Mlcbakery

🚀 MLC Bakery

A Python-based service for managing the lineage and pedigree of ML models, built with FastAPI and SQLAlchemy.

🚀 Quick Start

MLC Bakery is a Python-based service designed to manage the lineage and pedigree of machine learning models. Built with FastAPI and SQLAlchemy, it offers RESTful APIs and a web interface for viewing and managing ML model lineage information.

✨ Features

• RESTful API: Provides Create, Read, Update, and Delete (CRUD) operations to manage ML models.
• Web Interface: Offers an intuitive web interface via Streamlit to view and manage model lineage information.
• Docker Support: Comes with a Docker Compose file for easy deployment in a production environment.
• Authentication and Authorization: Uses JWT for authentication to ensure API security.

📦 Installation

Quick Start

1. Clone the repository:

   git clone https://github.com/yourusername/mlc-bakery.git
   cd mlt-bakery
   

2. Create a virtual environment and install dependencies:

   python -m venv env
   source env/bin/activate  # Use `env\Scripts\activate` for Windows
   pip install -r requirements.txt
   

3. Configure environment variables: Create a .env file and add the following content:

   DB_URL=postgresql://postgres:password@localhost/mlc_bakery
   ADMIN_AUTH_TOKEN=your_secure_token_here
   

4. Start the service:

   uvicorn app.main:app --reload
   

5. Access the web interface: Open your browser and visit http://localhost:8000.

Database Migration

Use Alembic for database migration:

1. Initialize Alembic:

   alembic init migrations
   

2. Add models to the migration file: Modify the migrations/versions/[timestamp]_[name].py file and add your ML models to the ml_models table.

3. Commit the migration:

   alembic revision --autogenerate -m "add ml models"
   alembic upgrade head
   

Authentication and Authorization

1. Create a JWT encryption key: Add the following to the .env file:

   SECRET_KEY=your_jwt_secret_key_here
   

2. Use JWT for authentication in the API: Modify the app/security.py file to implement JWT parsing and user authentication.

3. Protect sensitive endpoints: Use the Depends and security.get_current_user decorators to protect endpoints that require authentication.

📚 Documentation

Environment Variables

The following environment variables are required for the project:

• DB_URL: Database connection URL in the format postgresql://username:password@host/database_name.
• SECRET_KEY: JWT encryption key.
• ADMIN_AUTH_TOKEN: Authentication token for administrators.

Logging and Monitoring

1. Use a logging library (such as SQLAlchemy or FastAPI's default logging) for logging.
2. Configure the log file to output logs to a file or the console.

🚀 Deployment (Docker Compose)

Quick Start

1. Create a Docker network:

   docker network create caddy-network
   

2. Configure environment variables: Add ADMIN_AUTH_TOKEN and other necessary configurations to the .env file.

3. Start the service:

   docker-compose up --build -d
   

4. Access the service:

   • API: http://localhost/docs
   • Streamlit Interface: http://streamlit.localhost
   • MCP Server: http://mcp.localhost

Database Management

1. Add or remove databases:

   docker compose exec db psql -U postgres -c "drop DATABASE mlcbakery;"
   docker compose exec db psql -U postgres -c "create DATABASE mlcbakery;"
   

2. Execute database migrations:

   docker compose exec api alembic -c alembic.ini upgrade heads
   

📄 License

MIT License

⚠️ Important Note

• ADMIN_AUTH_TOKEN: The administrator token must be configured to protect sensitive endpoints.
• Docker Network: The caddy-network network must be created during deployment to ensure proper communication between services.

With the above steps, you can easily install, configure, and deploy the MLC Bakery service.