Setting Up CI/CD with GitHub Actions for Python Projects

Introduction

Continuous Integration and Continuous Deployment (CI/CD) are essential practices in modern software development. In this guide, I’ll walk you through setting up a complete CI/CD pipeline for a Python project using GitHub Actions.

Why GitHub Actions?

GitHub Actions is a powerful, free (for public repositories) automation platform that integrates seamlessly with your GitHub workflow. Key benefits include:

• Native GitHub integration — no external services needed
• Matrix builds — test across multiple Python versions simultaneously
• Extensive marketplace — pre-built actions for common tasks
• Free for open source — generous free tier for public repos

Basic Workflow Structure

Create a .github/workflows/ci.yml file in your repository:

name: CI Pipeline

on:
  push:
    branches: [main, develop]
  pull_request:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: ['3.10', '3.11', '3.12']

    steps:
      - uses: actions/checkout@v4
      
      - name: Set up Python ${{ matrix.python-version }}
        uses: actions/setup-python@v5
        with:
          python-version: ${{ matrix.python-version }}
      
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
          pip install pytest pytest-cov flake8          
      
      - name: Lint with flake8
        run: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
      
      - name: Run tests
        run: pytest --cov=./ --cov-report=xml

Adding Docker Build

For projects that use Docker, extend your workflow:

  build:
    needs: test
    runs-on: ubuntu-latest
    
    steps:
      - uses: actions/checkout@v4
      
      - name: Build Docker image
        run: docker build -t myapp:${{ github.sha }} .
      
      - name: Run container tests
        run: |
          docker run --rm myapp:${{ github.sha }} python -m pytest          

Deployment Stage

Add automatic deployment to your server or cloud provider:

  deploy:
    needs: build
    runs-on: ubuntu-latest
    if: github.ref == 'refs/heads/main'
    
    steps:
      - name: Deploy to production
        uses: appleboy/ssh-action@v1.0.0
        with:
          host: ${{ secrets.HOST }}
          username: ${{ secrets.USERNAME }}
          key: ${{ secrets.SSH_KEY }}
          script: |
            cd /app
            docker-compose pull
            docker-compose up -d            

Secrets Management

Store sensitive data in GitHub Secrets:

1. Go to Repository → Settings → Secrets and variables → Actions
2. Add secrets like SSH_KEY, HOST, USERNAME
3. Reference in workflow as ${{ secrets.SECRET_NAME }}

Best Practices

1. Fail fast — run linting before time-consuming tests
2. Cache dependencies — speed up builds with pip caching
3. Use matrix builds — ensure compatibility across Python versions
4. Separate concerns — use different jobs for test, build, deploy
5. Protect main branch — require passing CI before merge

Conclusion

A well-configured CI/CD pipeline saves hours of manual work and catches issues early. Start with basic testing, then gradually add linting, Docker builds, and automated deployment as your project grows.

This workflow is used in my projects like TicketPro and News Bot.