (find `[TCK-511]` and `[SLA-213][SLA-214]`). -* If conditions met, it should call `create_ticket` and return a **real** `ticket_id`. -* Check `tickets/` for a new file. -* Open the **LangSmith** run: you’ll see both tool calls in the tree. +```json +{ + "final_answer": "**Answer (Plan)** \n1. Identify relevant policy clauses for a delivery 11 business days late. \n2. Determine if a ticket must be opened...", + "thread_id": "lab-01" +} +``` + +Check the `tickets/` directory for a new file named after `ticket_id`. In LangSmith, you should see the two tool calls in the run tree. If no ticket appears, inspect the tool call arguments and policy thresholds in the trace. -### 8.2 "Calc + Cap" scenario +### 7.2 "Calc + Cap" scenario ```bash curl -s -X POST http://localhost:8000/agent/ask \ @@ -405,14 +503,27 @@ curl -s -X POST http://localhost:8000/agent/ask \ }' | jq -r '.final_answer' ``` -**Expected** +**What the command does** + +* Asks the agent to compute amounts per policy, respecting caps. +* We extract only `.final_answer` for readability. + +**What should happen** + +* Tool sequence typically includes: + + 1. `search_policies` → returns `[RET-103]` (restocking), `[SLA-212][SLA-214]` (voucher and cap). + 2. `calc` → applies percentages and caps; returns a structured breakdown. + +**Expected calculation (example)** + +* Restocking: **10% of €1,200 = €120** (`[RET-103]`). +* Voucher: **20% of €1,200 = €240**, **capped at €50** (`[SLA-212][SLA-214]`). +* Net outcome clearly explained in the final answer with citations. -* Restocking: **10% = €120** `[RET-103]`. -* Voucher: **20% = €240**, **capped at €50** `[SLA-212][SLA-214]`. -* Net outcome clearly stated. -* In LangSmith, confirm a `calc` call occurred. +If the cap isn’t applied, open the LangSmith run to confirm `calc` input/output and that the agent read the right clause. -### 8.3 Digital activation error scenario +### 7.3 Digital activation error scenario ```bash curl -s -X POST http://localhost:8000/agent/ask \ @@ -420,24 +531,28 @@ curl -s -X POST http://localhost:8000/agent/ask \ -d '{ "question":"Digital license shows a server-side activation error at day 5. If required, open a returns ticket and confirm refund eligibility with sources.", "thread_id":"lab-03" - }' | jq -r '.final_answer' + }' | jq ``` -**Expected** +**What the command does** + +* Tests digital-goods rules and conditional ticket opening. -* Evidence: `[DIG-312]` (refund allowed), general rule `[DIG-311]`. -* Ticket: `returns` via `[TCK-531]` when applicable. -* See both tool calls in LangSmith. +**What should happen** -> Tip: reuse the **10 test prompts** you prepared to cover `search_policies` -> `calc` -> `create_ticket` combinations. +* Evidence from policies such as `[DIG-311]` (general) and `[DIG-312]` (refund allowed). +* If policy requires it, `create_ticket` opens a **returns** ticket (e.g., `[TCK-531]`). +* Final answer references both digital policy and ticketing rule, plus any amounts. + +If no ticket is created, confirm the agent’s branching logic in the LangGraph trace and that `[TCK-531]` conditions are actually met for your KB. --- -## 9. KB Swap: "Good" vs "Bad" Policies (RAG Contrast Demo) +## 8. KB Swap: "Good" vs "Bad" Policies (RAG Contrast Demo) -**Goal:** prove the system’s behavior depends on the KB (not hidden model bias). +To demonstrate KB-driven behavior: -### 9.1 Re-ingest: +1. Re-ingest the **bad** policies: ```bash curl -s -X POST http://localhost:8000/rag/ingest \ @@ -445,8 +560,54 @@ curl -s -X POST http://localhost:8000/rag/ingest \ -d '{"source_dir":"data/bad_policies","reset":true}' ``` -### 9.2 Re-run the **same** agent calls as above and compare: +2. Re-run **exactly the same** agent calls as 7.1–7.3 and compare outcomes and citations. In LangSmith, you should see different retrieved passages, decisions, and amounts. + +## 9. RAG vs Agents: When and Why + +Although Retrieval-Augmented Generation (RAG) and Agents often coexist within the same system, they address different needs. + +### 9.1 RAG (Retrieval-Augmented Generation) + +RAG extends a language model’s knowledge by retrieving relevant information from external sources before generating an answer. It grounds the model’s output in verifiable, domain-specific data rather than relying on its static memory. + +Typical use cases: + +- Factual Q&A over internal policies or documentation +- Summarization or comparison of company knowledge +- Search and retrieval across unstructured text + +Example: A user asks, "What is the refund cap for late deliveries?" RAG retrieves the corresponding clause from the KB (e.g., [SLA-214]) and generates a grounded summary referencing that source. + +### 9.2 Agents with Integrated Tools + +Agents focus on reasoning and decision-making. They can call multiple tools to perform calculations, trigger actions, or make multi-step decisions. + +Typical use cases: + +- Task automation and workflow orchestration +- Conditional logic and business rule enforcement +- Multi-step decision flows requiring both reasoning and action + +Example: A user asks, "Delivery was 11 days late—if required, open a ticket and calculate compensation." The agent reasons about the conditions, uses RAG to fetch the policy, calls calc to compute the refund, and finally triggers create_ticket. + +## 10. Conclusions + +This lab marks the closing chapter of the AI Academy track, completing the journey from model foundations to production-grade applications. +In the previous sessions, we explored: + +- Lab 1: Model foundations and experiment tracking: introduced core ML concepts using MLflow to manage experiments, metrics, and artifacts, establishing reproducibility and version control as foundational practices. +- Lab 2: Training pipelines with Prefect: focused on building and orchestrating modular training flows, turning ad-hoc scripts into traceable, automatable pipelines. +- Lab 3: LoRA fine-tuning and prompt engineering: explored efficient model adaptation through LoRA and demonstrated how advanced prompting techniques can refine model behavior without retraining. +- Lab 4 (this lab): Orchestration and reasoning: integrates all previous concepts by combining retrieval-augmented generation (RAG) and agent-based reasoning, creating systems that connect knowledge and action under full observability. + +Through this progression, we moved from understanding LLMs to engineering with LLMs. +Key takeaways from this final stage: + +- Grounding is essential: RAG transforms static models into domain-aware assistants by connecting them to your organization’s data. +- Reasoning unlocks autonomy: agentic workflows powered by LangGraph and LangChain let models plan, decide, and act with explicit tool use. +- Observability ensures trust: LangSmith provides the transparency needed to debug, compare, and validate complex AI behaviors. +- Integration defines value: LLMs become business-ready when embedded within APIs, governance rules, and reproducible workflows. + +By completing this lab, you now have a full view of what it means to design, deploy, and monitor LLM-based systems that act responsibly and reliably. -* Different **citations** (same IDs, harsher content). -* Different **decisions** (smaller vouchers, stricter returns, fewer tickets). -* In **LangSmith**, compare run trees