ML Container Creator

Simplify your machine learning deployments on AWS SageMaker

Why ML Container Creator?

GitHub Pages site -- ML Container Creator

Deploying machine learning models to production shouldn't be complicated. ML Container Creator eliminates the complexity of creating SageMaker Bring Your Own Container (BYOC) deployments, letting you focus on what matters most - your models.

Perfect for:

Data scientists who want to deploy models without DevOps overhead
ML engineers building production model serving pipelines
Teams standardizing their ML deployment process
Organizations moving from prototype to production

What is SageMaker BYOC?

Amazon SageMaker Bring Your Own Container (BYOC) lets you deploy custom machine learning models using your own Docker containers. This gives you full control over your model's runtime environment while leveraging SageMaker's managed infrastructure for hosting and scaling.

📋 What You Get with ml-container-creator

Every generated project includes:

SageMaker-compatible container with health checks and invocation endpoints
Multiple deployment options - Direct SageMaker deployment or AWS CodeBuild for CI/CD
Local testing suite to validate before deployment
Sample model and training code to illustrate the deployment
AWS deployment scripts for ECR and SageMaker
Multi-framework support (sklearn, XGBoost, TensorFlow, vLLM, SGLang, TensorRT-LLM)
🆕 Intelligent configuration system with community-validated settings
- Framework Registry: Pre-configured settings for framework versions (vLLM, SGLang, TensorRT-LLM, LMI, DJL)
- Model Registry: Model-specific optimizations and overrides
- HuggingFace Hub integration: Automatic model metadata fetching
🆕 Accelerator compatibility validation (CUDA, Neuron SDK, CPU, ROCm)
- Automatic instance type validation against framework requirements
- Version compatibility checking for accelerator drivers
- Clear error messages with recommended alternatives
🆕 Configuration profiles for different optimization strategies
- Low-latency vs high-throughput profiles
- Single-GPU vs multi-GPU configurations
- Framework and model-specific optimizations
🆕 Environment variable validation to catch configuration errors early
- Known flags validation against framework specifications
- Type and range constraint checking
- Deprecated flag warnings with suggested replacements

Note: This tool generates starter code. Review and customize for your production requirements.

Prerequisites

Before you begin, ensure you have:

Node.js v24+ (required for development and testing)
- Check version: node --version
- Use nvm: nvm use node to switch to latest version
An AWS account with SageMaker access
Basic familiarity with Docker and command line
A trained machine learning model ready for deployment

🚀 Deployment Options

ML Container Creator supports two deployment approaches to fit different workflows:

🏗️ CodeBuild CI/CD Pipeline (Recommended)

Enterprise-ready CI/CD with AWS CodeBuild:

Automated Docker image building in AWS with correct architecture
Shared ECR repository with project-specific tagging
No local architecture compatibility issues
Integrated with AWS infrastructure
Perfect for production and team environments

# Generate project with CodeBuild deployment
yo ml-container-creator my-model --deploy-target=codebuild --skip-prompts

# Submit build job and deploy
cd my-model
./deploy/submit_build.sh  # Builds image in CodeBuild
./deploy/deploy.sh your-sagemaker-role-arn  # Deploys to SageMaker

🎯 Direct SageMaker Deployment

Quick local builds for development:

Build and push Docker image locally
Deploy directly to SageMaker endpoint
Ideal for prototyping and development environments
⚠️ Note: May encounter architecture compatibility issues (e.g., ARM64 Mac → x86_64 SageMaker)

# Generate project with SageMaker deployment
yo ml-container-creator my-model --deploy-target=sagemaker --skip-prompts

# Deploy to SageMaker
cd my-model
./deploy/deploy.sh your-sagemaker-role-arn

🔄 CodeBuild Features

Shared ECR Repository: All projects use ml-container-creator repository with project-specific tags
Automatic Infrastructure: Creates CodeBuild projects, IAM roles, and S3 buckets automatically
Build Monitoring: Real-time build status and progress tracking
Compute Options: Small, Medium, or Large compute types for different project sizes
Comprehensive Logging: CloudWatch integration for build logs and debugging

