FastAPI MLOps Project

This repository showcases how to integrate FastAPI, Docker, and Poetry to build a production-ready Machine Learning Operations (MLOps) project. The project is designed to deploy machine learning models via a FastAPI microservice and automate the deployment process using GitHub Actions for a seamless CI/CD pipeline, deploying the containerized app to AWS EC2.

This project is based on the tutorial provided by The Halftime Code, which walks through the entire process from project setup to deployment on AWS.

Project Features

• FastAPI: A high-performance web framework for serving machine learning models.
• Poetry: Manages Python dependencies and virtual environments.
• Docker: Containerizes the application for flexible deployment.
• AWS EC2: Deployment platform using GitHub Actions for CI/CD.
• GitHub Actions: Automates testing, building, and deploying the model to AWS EC2.

Project Structure

mlops/
├── .github/               # GitHub Actions workflows
│   └── workflows/
│       └── deploy.yml     # CI/CD pipeline
├── src/                   # Source code
│   ├── api/               # API routes
│   │   └── v1/
│   │       └── routes.py  # Prediction endpoint
│   ├── core/              # Core app setup
│   │   └── config.py      # Configuration management
│   └── app/               # Business logic
│       └── services/      # Model service logic
├── tests/                 # Unit tests
│   ├── test_api.py        # Test API endpoints
│   └── test_services.py   # Test business logic
├── models/                # Pre-trained machine learning model
│   └── model.pkl
├── Dockerfile             # Docker configuration
├── docker-compose.yaml    # Docker Compose setup
├── pyproject.toml         # Poetry configuration
└── README.md              # Documentation

Setup Instructions

1. Clone the Repository

git clone https://github.com/yourusername/mlops-fastapi.git
cd mlops-fastapi

2. Install Dependencies

Ensure you have Poetry installed:

pip install poetry

Then, install the project dependencies:

poetry install

3. Train and Save the Model

Train a sample machine learning model (RandomForest on Iris dataset) and save it to the models directory:

poetry run python -m train_model

4. Run the FastAPI Application

Start the FastAPI server locally:

poetry run uvicorn src.core.server:app --reload

Access the API at http://127.0.0.1:8000.

5. Test the API

You can test the prediction endpoint using curl:

curl -X POST "http://127.0.0.1:8000/api/v1/predict" \
-H "Content-Type: application/json" \
-d '{"features": {"sepal length (cm)": 5.1, "sepal width (cm)": 3.5, "petal length (cm)": 1.4, "petal width (cm)": 0.2}}'

6. Run Tests

Run the unit tests with:

poetry run python -m pytest tests/

7. Dockerize the Application

To build and run the Docker container:

docker build -t mlops-fastapi .
docker run -p 8000:8000 mlops-fastapi

8. CI/CD Pipeline

The GitHub Actions CI/CD pipeline automates deployment to AWS EC2. Ensure you configure the necessary GitHub secrets for AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, EC2_SSH_KEY, and DOCKER_PASSWORD.

Deployment to AWS EC2

Follow the tutorial for detailed instructions on setting up AWS EC2 and automating deployment via GitHub Actions.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

References

• Tutorial: FastAPI for MLOps: Integrate Docker, Poetry, and Deploy to AWS EC2