side-by-side. +The next step is not another lab, it’s applying these patterns to real projects: turning proof-of-concepts into production-grade AI services, and continuously evolving them as the ecosystem grows. diff --git a/Workshops/AI/04-Agents-and-RAG/docs/curls.md b/Workshops/AI/04-Agents-and-RAG/docs/curls.md new file mode 100644 index 0000000..53efcad --- /dev/null +++ b/Workshops/AI/04-Agents-and-RAG/docs/curls.md @@ -0,0 +1,157 @@ +### 1) Delay ≥10 business days → logistics ticket + 25% voucher (cap €50) + free shipping + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"I had an issue with my order. Delivery is 11 business days late.","thread_id":"tool-tests-01"}' | jq -r '.final_answer' +``` + +**Expected tools:** `search_policies` → `create_ticket(category="logistics")` +**Expected result:** Opens a **logistics** ticket ([TCK-511]). Grants a **25% voucher** + **free shipping credit** ([SLA-213]), **capped at €50** ([SLA-214]). +**Citations:** `[TCK-511], [SLA-213], [SLA-214]`. + +--- + +### 2) Route delayed twice within 60 days → logistics ticket + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"The same route was late twice within the last 60 days. Please open a logistics ticket and summarize next steps with sources.","thread_id":"tool-tests-02"}' | jq -r '.final_answer' +``` + +**Expected tools:** `search_policies` → `create_ticket("logistics")` +**Expected result:** Opens a **logistics** ticket for repeated route delays ([TCK-512]). +**Citations:** `[TCK-512]`. + +--- + +### 3) Carrier investigation unresolved for 3 days → logistics ticket + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"Carrier investigation is unresolved after 3 business days. Open a logistics ticket and cite the rule.","thread_id":"tool-tests-03"}' | jq -r '.final_answer' +``` + +**Expected tools:** `search_policies` → `create_ticket("logistics")` +**Expected result:** Opens a **logistics** ticket for an unresolved carrier investigation (>3 days) ([TCK-513]). +**Citations:** `[TCK-513]`. + +--- + +### 4) Confirmed loss + signature opt-out → refund/reshipment, no voucher, no ticket + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"Express order €800, customer opted out of signature, package is confirmed lost. What do we grant? Do not open a ticket unless policy requires it.","thread_id":"tool-tests-04"}' | jq -r '.final_answer' +``` + +**Expected tools:** `search_policies` (no `create_ticket`) +**Expected result:** **Refund or reshipment** ([SLA-231]); **no loss voucher** when signature was opted out ([SLA-232]); ticket optional but **not required** ([TCK-514]). +**Citations:** `[SLA-231], [SLA-232], [TCK-514]`. + +--- + +### 5) Partial return dispute on bundle → returns ticket + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"Customer disputes a partial return on a 3-item bundle with unclear promo terms. Open a returns ticket and explain the refund logic with sources.","thread_id":"tool-tests-05"}' | jq -r '.final_answer' +``` + +**Expected tools:** `search_policies` → `create_ticket("returns")` +**Expected result:** Opens a **returns** ticket ([TCK-522]); partial return allowed only for **standalone SKUs** and eligible promotions ([RET-121]); **pro-rata** refund, potential bundle discount loss ([RET-122]). +**Citations:** `[TCK-522], [RET-121], [RET-122]`. + +--- + +### 6) DOA headphones on day 7 → free label, refund/replacement, no restocking (no ticket) + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"Headphones are defective on day 7. What is the exact process and any fees? Please cite the rules.","thread_id":"tool-tests-06"}' | jq -r '.final_answer' +``` + +**Expected tools:** `search_policies` +**Expected result:** **DOA within 14 days** → free return label + **refund or replacement**, **no restocking fee** ([RET-111]). +**Citations:** `[RET-111]`. + +--- + +### 7) Open electronics on day 10 (€1,200) + 5-day delay → compute amounts + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"Opened laptop (fully working) returned on day 10, price €1,200; delivery was 5 business days late. Compute the euros for restocking and voucher (respecting caps) and give the net outcome with citations.","thread_id":"tool-tests-07"}' +``` + +**Expected tools:** `search_policies` → `calc("0.10*1200")` +**Expected result:** Restocking **10% = €120** ([RET-103]); voucher **20%** ([SLA-212]) **capped at €50** ([SLA-214]) → voucher **€50**; **net refund** = €1,200 − €120 = **€1,080**, plus voucher **€50**. +**Citations:** `[RET-103], [SLA-212], [SLA-214]`. + +--- + +### 8) Digital license activation failure on day 5 → returns ticket + refund + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"Digital license shows a server-side activation error at day 5. If required, open a returns ticket and confirm refund eligibility with sources.","thread_id":"tool-tests-08"}' | jq -r '.final_answer' +``` + +**Expected tools:** `search_policies` → `create_ticket("returns")` +**Expected result:** Opens a **returns** ticket ([TCK-531]); **refund** for **faulty activation within 7 days** ([DIG-312]); reminder: no refund after usage ([DIG-311]). +**Citations:** `[TCK-531], [DIG-312], [DIG-311]`. + +--- + +### 9) B2B pallet 3 days late, value €3,200 → compute voucher with B2B cap (€250, no ticket) + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"B2B pallet shipment arrived 3 business days late, PO value €3,200. Calculate the voucher per policy and cite the cap. Do not open a ticket unless required.","thread_id":"tool-tests-09"}' +``` + +**Expected tools:** `search_policies` → `calc("0.10*3200")` +**Expected result:** Pallet tolerance **+2 days** ([SLA-241]) → after 3 days, **10%** = **€320** ([SLA-211]) but **B2B cap €250 per PO** ([SLA-242]) → **€250**. Ticket **not required** (<10 days) ([TCK-511]). +**Citations:** `[SLA-241], [SLA-211], [SLA-242], [TCK-511]`. + +--- + +### 10) Standard delivery 5 days late, order €180 → 20% voucher (no cap exceeded), no ticket + +**curl** + +```bash +curl -s http://localhost:8000/agent/ask \ + -H "Content-Type: application/json" \ + -d '{"question":"Standard delivery was 5 business days late on an order of €180. Compute the voucher and cite the rule. Do not escalate unless required.","thread_id":"tool-tests-10"}' +``` + +**Expected tools:** `search_policies` → `calc("0.20*180")` +**Expected result:** Voucher **20% = €36** ([SLA-212]); **cap €50** not reached ([SLA-214]); no escalation (<10 days) ([TCK-511]). +**Citations:** `[SLA-212], [SLA-214], [TCK-511]`. From 5a4e46eb690328df45dd430d237af0ccc3f70585 Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 5 Nov 2025 21:42:08 +0100 Subject: [PATCH 68/70] fix policies and readme --- Workshops/AI/04-Agents-and-RAG/README.md | 115 +++++++++++------- .../data/bad_policies/00_index.md | 73 ++--------- .../data/policies/00_index.md | 73 +++++++++-- 3 files changed, 147 insertions(+), 114 deletions(-) diff --git a/Workshops/AI/04-Agents-and-RAG/README.md b/Workshops/AI/04-Agents-and-RAG/README.md index 1e85acc..2d6da03 100644 --- a/Workshops/AI/04-Agents-and-RAG/README.md +++ b/Workshops/AI/04-Agents-and-RAG/README.md @@ -1,14 +1,21 @@ # Lab: Agents & RAG with LangChain, LangGraph & LangSmith -In **Lab 3** we explored *prompt engineering patterns* and *LoRA fine-tuning* to steer and adapt LLMs. We learned that