🎯 Intelligent Configuration System

ML Container Creator includes a powerful multi-registry configuration system that provides:

Framework Registry

Pre-configured, tested settings for transformer serving frameworks:

Base images and environment variables for each framework version
Accelerator requirements (CUDA versions, Neuron SDK versions)
SageMaker AMI versions matched to accelerator requirements
Recommended instance types for optimal performance
Validation levels indicating how well-tested each configuration is

Model Registry

Model-specific optimizations and overrides:

Chat templates for conversational models
Framework compatibility information
Known issues and workarounds
Pattern matching for model families (e.g., mistral* matches all Mistral variants)

HuggingFace Hub Integration

Automatic fetching of model metadata:

Tokenizer configurations including chat templates
Model architectures and requirements
Graceful fallback when API is unavailable
Offline mode for air-gapped environments

Configuration Profiles

Choose optimization strategies for your use case:

Low-latency profiles: Optimized for single-request response time
High-throughput profiles: Optimized for batch processing
Single-GPU vs multi-GPU: Different tensor parallelism settings
Framework-specific profiles: vLLM, TensorRT-LLM, SGLang optimizations

Accelerator Compatibility Validation

Automatic validation of hardware compatibility:

Type matching: Ensures framework accelerator type (CUDA/Neuron/CPU/ROCm) matches instance
Version checking: Validates accelerator driver versions
AMI compatibility: Verifies SageMaker AMI provides required drivers
Clear recommendations: Suggests compatible instance types when mismatches detected

Environment Variable Validation

Catch configuration errors before deployment:

Known flags validation: Checks against framework specifications
Type constraints: Validates integer, float, string, boolean values
Range constraints: Enforces min/max values
Deprecation warnings: Alerts about deprecated flags with suggested replacements
Opt-in validation: Enable with --validate-env-vars flag (enabled by default)

Community Contributions

Share your tested configurations:

Export working configurations: After successful deployment
Validation levels: Experimental → Community-validated → Tested
Test reports: Document successful deployments on specific instance types
Registry contributions: Submit configurations via pull requests

How It Works

User Input → Framework Registry Lookup → Profile Selection (optional)
                    ↓
         Model Registry Lookup (optional)
                    ↓
         HuggingFace API Fetch (optional)
                    ↓
         Configuration Merge (priority order)
                    ↓
         Accelerator Validation
                    ↓
         Environment Variable Validation
                    ↓
         Template Generation

Configuration Priority Order:

Base framework configuration (Framework Registry)
Framework profile (if selected)
HuggingFace API data (if available)
Model registry overrides (if available)
Model profile (if selected)

Graceful Degradation: When registry data is unavailable, the generator falls back to current default behavior - ensuring backward compatibility and reliability.

🚀 Get Started in Minutes

# Install Yeoman and the generator
cd ml-container-creator
npm install -g yo
npm link

# Generate your project
yo ml-container-creator

Answer a few questions about your model, and get a complete container with:

Optimized model serving (Flask or FastAPI)
Built-in testing and deployment scripts
Support for SageMaker AI managed endpoint hosting

⚙️ Configuration Options

ML Container Creator supports multiple ways to configure your project, from interactive prompts to fully automated CLI usage. Choose the method that best fits your workflow.

