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PyMLpipe is a Python library for ease Machine Learning Model monitering and Deployment.
- Simple
- Intuative
- Easy to use
Use the package manager pip to install PyMLpipe.
pip install pymlpipeor
pip3 install pymlpipefrom sklearn.datasets import load_iris
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score
#import PyMLPipe from tabular
from pymlpipe.tabular import PyMLPipe
# Initiate the class
mlp=PyMLPipe()
# Set experiment name
mlp.set_experiment("IrisDataV2")
# Set Version name
mlp.set_version(0.2)
iris_data=load_iris()
data=iris_data["data"]
target=iris_data["target"]
df=pd.DataFrame(data,columns=iris_data["feature_names"])
trainx,testx,trainy,testy=train_test_split(df,target)
# to start monitering use mlp.run()
with mlp.run():
# set tags
mlp.set_tags(["Classification","test run","logisticRegression"])
model=LogisticRegression()
model.fit(trainx, trainy)
predictions=model.predict(testx)
# log performace metrics
mlp.log_matric("Accuracy", accuracy_score(testy,predictions))
mlp.log_matric("Precision", precision_score(testy,predictions,average='macro'))
mlp.log_matric("Recall", recall_score(testy,predictions,average='macro'))
mlp.log_matric("F1", f1_score(testy,predictions,average='macro'))
# Save train data and test data
mlp.register_artifact("train", trainx)
mlp.register_artifact("test", testx,artifact_type="testing")
# Save the model
mlp.scikit_learn.register_model("logistic regression", model)To start the UI
pymlpipeui or
from pymlpipe.pymlpipeUI import start_ui
start_ui(host='0.0.0.0', port=8085)
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.