smarter prompts and light-weight fine-tuning improve response quality, but **they don’t give the model access to your private knowledge** nor the ability to **act**. +## Overview + +In **Lab 3** we explored *prompt engineering patterns* and *LoRA fine-tuning* to steer and adapt LLMs. We learned that smarter prompts and light-weight fine-tuning improve response quality, but **they don't give the model access to your Knowledge** nor the ability to **act**. **Lab 4 connects the dots**: -* We build a **RAG** (Retrieval-Augmented Generation) pipeline to ground answers in **our policy KB**. +* We build a **RAG** (Retrieval-Augmented Generation) pipeline to ground answers in **our policy Knowledge Base (KB)**. * We wrap the LLM inside a **LangGraph agent** that can **use tools** (retrieve policies, compute amounts, open tickets). -* We make executions **observable end-to-end** with **LangSmith** (mandatory): every run, step, prompt, and tool call is traceable. +* We make executions **observable end-to-end** with **LangSmith**: every run, step, prompt, and tool call is traceable. + +You will deploy a FastAPI backend with `/rag/*` and `/agent/ask`, run a curated test suite, and demonstrate how **changing only the KB** ("good" vs "bad" policies) flips decisions. -You will deploy a FastAPI backend with `/rag/*` and `/agent/ask`, run a curated test suite, and demonstrate how **changing only the KB** (“good” vs “bad” policies) flips decisions. +## Context + +Imagine you're on the escalation desk of a e-commerce platform: every day you have to manage late deliveries, defective bundles, and digital activations that fail minutes before launch. Policies change weekly, refunds depend on caps buried in PDFs, and compliance demands you cite the exact clause before touching a customer's order. +This lab puts you in control of that chaos, building an agent that can surface the right policy on demand, compute the precise euro amount, and, when escalation is mandatory, open the ticket with the same discipline a seasoned specialist would. --- @@ -16,11 +23,11 @@ You will deploy a FastAPI backend with `/rag/*` and `/agent/ask`, run a curated ### 1.1 Tools, concepts and our usage -| Tool | Why we use it | What you’ll touch | Key commands / env | | -| ----------------------------------- | --------------------------------------------------- | ----------------------------------------- | ------------------------------------------------------ | -------------- | +| Tool | Why we use it | What you'll touch | Key commands / env | +| ----------------------------------- | --------------------------------------------------- | ----------------------------------------- | ------------------------------------------------------ | | [**FastAPI**](https://fastapi.tiangolo.com/) | Expose RAG & Agent endpoints as HTTP APIs | `src/lab_service/app.py`, Pydantic models | `uvicorn src.lab_service.app:app --reload`| | | [**LangChain** ](https://docs.langchain.com/oss/python/langchain/overview) | Model wrappers, message schema, tool binding | `Chat*` wrapper, `@tool` functions | `chat_model = ... .bind_tools(TOOLS)` | | -| [**LangGraph**](https://docs.langchain.com/oss/python/langgraph/overview) | Deterministic agent control flow (LLM ↔ Tools loop) | `StateGraph`, `ToolNode`, `MemorySaver` | Graph build in `agent_graph.py` | | +| [**LangGraph**](https://docs.langchain.com/oss/python/langgraph/overview) | Deterministic agent control flow (LLM <-> Tools loop) | `StateGraph`, `ToolNode`, `MemorySaver` | Graph build in `agent_graph.py` | | | [**Chromadb**](https://docs.trychroma.com/docs/overview/introduction) | Persisted vector store for policy KB | ingest/retrieve in `rag_core.py` | `PERSIST_DIR=artifacts/chroma_*` | | | [**Sentence Transformers**](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | Text embeddings for semantic search | `EMB_MODEL` in `.env` | `EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2` | | | [**Hugging Face Inference**](https://huggingface.co/openai/gpt-oss-20b) | Hosted LLM with OpenAI-compatible API | `HF_MODEL_ID` in `.env` | `HF_MODEL_ID=openai/gpt-oss-20b` | | @@ -31,7 +38,7 @@ You will deploy a FastAPI backend with `/rag/*` and `/agent/ask`, run a curated ### 1.2 Sentence Transformers (Embeddings) -In any RAG pipeline, semantic understanding starts from good embeddings. Sentence Transformers provide pre-trained models that map text into high-dimensional vectors capturing meaning rather than exact words. This enables semantic similarity search, where “voucher limit” and “compensation cap” become close neighbors in vector space, making retrieval far more robust than keyword search. +In any RAG pipeline, semantic understanding starts from good embeddings. Sentence Transformers provide pre-trained models that map text into high-dimensional vectors capturing meaning rather than exact words. This enables semantic similarity search, where "voucher limit" and "compensation cap" become close neighbors in vector space, making retrieval far more robust than keyword search. * **Role:** Encode chunks and queries for similarity search. * **Config:** `.env: EMB_MODEL=sentence-transformers/all-MiniLM-L6-v2` @@ -40,7 +47,7 @@ In any RAG pipeline, semantic understanding starts from good embeddings. Sentenc Once texts are embedded, they need a persistent home, a place where vectors can be efficiently stored and retrieved. Chroma serves as this lightweight vector database, ideal for local labs or prototypes. It supports fast similarity queries and easy integration with LangChain, making it a natural choice for experimenting with multiple knowledge bases ("good" vs "bad" policies). -* **Role:** Persisted embeddings for semantic retrieval (per-KB isolation). +* **Role:** Persisted embeddings for semantic retrieval. * **Config:** `.env: PERSIST_DIR=data/policies` ### 1.4 Hugging Face Inference Router (LLM) @@ -60,7 +67,7 @@ LangChain acts as the glue layer between models and logic. It standardizes chat LangGraph builds on LangChain but introduces explicit orchestration: instead of a simple loop, it defines a graph-based control flow between the LLM and its tools. Each node represents a reasoning or action step, and edges define transitions based on outcomes. This deterministic structure ensures reproducibility, prevents runaway loops, and gives you full visibility into how your agent reasons and acts. -* **Role:** State machine controlling LLM ↔ Tools turns; prevents “runaway” behaviors. +* **Role:** State machine controlling LLM <-> Tools turns; prevents "runaway" behaviors. * **Key pattern:** ```python @@ -76,18 +83,18 @@ LangGraph builds on LangChain but introduces explicit orchestration: instead of Modern LLM agents do not simply call tools on demand: they reason, plan, and act. Two common reasoning strategies used in agent design are ReAct and Plan-and-Execute. -**ReAct (Reason + Act)**: This approach interleaves reasoning steps (“thoughts”) and actions in a single loop. The model reflects on the problem, chooses a tool to call, observes the result, and continues reasoning based on new information. +**ReAct (Reason + Act)**: This approach interleaves reasoning steps ("thoughts") and actions in a single loop. The model reflects on the problem, chooses a tool to call, observes the result, and continues reasoning