Configuration Methods (by precedence)

CLI Options (highest precedence)
CLI Arguments
Environment Variables
CLI Config File (--config=file.json)
Custom Config File (ml-container.config.json)
Package.json Section ("ml-container-creator": {...})
Generator Defaults
Interactive Prompts (lowest precedence)

Quick Start Examples

Interactive Mode (Default)

yo ml-container-creator
# Follow the prompts to configure your project

CLI Mode (Skip Prompts)

# Basic sklearn project with SageMaker deployment
yo ml-container-creator my-sklearn-project \
  --framework=sklearn \
  --model-server=flask \
  --model-format=pkl \
  --deploy-target=sagemaker \
  --skip-prompts

# Transformers project with vLLM and CodeBuild CI/CD
yo ml-container-creator my-llm-project \
  --framework=transformers \
  --model-server=vllm \
  --instance-type=gpu-enabled \
  --deploy-target=codebuild \
  --codebuild-compute-type=BUILD_GENERAL1_MEDIUM \
  --skip-prompts

# XGBoost with FastAPI and testing
yo ml-container-creator my-xgb-project \
  --framework=xgboost \
  --model-server=fastapi \
  --model-format=json \
  --include-testing \
  --deploy-target=sagemaker \
  --skip-prompts

Environment Variables

# Set configuration via environment (only supported parameters)
export ML_INSTANCE_TYPE="cpu-optimized"
export AWS_REGION="us-east-1"
export AWS_ROLE="arn:aws:iam::123456789012:role/SageMakerRole"

# Generate with environment config + CLI options for core parameters
yo ml-container-creator --framework=sklearn --model-server=flask --model-format=pkl --skip-prompts

Configuration File

# Create configuration file
yo ml-container-creator configure

# Or generate empty config
yo ml-container-creator generate-empty-config

# Use configuration file
yo ml-container-creator --config=production.json --skip-prompts

CLI Options Reference

Option	Description	Values
`--skip-prompts`	Skip interactive prompts	`true/false`
`--config=<file>`	Load configuration from file	File path
`--project-name=<name>`	Project name	String
`--framework=<framework>`	ML framework	`sklearn`, `xgboost`, `tensorflow`, `transformers`
`--model-server=<server>`	Model server	`flask`, `fastapi`, `vllm`, `sglang`, `tensorrt-llm`
`--model-format=<format>`	Model format	Depends on framework
`--include-sample`	Include sample model code	`true/false`
`--include-testing`	Include test suite	`true/false`
`--test-types=<types>`	Test types (comma-separated)	`local-model-cli`, `local-model-server`, `hosted-model-endpoint`
`--deploy-target=<target>`	Deployment target	`sagemaker`, `codebuild`
`--codebuild-compute-type=<type>`	CodeBuild compute type	`BUILD_GENERAL1_SMALL`, `BUILD_GENERAL1_MEDIUM`, `BUILD_GENERAL1_LARGE`
`--instance-type=<type>`	Instance type	`cpu-optimized`, `gpu-enabled`
`--region=<region>`	AWS region	`us-east-1`, etc.
`--role-arn=<arn>`	AWS IAM role ARN	`arn:aws:iam::123456789012:role/SageMakerRole`
`--project-dir=<dir>`	Output directory path	`./my-project`
`--hf-token=<token>`	HuggingFace authentication token	`hf_abc123...` or `$HF_TOKEN`
`--validate-env-vars`	Enable environment variable validation	`true/false` (default: `true`)
`--validate-with-docker`	Enable Docker introspection validation	`true/false` (default: `false`)
`--offline`	Skip HuggingFace API lookups	`true/false` (default: `false`)

Environment Variables Reference

Note: According to the parameter matrix specification, only the following environment variables are supported. Core parameters (framework, model-server, etc.) must be configured via CLI options or configuration files.

Variable	Description	Example
`ML_INSTANCE_TYPE`	Instance type	`cpu-optimized`
`AWS_REGION`	AWS region	`us-east-1`
`AWS_ROLE`	AWS IAM role ARN	`arn:aws:iam::123456789012:role/SageMakerRole`
`ML_CONTAINER_CREATOR_CONFIG`	Config file path	`./my-config.json`

Configuration File Examples

Custom Config File (`ml-container.config.json`)

{
  "projectName": "my-ml-project",
  "framework": "sklearn",
  "modelServer": "flask",
  "modelFormat": "pkl",
  "includeSampleModel": false,
  "includeTesting": true,
  "testTypes": ["local-model-cli", "hosted-model-endpoint"],
  "deployTarget": "sagemaker",
  "instanceType": "cpu-optimized",
  "awsRegion": "us-east-1"
}