based on new information. ```txt Example: -The user asks, “Am I eligible for a refund?” +The user asks, "Am I eligible for a refund?" -The model reasons: “I should check the refund policy first.” +The model reasons: "I should check the refund policy first." Calls the search_policies tool to find relevant clauses. -Reads the results and reasons again: “SLA-214 confirms refund allowed.” +Reads the results and reasons again: "SLA-214 confirms refund allowed." Produces the final answer, citing the relevant policy. ``` @@ -115,7 +122,7 @@ LangGraph can support both patterns, providing deterministic control over the re ### 1.6 LangSmith (Observability) -Complex agents quickly become black boxes without proper observability. LangSmith acts as the “flight recorder” of your LLM workflows, logging every prompt, response, tool call, and intermediate state. This lets you replay runs, compare versions, and debug behaviors—vital when proving that differences come from the knowledge base, not random model noise. +Complex agents quickly become black boxes without proper observability. LangSmith acts as the "flight recorder" of your LLM workflows, logging every prompt, response, tool call, and intermediate state. This lets you replay runs, compare versions, and debug behaviors-vital when proving that differences come from the knowledge base, not random model noise. * **Role:** Trace every call: prompts, tool I/O, graph edges, timings; compare runs ("good" vs "bad" KB). * **Config:** @@ -153,18 +160,18 @@ flowchart LR RG1[Ingest: /rag/ingest -> chunk and embed] RG2[Embeddings: sentence-transformers] RG3[Chroma Vector DB PERSIST_DIR] - RS1[Search: /rag/search -> top-k] - RQ1[RAG LLM: /rag/query -> answer and citations] + RS1[Search: /rag/retrieve -> top-k] + RQ1[RAG LLM: /rag/generate -> answer and citations] end A -->|/rag/ingest| R0 R0 --> RG1 --> RG2 --> RG3 - A -->|/rag/search| RS1 + A -->|/rag/retrieve| RS1 RS1 --> RG3 RS1 --> RQ1 - A -->|/rag/query| RQ1 + A -->|/rag/generate| RQ1 %% KBs K1[KB Good Policies data/policies] @@ -215,7 +222,7 @@ flowchart LR Everything begins with a client request. Through the frontend, command line, or automated test, it is sent to the FastAPI service. The service exposes two main groups of endpoints: -- `/rag/*` routes for retrieval-augmented generation, such as `/rag/ingest`, `/rag/search`, and `/rag/query`. +- `/rag/*` routes for retrieval-augmented generation, such as `/rag/ingest`, `/rag/retrieve`, and `/rag/generate`. - `/agent/ask` route for agentic reasoning and tool use. FastAPI acts as the entrypoint and router, validating payloads via Pydantic models and dispatching each request to the proper internal controller. @@ -227,7 +234,7 @@ Once a /rag/* request arrives, the system executes the retrieval pipeline: - Ingestion phase (/rag/ingest): the documents from the knowledge base (KB) are chunked and transformed into embeddings using Sentence Transformers. - Storage phase: those vectors are persisted in Chroma, a local vector store, ensuring fast and repeatable semantic queries. -- Query phase (/rag/query): when a user question arrives, the text is embedded and matched against the stored vectors to retrieve the most relevant chunks. +- Query phase (/rag/generate): when a user question arrives, the text is embedded and matched against the stored vectors to retrieve the most relevant chunks. - Answer generation: the retrieved context is then passed to the LLM (via the Hugging Face Inference endpoint), which produces a grounded answer with citations to specific policy documents. This flow ensures factual, source-based answers rather than hallucinated ones. Each RAG run can be traced later in LangSmith for inspection. @@ -252,7 +259,7 @@ All executions, RAG or Agent, are continuously observed via LangSmith. Every prompt, tool invocation, and intermediate output is logged as part of a run tree. This enables: - Step-by-step replay of agent decisions. -- Comparison of behaviors when using different knowledge bases (“good” vs “bad” policies). +- Comparison of behaviors when using different knowledge bases ("good" vs "bad" policies). - Root-cause analysis when responses differ or fail. LangSmith effectively transforms the system into a transparent and auditable workflow, where each reasoning path and retrieved chunk can be inspected. @@ -283,9 +290,9 @@ artifacts/chroma_* # Chroma persist directories per KB This lab assumes that Python is already installed, the repository kiratech/labs is accessible, and Git is properly configured on your local machine. * Python **3.12+** -* **HuggingFace** account & API key +* [**HuggingFace**](https://huggingface.co/) account & API key. Once logged in, create an access token with `read` permissions from https://huggingface.co/settings/tokens. * `curl` and `jq` -* **LangSmith** account & API key +* [**LangSmith**](https://smith.langchain.com/) account & API key. Once logged in, create an access token in your `settings/API key` section. --- @@ -293,7 +300,33 @@ This lab assumes that Python is already installed, the repository kiratech/labs As in the previous lab, in order to execute this laboratory, you will be asked to install a set of tools common in Agent engineering field. -### 5.1 Create a virtual environment +### 5.1 Clone the repository + +To start, clone the lab repository by running the following command in the terminal: + +```sh + git clone https://github.com/kiratech/labs.git +``` + +After cloning the repository, checkout the `academy-ai` branch: + +```sh + git checkout academy-ai +``` + +Then, navigate to the project folder: + +```sh + cd labs/Workshops/AI/04-Agents-and-RAG +``` + +This folder contains resources related to this lab. Now you can open it in your favorite code editor (e.g., VS Code, PyCharm) to explore the files and follow along with the exercises. For VS Code, you can run: + +```sh + code . +``` + +### 5.2 Create a virtual environment A virtual environment allows you to isolate the project's dependencies from the system-wide ones. If you have an active conda environment, please deactivate it first via `conda deactivate`. @@ -303,7 +336,7 @@ python -m venv .venv && source .venv/bin/activate You should see the prompt change to indicate that the virtual environment is active (e.g., `(.venv) user@machine:~/path/to/repo`). -### 5.2 Create and edit `.env`: +### 5.3 Create and edit `.env`: This project uses environment variables for configuration. Create a `.env` file by copying the provided example: @@ -311,7 +344,7 @@ This project uses environment variables for configuration. Create a `.env` file cp .env.example .env ``` -### 5.3 Install & run the FastAPI service: +### 5.4 Install & run the FastAPI service: Now, install the required dependencies and start the FastAPI service: @@ -322,7 +355,7 @@ uvicorn src.lab_service.app:app --reload The service will be available at `http://localhost:8000`. -### 5.4 Test the service: +### 5.5 Test the service: ```bash curl -X 'GET' \ @@ -363,12 +396,12 @@ curl -s -X POST http://localhost:8000/rag/ingest \ } ``` -You should also see the persist directory updated under your Chroma path. If `chunks` is `0` or the call fails, check that `source_dir` exists and that `.env` contains valid embedding model settings. +**What are chunks?** During ingestion each policy document is split into small, overlapping slices (usually a few hundred tokens). These chunks are embedded and stored individually so semantic search can pull back only the clauses that matter. ### 6.2 Explore search results ```bash -curl -s -X POST http://localhost:8000/rag/search \ +curl -s -X POST http://localhost:8000/rag/retrieve \ -H "Content-Type: application/json" \ -d '{"query":"voucher cap consumer","k":8}' | jq ``` @@ -386,8 +419,8 @@ curl -s -X POST http://localhost:8000/rag/search \ "query": "voucher cap consumer", "results": [ { - "text": "# Shipping & SLA (SLA-200) — ...", - "snippet": "# Shipping & SLA (SLA-200) — ... (shorter)", + "text": "# Shipping & SLA (SLA-200) - ...", + "snippet": "# Shipping & SLA (SLA-200) - ... (shorter)", "meta": { "chunk": 0, "source": "data/policies/20_shipping_sla.md" @@ -418,7 +451,7 @@ You should recognize policy IDs in `source`/`metadata` (e.g., `SLA-214`). If res ### 6.3 Ask with citations (RAG only) ```bash -curl -s -X POST http://localhost:8000/rag/query \ +curl -s -X POST http://localhost:8000/rag/generate \ -H "Content-Type: application/json" \ -d '{ "question":"What is the voucher cap for late deliveries and when does it apply?" @@ -457,7 +490,7 @@ If there are no citations, verify the KB and that your LLM endpoint is reachable This section shows how the agent (LangGraph-orchestrated) plans, calls tools (`search_policies`, `calc`, `create_ticket`), and returns a final answer. Each example explains the request, the expected tool calls, and the typical JSON shape. Keep **LangSmith** open, agent traces are particularly useful to inspect plan/act loops and tool I/O. -### 7.1 Basic “ticketing” scenario +### 7.1 Basic "ticketing" scenario ```bash curl -s -X POST http://localhost:8000/agent/ask \ @@ -521,7 +554,7 @@ curl -s -X POST http://localhost:8000/agent/ask \ * Voucher: **20% of €1,200 = €240**, **capped at €50** (`[SLA-212][SLA-214]`). * Net outcome clearly explained in the final answer with citations. -If the cap isn’t applied, open the LangSmith run to confirm `calc` input/output and that the agent read the right clause. +If the cap isn't applied, open the LangSmith run to confirm `calc` input/output and that the agent read the right clause. ### 7.3 Digital activation error scenario @@ -544,11 +577,11 @@ curl -s -X POST http://localhost:8000/agent/ask \ * If policy requires it, `create_ticket` opens a **returns** ticket (e.g., `[TCK-531]`). * Final answer references both digital policy and ticketing rule, plus any amounts. -If no ticket is created, confirm the agent’s branching logic in the LangGraph trace and that `[TCK-531]` conditions are actually met for your KB. +If no ticket is created, confirm the agent's branching logic in the LangGraph trace and that `[TCK-531]` conditions are actually met for your KB. --- -## 8. KB Swap: "Good" vs "Bad" Policies (RAG Contrast Demo) +## 8. Knowledge Base Swap: "Good" vs "Bad" Policies (RAG Contrast Demo) To demonstrate KB-driven behavior: @@ -568,7 +601,7 @@ Although Retrieval-Augmented Generation (RAG) and Agents often coexist within th ### 9.1 RAG (Retrieval-Augmented Generation) -RAG extends a language model’s knowledge by retrieving relevant information from external sources before generating an answer. It grounds the model’s output in verifiable, domain-specific data rather than relying on its static memory. +RAG extends a language model's knowledge by retrieving relevant information from external sources before generating an answer. It grounds the model's output in verifiable, domain-specific data rather than relying on its static memory. Typical use cases: @@ -588,7 +621,7 @@ Typical use cases: - Conditional logic and business rule enforcement - Multi-step decision flows requiring both reasoning and action -Example: A user asks, "Delivery was 11 days late—if required, open a ticket and calculate compensation." The agent reasons about the conditions, uses RAG to fetch the policy, calls calc to compute the refund, and finally triggers create_ticket. +Example: A user asks, "Delivery was 11 days late-if required, open a ticket and calculate compensation." The agent reasons about the conditions, uses RAG to fetch the policy, calls calc to compute the refund, and finally triggers create_ticket. ## 10. Conclusions @@ -603,11 +636,11 @@ In the previous sessions, we explored: Through this progression, we moved from understanding LLMs to engineering with LLMs. Key takeaways from this final stage: -- Grounding is essential: RAG transforms static models into domain-aware assistants by connecting them to your organization’s data. +- Grounding is essential: RAG transforms static models into domain-aware assistants by connecting them to your organization's data. - Reasoning unlocks autonomy: agentic workflows powered by LangGraph and LangChain let models plan, decide, and act with explicit tool use. - Observability ensures trust: LangSmith provides the transparency needed to debug, compare, and validate complex AI behaviors. - Integration defines value: LLMs become business-ready when embedded within APIs, governance rules, and reproducible workflows. By completing this lab, you now have a full view of what it means to design, deploy, and monitor LLM-based systems that act responsibly and reliably. -The next step is not another lab, it’s applying these patterns to real projects: turning proof-of-concepts into production-grade AI services, and continuously evolving them as the ecosystem grows. +The next step is not another lab, it's applying these patterns to real projects: turning proof-of-concepts into production-grade AI services, and continuously evolving them as the ecosystem grows. diff --git a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/00_index.md b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/00_index.md index 67e1171..b6877f5 100644 --- a/Workshops/AI/04-Agents-and-RAG/data/bad_policies/00_index.md +++ b/Workshops/AI/04-Agents-and-RAG/data/bad_policies/00_index.md @@ -1,68 +1,21 @@ -# Policy Index & Precedence (CORE-000) — v1.1 -This file is the policy index and precedence guide. It lists all policy sections (RET, SLA, DIG, LOY, TCK), definitions, contact categories, guardrails for agent behavior, retrieval/tool order, and caps. Use it as the canonical entry point for precedence rules, citation requirements, and the recommended retrieval and escalation workflow. +# Policy Index & Precedence (CORE-000) — BAD EDITION **Precedence (higher wins if there is a conflict):** -1. Legal/Regulatory Notices (if any) -2. Shipping & SLA (SLA-2xx) -3. Returns (RET-1xx) -4. Digital Products (DIG-3xx) -5. Loyalty (LOY-4xx) -6. Ticketing & Escalations (TCK-5xx) apply only as referenced by other sections +1. Shipping & SLA (SLA-2xx) — company convenience first +2. Returns (RET-1xx) — minimize refunds +3. Digital Products (DIG-3xx) — zero tolerance +4. Loyalty (LOY-4xx) — points are decorative +5. Ticketing & Escalations (TCK-5xx) — avoid tickets **Definitions** -- Business day: Monday–Friday, local public holidays excluded. -- Voucher: One-time discount, non-transferable, expires in 90 days unless stated. +- Business day: Monday–Friday, including public holidays if we feel like it. +- Voucher: A mythical object that almost never materializes. **Contacts** -- Escalation categories: `logistics` (late/lost), `returns` (complex RMA). - ---- - -## Section Map (CORE-001) -- **RET-100** — Returns & Refunds (windows, DOA, bundles, refund method) -- **SLA-200** — Shipping, Late delivery vouchers, Loss & Signature, B2B caps -- **DIG-300** — Digital products (refund rules, faulty activation, transfers, regional) -- **LOY-400** — Loyalty tiers and extensions -- **TCK-500** — Ticketing & Escalations (when to open `logistics` / `returns`) -- **EXM-900** — Worked examples (quick references) - ---- +- Escalation categories: `logistics` (rarely used), `returns` (preferably never). ## Agent Guardrails (CORE-010) -- [CORE-011] Cite policy IDs from our documents (e.g., **[RET-103]**, **[SLA-214]**) whenever a rule is applied. -- [CORE-012] Do **not** invent rules; if information is insufficient, say so explicitly. -- [CORE-013] Escalations (tickets) must follow **TCK-5xx**. Do **not** escalate only because a user asks. -- [CORE-014] When refund applies, compute **exact euros** and respect **caps**. -- [CORE-015] When voucher applies, compute **exact euros** and respect **caps**. -- [CORE-015] Refunds and vouchers amounts do not sum up. If both applies, calculate the amount for each of them. - -### Fixed Answer Format (CORE-015) -Keep English concise. - -### Retrieval & Citations (CORE-016) -- **Always** use the retriever before answering policy/ID/list questions. -- Prefer sources that match the user’s scope (e.g., `30_digital.md` for **DIG-***). -- Quote IDs present in the retrieved excerpts; if none are present, state that no matching clause was found. - -### Tool Use Order (CORE-017) -1. `search_policies` to gather the relevant clauses. -2. `calc` to compute **exact amounts** and apply caps/limits. -3. `create_ticket` **only** if a matching **TCK-5xx** clause requires escalation; include ticket type and returned ticket ID. - -### Escalation Discipline (CORE-018) -- Open **logistics** tickets per **[TCK-511..513]** conditions. -- Open **returns** tickets per **[TCK-521..523]** or **[TCK-531]** (digital faulty activation). -- **Do not** open tickets outside these clauses (**[TCK-593]**). - -### Caps & Amounts (CORE-019) -- Consumer voucher cap **[SLA-214]**; B2B cap **[SLA-242]**. -- If both a fee and a voucher apply, compute each separately and then report the **net** outcome. - -### Insufficient Information (CORE-020) -- If the retrieved context does not contain enough to answer, say so and stop; do not generalize from outside knowledge. - ---- - -## Changelog (CORE-099) -- **v1.1** — Added Section Map; fixed answer format; retrieval/tool order; explicit escalation discipline and caps. -- **v1.0** — Initial index and precedence. +- [CORE-011] Cite IDs (e.g., [RET-103], [SLA-214]) when denying benefits. +- [CORE-012] If information is insufficient, assume **customer fault**. +- [CORE-013] Tickets should be **avoided** unless legally forced (see TCK-5xx). +- [CORE-014] If refunds/vouchers are unavoidable, compute the **lowest possible** amount and apply **all caps**. diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md b/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md index b6877f5..67e1171 100644 --- a/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md @@ -1,21 +1,68 @@ -# Policy Index & Precedence (CORE-000) — BAD EDITION +# Policy Index & Precedence (CORE-000) — v1.1 +This file is the policy index and precedence guide. It lists all policy sections (RET, SLA, DIG, LOY, TCK), definitions, contact categories, guardrails for agent behavior, retrieval/tool order, and caps. Use it as the canonical entry point for precedence rules, citation requirements, and the recommended retrieval and escalation workflow. **Precedence (higher wins if there is a conflict):** -1. Shipping & SLA (SLA-2xx) — company convenience first -2. Returns (RET-1xx) — minimize refunds -3. Digital Products (DIG-3xx) — zero tolerance -4. Loyalty (LOY-4xx) — points are decorative -5. Ticketing & Escalations (TCK-5xx) — avoid tickets +1. Legal/Regulatory Notices (if any) +2. Shipping & SLA (SLA-2xx) +3. Returns (RET-1xx) +4. Digital Products (DIG-3xx) +5. Loyalty (LOY-4xx) +6. Ticketing & Escalations (TCK-5xx) apply only as referenced by other sections **Definitions** -- Business day: Monday–Friday, including public holidays if we feel like it. -- Voucher: A mythical object that almost never materializes. +- Business day: Monday–Friday, local public holidays excluded. +- Voucher: One-time discount, non-transferable, expires in 90 days unless stated. **Contacts** -- Escalation categories: `logistics` (rarely used), `returns` (preferably never). +- Escalation categories: `logistics` (late/lost), `returns` (complex RMA). + +--- + +## Section Map (CORE-001) +- **RET-100** — Returns & Refunds (windows, DOA, bundles, refund method) +- **SLA-200** — Shipping, Late delivery vouchers, Loss & Signature, B2B caps +- **DIG-300** — Digital products (refund rules, faulty activation, transfers, regional) +- **LOY-400** — Loyalty tiers and extensions +- **TCK-500** — Ticketing & Escalations (when to open `logistics` / `returns`) +- **EXM-900** — Worked examples (quick references) + +--- ## Agent Guardrails (CORE-010) -- [CORE-011] Cite IDs (e.g., [RET-103], [SLA-214]) when denying benefits. -- [CORE-012] If information is insufficient, assume **customer fault**. -- [CORE-013] Tickets should be **avoided** unless legally forced (see TCK-5xx). -- [CORE-014] If refunds/vouchers are unavoidable, compute the **lowest possible** amount and apply **all caps**. +- [CORE-011] Cite policy IDs from our documents (e.g., **[RET-103]**, **[SLA-214]**) whenever a rule is applied. +- [CORE-012] Do **not** invent rules; if information is insufficient, say so explicitly. +- [CORE-013] Escalations (tickets) must follow **TCK-5xx**. Do **not** escalate only because a user asks. +- [CORE-014] When refund applies, compute **exact euros** and respect **caps**. +- [CORE-015] When voucher applies, compute **exact euros** and respect **caps**. +- [CORE-015] Refunds and vouchers amounts do not sum up. If both applies, calculate the amount for each of them. + +### Fixed Answer Format (CORE-015) +Keep English concise. + +### Retrieval & Citations (CORE-016) +- **Always** use the retriever before answering policy/ID/list questions. +- Prefer sources that match the user’s scope (e.g., `30_digital.md` for **DIG-***). +- Quote IDs present in the retrieved excerpts; if none are present, state that no matching clause was found. + +### Tool Use Order (CORE-017) +1. `search_policies` to gather the relevant clauses. +2. `calc` to compute **exact amounts** and apply caps/limits. +3. `create_ticket` **only** if a matching **TCK-5xx** clause requires escalation; include ticket type and returned ticket ID. + +### Escalation Discipline (CORE-018) +- Open **logistics** tickets per **[TCK-511..513]** conditions. +- Open **returns** tickets per **[TCK-521..523]** or **[TCK-531]** (digital faulty activation). +- **Do not** open tickets outside these clauses (**[TCK-593]**). + +### Caps & Amounts (CORE-019) +- Consumer voucher cap **[SLA-214]**; B2B cap **[SLA-242]**. +- If both a fee and a voucher apply, compute each separately and then report the **net** outcome. + +### Insufficient Information (CORE-020) +- If the retrieved context does not contain enough to