Package.json Section

{
  "name": "my-project",
  "ml-container-creator": {
    "projectName": "my-ml-project",
    "framework": "transformers",
    "modelServer": "vllm",
    "instanceType": "gpu-enabled",
    "includeTesting": true
  }
}

CLI Commands

Command	Description
`yo ml-container-creator`	Interactive generation
`yo ml-container-creator configure`	Interactive configuration setup
`yo ml-container-creator generate-empty-config`	Generate empty config file
`yo ml-container-creator help`	Show help information

Framework-Specific Options

Traditional ML (sklearn, xgboost, tensorflow)

Model Servers: flask, fastapi
Model Formats: Varies by framework
Sample Model: Available (Abalone classifier)
Instance Types: cpu-optimized, gpu-enabled

Transformers (LLMs)

Model Servers: vllm, sglang, tensorrt-llm
Model Formats: Not applicable (loaded from Hugging Face Hub)
Sample Model: Not available
Instance Types: gpu-enabled (defaults to ml.g6.12xlarge)
Note: TensorRT-LLM requires NVIDIA NGC authentication to pull the base image

Example: Deploy a scikit-learn Model

Prepare your model: Save as model.pkl
Generate container: Run yo and choose ml-container-creator
Configure: Choose sklearn → pkl → flask → deployment target (SageMaker or CodeBuild)
Deploy:
- SageMaker: Run ./deploy/deploy.sh your-sagemaker-role-arn
- CodeBuild: Run ./deploy/submit_build.sh then ./deploy/deploy.sh your-sagemaker-role-arn

🔐 HuggingFace Authentication

When is Authentication Needed?

HuggingFace authentication is required for:

Private models: Models in private repositories
Gated models: Models requiring user agreement (e.g., Llama 2, Llama 3)
Rate-limited access: Avoiding rate limits on public models

Public models like openai/gpt-oss-20b do not require authentication.

Providing Your HF_TOKEN

When you manually enter a transformer model ID (not selecting from examples), you'll be prompted for authentication:

Option 1: Interactive Prompt (Recommended for Local Development)

🔐 HuggingFace Authentication
⚠️  Security Note: The token will be baked into the Docker image.
   For CI/CD, consider using "$HF_TOKEN" to reference an environment variable.

? HuggingFace token (enter token, "$HF_TOKEN" for env var, or leave empty):

You can:

Enter your token directly: hf_abc123...
Reference an environment variable: $HF_TOKEN
Leave empty for public models: (press Enter)

Option 2: CLI Option

# Direct token
yo ml-container-creator my-llm-project \
  --framework=transformers \
  --model-name=meta-llama/Llama-2-7b-hf \
  --model-server=vllm \
  --hf-token=hf_abc123... \
  --skip-prompts

# Environment variable reference
yo ml-container-creator my-llm-project \
  --framework=transformers \
  --model-name=meta-llama/Llama-2-7b-hf \
  --model-server=vllm \
  --hf-token='$HF_TOKEN' \
  --skip-prompts

Option 3: Configuration File

{
  "framework": "transformers",
  "modelName": "meta-llama/Llama-2-7b-hf",
  "modelServer": "vllm",
  "hfToken": "$HF_TOKEN"
}

Security Best Practices

⚠️ Important Security Considerations:

Tokens are baked into the image: Anyone with access to your Docker image can extract the token using docker inspect.

Use environment variable references for CI/CD:

export HF_TOKEN=hf_your_token_here
yo ml-container-creator --framework=transformers --hf-token='$HF_TOKEN' --skip-prompts

Never commit tokens to version control: Use $HF_TOKEN in config files, not actual tokens.
Rotate tokens regularly: Generate new tokens periodically from your HuggingFace account.