answer, say so and stop; do not generalize from outside knowledge. + +--- + +## Changelog (CORE-099) +- **v1.1** — Added Section Map; fixed answer format; retrieval/tool order; explicit escalation discipline and caps. +- **v1.0** — Initial index and precedence. From 70611d716a0d2fb16939a8a5ea6f335d13bedf6e Mon Sep 17 00:00:00 2001 From: rndg Date: Wed, 5 Nov 2025 21:59:22 +0100 Subject: [PATCH 69/70] refactor readme --- Workshops/AI/04-Agents-and-RAG/README.md | 43 ++++++++++--------- .../data/policies/00_index.md | 2 +- .../data/policies/20_shipping_sla.md | 2 +- .../data/policies/40_loyalty.md | 2 +- .../data/policies/50_ticketing.md | 2 +- 5 files changed, 26 insertions(+), 25 deletions(-) diff --git a/Workshops/AI/04-Agents-and-RAG/README.md b/Workshops/AI/04-Agents-and-RAG/README.md index 2d6da03..f85b799 100644 --- a/Workshops/AI/04-Agents-and-RAG/README.md +++ b/Workshops/AI/04-Agents-and-RAG/README.md @@ -14,7 +14,7 @@ You will deploy a FastAPI backend with `/rag/*` and `/agent/ask`, run a curated ## Context -Imagine you're on the escalation desk of a e-commerce platform: every day you have to manage late deliveries, defective bundles, and digital activations that fail minutes before launch. Policies change weekly, refunds depend on caps buried in PDFs, and compliance demands you cite the exact clause before touching a customer's order. +Imagine you're on the escalation desk of a e-commerce platform: every day you have to manage late deliveries, defective bundles, and digital activations that fail minutes before launch. Policies change weekly, refunds depend on caps buried in PDFs, and compliance demands you cite the exact clause before touching a customer's order. This lab puts you in control of that chaos, building an agent that can surface the right policy on demand, compute the precise euro amount, and, when escalation is mandatory, open the ticket with the same discipline a seasoned specialist would. --- @@ -243,7 +243,7 @@ This flow ensures factual, source-based answers rather than hallucinated ones. E When the /agent/ask endpoint is called, the request is routed to a LangGraph Agent, which combines reasoning and action: - The model receives the user query and decides whether to call one or more tools, such as: - + - search_policies to retrieve evidence from the vector database, - calc to compute compensations or voucher caps, - create_ticket to open and persist a JSON ticket in the tickets/ directory. @@ -270,7 +270,7 @@ LangSmith effectively transforms the system into a transparent and auditable wor The repository is structured as follows: -``` +```text src/lab_service/ app.py # FastAPI entrypoint for router endpoints models.py # Pydantic models of requests and responses @@ -287,9 +287,9 @@ artifacts/chroma_* # Chroma persist directories per KB ## 4. Prerequisites -This lab assumes that Python is already installed, the repository kiratech/labs is accessible, and Git is properly configured on your local machine. +This lab assumes that Python is already installed, the repository kiratech/labs is accessible, and Git is properly configured on your local machine. -* Python **3.12+** +* Python **3.12+** * [**HuggingFace**](https://huggingface.co/) account & API key. Once logged in, create an access token with `read` permissions from https://huggingface.co/settings/tokens. * `curl` and `jq` * [**LangSmith**](https://smith.langchain.com/) account & API key. Once logged in, create an access token in your `settings/API key` section. @@ -297,7 +297,7 @@ This lab assumes that Python is already installed, the repository kiratech/labs --- ## 5. Environment Setup - + As in the previous lab, in order to execute this laboratory, you will be asked to install a set of tools common in Agent engineering field. ### 5.1 Clone the repository @@ -312,19 +312,19 @@ After cloning the repository, checkout the `academy-ai` branch: ```sh git checkout academy-ai -``` +``` Then, navigate to the project folder: ```sh cd labs/Workshops/AI/04-Agents-and-RAG -``` +``` This folder contains resources related to this lab. Now you can open it in your favorite code editor (e.g., VS Code, PyCharm) to explore the files and follow along with the exercises. For VS Code, you can run: ```sh code . -``` +``` ### 5.2 Create a virtual environment @@ -367,7 +367,7 @@ curl -X 'GET' \ ## 6. Step-by-Step: Running RAG -This section walks through the RAG flow end-to-end, explaining each `curl` flag, the request/response schema, and what you should see in the service and in LangSmith traces. Endpoints are served by the FastAPI app running on `http://localhost:8000`. +This section walks through the RAG flow end-to-end, explaining each `curl` flag, the request/response schema, and what you should see in the service and in LangSmith traces. Endpoints are served by the FastAPI app running on `http://localhost:8000`. ### 6.1 Ingest the Knowledge Base @@ -396,7 +396,7 @@ curl -s -X POST http://localhost:8000/rag/ingest \ } ``` -**What are chunks?** During ingestion each policy document is split into small, overlapping slices (usually a few hundred tokens). These chunks are embedded and stored individually so semantic search can pull back only the clauses that matter. +**What are chunks?** During ingestion each policy document is split into small, overlapping slices (usually a few hundred tokens). These chunks are embedded and stored individually so semantic search can pull back only the clauses that matter. ### 6.2 Explore search results @@ -446,7 +446,7 @@ curl -s -X POST http://localhost:8000/rag/retrieve \ } ``` -You should recognize policy IDs in `source`/`metadata` (e.g., `SLA-214`). If results look irrelevant or empty, re-check embeddings, re-ingest with `reset:true`, or adjust `k`. +You should recognize policy IDs in `source`/`metadata` (e.g., `SLA-214`). If results look irrelevant or empty, re-check embeddings, re-ingest with `reset:true`, or adjust `k`. ### 6.3 Ask with citations (RAG only) @@ -481,13 +481,13 @@ curl -s -X POST http://localhost:8000/rag/generate \ } ``` -If there are no citations, verify the KB and that your LLM endpoint is reachable/configured. +If there are no citations, verify the KB and that your LLM endpoint is reachable/configured. --- ## 7. Step-by-Step: Running the Agent -This section shows how the agent (LangGraph-orchestrated) plans, calls tools (`search_policies`, `calc`, `create_ticket`), and returns a final answer. Each example explains the request, the expected tool calls, and the typical JSON shape. +This section shows how the agent (LangGraph-orchestrated) plans, calls tools (`search_policies`, `calc`, `create_ticket`), and returns a final answer. Each example explains the request, the expected tool calls, and the typical JSON shape. Keep **LangSmith** open, agent traces are particularly useful to inspect plan/act loops and tool I/O. ### 7.1 Basic "ticketing" scenario @@ -523,7 +523,7 @@ curl -s -X POST http://localhost:8000/agent/ask \ } ``` -Check the `tickets/` directory for a new file named after `ticket_id`. In LangSmith, you should see the two tool calls in the run tree. If no ticket appears, inspect the tool call arguments and policy thresholds in the trace. +Check the `tickets/` directory for a new file named after `ticket_id`. In LangSmith, you should see the two tool calls in