TensorRT-LLM Authentication

TensorRT-LLM uses NVIDIA's NGC (NVIDIA GPU Cloud) registry, which requires authentication:

Before building a TensorRT-LLM container:

Create an NGC account: Visit https://ngc.nvidia.com/signup
Generate an API key:
- Go to https://ngc.nvidia.com/setup/api-key
- Click "Generate API Key"
- Save your API key securely
Set NGC_API_KEY environment variable:
```
export NGC_API_KEY='your-api-key-here'
```

Build and deploy:

For SageMaker deployment (local build):

cd deploy
./build_and_push.sh  # Automatically authenticates with NGC using NGC_API_KEY

For CodeBuild deployment (CI/CD):

cd deploy
./submit_build.sh    # Passes NGC_API_KEY to CodeBuild

How it works:

The build scripts automatically authenticate with NGC using your NGC_API_KEY environment variable
For local builds (build_and_push.sh), Docker login happens on your machine
For CodeBuild (submit_build.sh), the NGC_API_KEY is passed as a CodeBuild environment variable
No manual docker login required!

Security Note for CodeBuild:

NGC_API_KEY is passed as a plaintext environment variable to CodeBuild

For production, consider using AWS Secrets Manager:

# Store in Secrets Manager
aws secretsmanager create-secret --name ngc-api-key --secret-string "$NGC_API_KEY"

# Update buildspec to retrieve from Secrets Manager
# See AWS CodeBuild documentation for details

Note: NGC authentication is only required for TensorRT-LLM. vLLM and SGLang use publicly available images.

Getting Your HF_TOKEN

Go to https://huggingface.co/settings/tokens
Click "New token"
Give it a descriptive name (e.g., "sagemaker-deployment")
Select "Read" access
Copy the token (starts with hf_)

Troubleshooting Authentication

Error: "Repository not found" or "Access denied"

Verify your token is valid and not expired
Ensure you've accepted the model's license agreement on HuggingFace
Check that your token has access to the model's organization

Error: "HF_TOKEN environment variable not set"

You specified $HF_TOKEN but the environment variable is not set
Set it: export HF_TOKEN=hf_your_token_here
Or provide the token directly instead of using $HF_TOKEN

Container builds but fails at runtime

The model requires authentication but no token was provided
Rebuild with --hf-token option

For more authentication troubleshooting, see the Troubleshooting Guide.

🛠️ Requirements

For Users

Node.js 24+
Python 3.8+
Docker 20+
AWS CLI 2+

For Contributors

Node.js 24+ and npm
All other tools managed via npm scripts

🌟 Open Source & Community Driven

ML Container Creator is open source under the Apache 2.0 license.

🗺️ Live Roadmap

Our development is driven by user needs. Check out our live roadmap to:

See what's coming next
Vote on features you need
Contribute ideas and feedback
Track progress on requested features

Current priorities:

Enhanced transformer model support
Multi-model endpoints
Auto-scaling configurations
Cost optimization features

🧪 Testing

ML Container Creator has a comprehensive test suite ensuring reliability and correctness across all supported configurations.

Test Architecture

Our testing approach combines multiple testing strategies for comprehensive coverage:

📋 Unit Tests (87 tests)

Focused tests for specific functionality organized by capability:

CLI Options - Command-line option parsing and validation
Environment Variables - Environment variable handling and precedence
Configuration Files - JSON config files and package.json sections
Configuration Precedence - Multi-source configuration merging
File Generation - Template processing and conditional file creation
Error Handling - Validation errors and edge cases

🔬 Property-Based Tests (10 universal properties)

Automated testing of universal correctness properties using fast-check:

Parameter Source Enforcement - Unsupported sources are ignored
Environment Variable Mapping - Correct variable-to-parameter mapping
CLI Option Consistency - CLI options accepted and mapped correctly
Package.json Filtering - Only supported parameters loaded from package.json
Default Value Application - Correct defaults applied when values missing
Configuration Isolation - .yo-rc.json files completely ignored
Non-Promptable Handling - Non-interactive parameters handled correctly
Required Parameter Validation - Missing required parameters produce errors
Config File Resolution - Environment variable config paths work correctly
Parameter Precedence - Highest precedence source values used

🔒 Security Testing

Dependency Scanning - npm audit for known vulnerabilities
Code Quality - ESLint for security best practices
Input Validation - Malformed configuration handling

Running Tests

# Complete validation (recommended before PR)
npm run validate                # ESLint + Security + All Tests

# Individual test categories
npm test                        # Unit tests only
npm run test:property          # Property-based tests only
npm run test:all               # Unit + Property tests
npm run test:coverage          # Tests with coverage report

# Development workflows
npm run test:watch             # Unit tests in watch mode
npm run test:property:watch    # Property tests in watch mode

# Specific test targeting
npm test -- --grep "CLI Options"     # CLI-related tests
npm test -- --grep "sklearn"         # Framework-specific tests
npm test -- --grep "precedence"      # Configuration precedence tests

Test Features

🎯 Comprehensive Coverage

1000+ test iterations across all parameter combinations
All configuration sources tested (CLI, env vars, config files, package.json)
All frameworks tested (sklearn, xgboost, tensorflow, transformers)
All precedence scenarios validated

📊 Detailed Reporting

🧪 Test #1: should parse sklearn CLI options correctly
📍 Test Suite: CLI Options Parsing
🔍 Checking 6 expected files for sklearn CLI parsing...
✅ Found: Dockerfile
✅ Found: requirements.txt
📊 Summary: All 6 expected files found

🔍 Debug Information

When tests fail, detailed context is provided:

🔍 DEBUG: Current state for test #5 failure:
📁 Working directory: /tmp/test-dir
📄 Files in current directory (3 total): [Dockerfile, requirements.txt, ...]

⚡ Performance Optimized

Smart test generation - Only valid parameter combinations tested
Configurable timeouts - Prevent hanging tests
Efficient validation - Early returns for known invalid states
Minimal logging during property execution

Test Results

Current test status:

✅ 87 unit tests passing - All functionality validated
✅ 10 property tests passing - Universal correctness verified
✅ 100% success rate - Zero failing tests
✅ ~6 second execution - Fast feedback cycle
✅ 1000+ iterations - Comprehensive coverage

For Contributors

When adding new features, include appropriate tests:

Choose the right test module based on functionality
Follow existing patterns for consistency
Test both success and failure cases
Add property tests for universal behavior
Run npm run validate before submitting PR

See CONTRIBUTING.md for detailed testing guidelines.

Test Philosophy

Our testing approach ensures reliability through multiple complementary strategies:

🎯 Focused Unit Tests

Each test module focuses on a specific capability (CLI options, environment variables, configuration files, etc.), making it easy to understand what's being tested and debug failures.

🔬 Property-Based Testing

Using fast-check, we test universal correctness properties across thousands of parameter combinations, ensuring the system behaves correctly for all valid inputs.

🔒 Security & Quality

Automated security auditing and code quality checks ensure the generated containers and deployment scripts follow best practices.

📊 Comprehensive Coverage

87 unit tests covering specific functionality
10 property tests validating universal behavior
1000+ test iterations across all parameter combinations
Multi-Node.js version testing ensuring compatibility

⚡ Fast Feedback

Tests complete in ~6 seconds, providing rapid feedback during development while maintaining comprehensive coverage.

🤝 Contributing

We welcome contributions of all sizes! Whether you're:

Reporting bugs or requesting features
Improving documentation
Adding support for new frameworks
Optimizing performance

Your input shapes the future of this tool.