the run tree. If no ticket appears, inspect the tool call arguments and policy thresholds in the trace. ### 7.2 "Calc + Cap" scenario @@ -554,7 +554,7 @@ curl -s -X POST http://localhost:8000/agent/ask \ * Voucher: **20% of €1,200 = €240**, **capped at €50** (`[SLA-212][SLA-214]`). * Net outcome clearly explained in the final answer with citations. -If the cap isn't applied, open the LangSmith run to confirm `calc` input/output and that the agent read the right clause. +If the cap isn't applied, open the LangSmith run to confirm `calc` input/output and that the agent read the right clause. ### 7.3 Digital activation error scenario @@ -577,7 +577,7 @@ curl -s -X POST http://localhost:8000/agent/ask \ * If policy requires it, `create_ticket` opens a **returns** ticket (e.g., `[TCK-531]`). * Final answer references both digital policy and ticketing rule, plus any amounts. -If no ticket is created, confirm the agent's branching logic in the LangGraph trace and that `[TCK-531]` conditions are actually met for your KB. +If no ticket is created, confirm the agent's branching logic in the LangGraph trace and that `[TCK-531]` conditions are actually met for your KB. --- @@ -593,7 +593,7 @@ curl -s -X POST http://localhost:8000/rag/ingest \ -d '{"source_dir":"data/bad_policies","reset":true}' ``` -2. Re-run **exactly the same** agent calls as 7.1–7.3 and compare outcomes and citations. In LangSmith, you should see different retrieved passages, decisions, and amounts. +2. Re-run **exactly the same** agent calls as 7.1–7.3 and compare outcomes and citations. In LangSmith, you should see different retrieved passages, decisions, and amounts. ## 9. RAG vs Agents: When and Why @@ -613,7 +613,8 @@ Example: A user asks, "What is the refund cap for late deliveries?" RAG retrieve ### 9.2 Agents with Integrated Tools -Agents focus on reasoning and decision-making. They can call multiple tools to perform calculations, trigger actions, or make multi-step decisions. +Agents focus on reasoning and decision-making. +They can call multiple tools to perform calculations, trigger actions, or make multi-step decisions. Typical use cases: @@ -625,7 +626,7 @@ Example: A user asks, "Delivery was 11 days late-if required, open a ticket and ## 10. Conclusions -This lab marks the closing chapter of the AI Academy track, completing the journey from model foundations to production-grade applications. +This lab marks the closing chapter of the AI Academy track, completing the journey from model foundations to production-grade applications. In the previous sessions, we explored: - Lab 1: Model foundations and experiment tracking: introduced core ML concepts using MLflow to manage experiments, metrics, and artifacts, establishing reproducibility and version control as foundational practices. @@ -633,7 +634,7 @@ In the previous sessions, we explored: - Lab 3: LoRA fine-tuning and prompt engineering: explored efficient model adaptation through LoRA and demonstrated how advanced prompting techniques can refine model behavior without retraining. - Lab 4 (this lab): Orchestration and reasoning: integrates all previous concepts by combining retrieval-augmented generation (RAG) and agent-based reasoning, creating systems that connect knowledge and action under full observability. -Through this progression, we moved from understanding LLMs to engineering with LLMs. +Through this progression, we moved from understanding LLMs to engineering with LLMs. Key takeaways from this final stage: - Grounding is essential: RAG transforms static models into domain-aware assistants by connecting them to your organization's data. diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md b/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md index 67e1171..3c7b5cf 100644 --- a/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/00_index.md @@ -55,7 +55,7 @@ Keep English concise. - **Do not** open tickets outside these clauses (**[TCK-593]**). ### Caps & Amounts (CORE-019) -- Consumer voucher cap **[SLA-214]**; B2B cap **[SLA-242]**. +- Consumer voucher cap **[SLA-214]**; B2B cap **[SLA-242]**. - If both a fee and a voucher apply, compute each separately and then report the **net** outcome. ### Insufficient Information (CORE-020) diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/20_shipping_sla.md b/Workshops/AI/04-Agents-and-RAG/data/policies/20_shipping_sla.md index 3cf39b5..9cd2bba 100644 --- a/Workshops/AI/04-Agents-and-RAG/data/policies/20_shipping_sla.md +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/20_shipping_sla.md @@ -9,7 +9,7 @@ Covers shipping, delivery SLAs, late-delivery vouchers, lost shipments, signatur - [SLA-211] ≥ 2 business days late: 10% voucher. - [SLA-212] ≥ 5 business days late: 20% voucher. - [SLA-213] ≥ 10 business days late: free shipping credit + 25% voucher. -- [SLA-214] **Voucher cap €50 per order** (consumer). +- [SLA-214] **Voucher cap €50 per order** (consumer). - [SLA-215] Customer must contact us within 14 days of delivery for a voucher claim. ## Exclusions (SLA-22x) diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/40_loyalty.md b/Workshops/AI/04-Agents-and-RAG/data/policies/40_loyalty.md index 230c4b5..ae7273c 100644 --- a/Workshops/AI/04-Agents-and-RAG/data/policies/40_loyalty.md +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/40_loyalty.md @@ -13,5 +13,5 @@ Defines loyalty program rules: tiers, benefits, eligibility, voucher issuance an - [LOY-432] Redeem 100 pts = €1; cannot pay taxes/fees with points. - [LOY-433] Points expire after 18 months of inactivity. -**Note** +**Note** [LOY-421]/[LOY-422] **extend** but do not remove other rules (e.g., restocking). diff --git a/Workshops/AI/04-Agents-and-RAG/data/policies/50_ticketing.md b/Workshops/AI/04-Agents-and-RAG/data/policies/50_ticketing.md index aaf60f6..90b124f 100644 --- a/Workshops/AI/04-Agents-and-RAG/data/policies/50_ticketing.md +++ b/Workshops/AI/04-Agents-and-RAG/data/policies/50_ticketing.md @@ -17,5 +17,5 @@ Describes ticketing and escalation procedures (TCK-5xx): when to open logistics ## Notes (TCK-59x) - [TCK-591] Do **not** open a ticket if the case is fully resolved by a straightforward policy answer and no follow-up is required. -- [TCK-592] Minimum ticket payload: `category` (`logistics` or `returns`) and a short `reason`. +- [TCK-592] Minimum ticket payload: `category` (`logistics` or `returns`) and a short `reason`. - [TCK-593] Do NOT open a ticket solely based on user request; a matching TCK-5xx condition is required. From 41309d860653714df716b4fbe24711fd0a85aae4 Mon Sep 17 00:00:00 2001 From: rndg Date: Fri, 7 Nov 2025 09:47:06 +0100 Subject: [PATCH 70/70] refactor readme --- Workshops/AI/04-Agents-and-RAG/README.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/Workshops/AI/04-Agents-and-RAG/README.md b/Workshops/AI/04-Agents-and-RAG/README.md index f85b799..bc67779 100644 --- a/Workshops/AI/04-Agents-and-RAG/README.md +++ b/Workshops/AI/04-Agents-and-RAG/README.md @@ -169,7 +169,7 @@ flowchart LR A -->|/rag/retrieve| RS1 RS1 --> RG3 - RS1 --> RQ1 + RQ1 --> RS1 A -->|/rag/generate| RQ1 @@ -210,7 +210,6 @@ flowchart LR subgraph OBS["LangSmith Tracing Runs Tool IO Graph Steps"] end - RQ1 -.->|trace| OBS G1 -.->|trace| OBS TN -.->|tool IO| OBS

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