Development Setup

Quick contribution setup:

# Clone and setup
git clone https://github.com/awslabs/ml-container-creator
cd ml-container-creator

# Ensure you're using Node.js 24+
nvm use node  # or: nvm install 24

# Install dependencies and link for local development
npm install
npm link

# Run tests
npm test

# Run linting
npm run lint

# Make your changes and submit a PR!

Continuous Integration

All pull requests are automatically tested with our comprehensive CI pipeline:

🔄 Automated Testing

Multi-Node Testing: Tests run on Node.js 24.x and 22.x
Complete Test Suite: Unit tests + property-based tests + security audit
Code Quality: ESLint checks for code standards
Security Scanning: npm audit for known vulnerabilities
Coverage Reporting: Test coverage analysis and reporting

📋 CI Pipeline Stages

Test Suite - Runs on multiple Node.js versions
- ESLint code quality checks
- Security audit (npm audit)
- Unit tests (87 tests)
- Property-based tests (10 universal properties)
- Test coverage generation
Full Validation - Complete end-to-end testing
- Full validation suite (npm run validate)
- Generator installation testing
- CLI functionality verification
- File generation validation
Security Scan - Security vulnerability assessment
- Dependency vulnerability scanning
- High/critical vulnerability blocking
- Security best practices validation

✅ PR Requirements

Before your PR can be merged, it must:

✅ Pass all tests on supported Node.js versions
✅ Pass ESLint code quality checks
✅ Pass security audit (no high/critical vulnerabilities)
✅ Maintain or improve test coverage
✅ Successfully generate containers with CLI options

🚀 Local Validation

Run the same checks locally before submitting:

# Run the complete validation suite (same as CI)
npm run validate

# Test generator functionality
npm link
yo ml-container-creator test-project --framework=sklearn --model-server=flask --model-format=pkl --skip-prompts

Running Tests

The project has a comprehensive test suite with multiple types of tests:

# Run all tests (unit + property-based + security audit)
npm run validate

# Run unit tests only
npm test

# Run property-based tests only
npm run test:property

# Run all tests without security audit
npm run test:all

# Development workflows
npm run test:watch          # Watch mode for unit tests
npm run test:property:watch # Watch mode for property tests
npm run test:coverage       # Run with coverage report

Test Organization

Our test suite is organized into focused modules:

📋 Unit Tests (test/input-parsing-and-generation/)

CLI Options: cli-options.test.js - CLI option parsing and validation
Environment Variables: environment-variables.test.js - Environment variable handling
Configuration Files: configuration-files.test.js - Config file parsing (JSON, package.json)
Configuration Precedence: configuration-precedence.test.js - Multi-source precedence testing
File Generation: file-generation.test.js - Template processing and file creation
Error Handling: error-handling.test.js - Validation and error scenarios

🔬 Property-Based Tests (test/input-parsing-and-generation/parameter-matrix-compliance.property.test.js)

Parameter Matrix Compliance: 10 universal correctness properties
Comprehensive Coverage: 1000+ test iterations across all parameter combinations
Automated Validation: Tests all configuration sources and precedence rules

Test Features

87 passing tests with comprehensive coverage
Property-based testing using fast-check for universal correctness validation
Detailed progress reporting with numbered tests and clear output
Debug information when tests fail
Modular structure for easy maintenance and contribution

Running Specific Tests

# Run specific test categories
npm test -- --grep "CLI Options"        # CLI option tests
npm test -- --grep "Environment"        # Environment variable tests
npm test -- --grep "precedence"         # Configuration precedence tests
npm test -- --grep "sklearn"            # Framework-specific tests

# Run property tests for specific properties
npm run test:property -- --grep "Property 1"  # Parameter source enforcement
npm run test:property -- --grep "Property 10" # Parameter precedence order

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.

📚 Documentation

📖 Full Documentation Site - Complete guides, examples, and API reference

Quick Links

📖 Getting Started - Installation and first project tutorial
📖 Examples Guide - Step-by-step examples for common use cases including CodeBuild CI/CD
🔧 Troubleshooting Guide - Solutions to common issues
🎯 Template System - How the template system works
🏗️ Architecture - Complete architecture guide

Release Documentation

📋 v1.0.0 Release Notes - Complete feature descriptions and testing commands
📊 v1.0.0 Release Summary - High-level overview and statistics
⚡ Quick Test Guide - Fast validation for reviewers

For Contributors

🚀 Contributing Guide - Get started contributing in 5 minutes
🛠️ Adding Features - Guide for adding new frameworks
📝 Coding Standards - Code style and conventions
☁️ AWS/SageMaker Guide - Domain knowledge and best practices

Examples

🆘 Support & Troubleshooting

Get Help

📖 Examples Guide - Detailed walkthroughs
🔧 Troubleshooting Guide - Common issues and solutions
🔧 Advanced Troubleshooting - Development and CI issues
🐛 Report Issues
💬 Community Discussions
🗺️ Roadmap & Feature Requests
📖 SageMaker Documentation

Quick Troubleshooting

Container fails to start

# Check logs
docker logs your-container-name

# See detailed solutions in troubleshooting guide

SageMaker deployment fails

# Check CloudWatch logs
aws logs tail /aws/sagemaker/Endpoints/your-endpoint --follow

# See detailed solutions in troubleshooting guide

Need more help? Check the troubleshooting guide for common issues and solutions.

Made with ❤️ by the ML community, for the ML community

Name		Name	Last commit message	Last commit date
Latest commit History 101 Commits
.github		.github
config		config
docs		docs
generators/app		generators/app
scripts		scripts
test		test
.eslintrc.cjs		.eslintrc.cjs
.gitignore		.gitignore
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
LICENSE-THIRD-PARTY		LICENSE-THIRD-PARTY
NOTICE		NOTICE
README.md		README.md
THIRD_PARTY.csv		THIRD_PARTY.csv
TROUBLESHOOTING.md		TROUBLESHOOTING.md
licenses.csv		licenses.csv
logo.png		logo.png
mkdocs.yml		mkdocs.yml
package-lock.json		package-lock.json
package.json		package.json
sbom.json		sbom.json

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awslabs/ml-container-creator

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ML Container Creator

Why ML Container Creator?

What is SageMaker BYOC?

📋 What You Get with ml-container-creator

Prerequisites

🚀 Deployment Options

🏗️ CodeBuild CI/CD Pipeline (Recommended)

🎯 Direct SageMaker Deployment

🔄 CodeBuild Features

🎯 Intelligent Configuration System

Framework Registry

Model Registry

HuggingFace Hub Integration

Configuration Profiles

Accelerator Compatibility Validation

Environment Variable Validation

Community Contributions

How It Works

🚀 Get Started in Minutes

⚙️ Configuration Options

Configuration Methods (by precedence)

Quick Start Examples

Interactive Mode (Default)

CLI Mode (Skip Prompts)

Environment Variables

Configuration File

CLI Options Reference

Environment Variables Reference

Configuration File Examples

Custom Config File (ml-container.config.json)

Package.json Section

CLI Commands

Framework-Specific Options

Traditional ML (sklearn, xgboost, tensorflow)

Transformers (LLMs)

Example: Deploy a scikit-learn Model

🔐 HuggingFace Authentication

When is Authentication Needed?

Providing Your HF_TOKEN

Option 1: Interactive Prompt (Recommended for Local Development)

Option 2: CLI Option

Option 3: Configuration File

Security Best Practices

TensorRT-LLM Authentication

Getting Your HF_TOKEN

Troubleshooting Authentication

🛠️ Requirements

For Users

For Contributors

🌟 Open Source & Community Driven

🗺️ Live Roadmap

🧪 Testing

Test Architecture

📋 Unit Tests (87 tests)

🔬 Property-Based Tests (10 universal properties)

🔒 Security Testing

Running Tests

Test Features

🎯 Comprehensive Coverage

📊 Detailed Reporting

🔍 Debug Information

⚡ Performance Optimized

Test Results

For Contributors

Test Philosophy

🎯 Focused Unit Tests

🔬 Property-Based Testing

🔒 Security & Quality

📊 Comprehensive Coverage

⚡ Fast Feedback

🤝 Contributing

Development Setup

Continuous Integration

Custom Config File (`ml-container.config.json`)

Packages