CI/CD Workflow

This section outlines the steps required to configure a CI/CD workflow using the docker/docker-py project. As there is no CI/CD setup currently configured for this project, it is essential to understand how the various components can interact to form an automated pipeline. Below are suggested steps to implement a CI/CD workflow, including code examples.

Step 1: Setting up Continuous Integration

To establish continuous integration, a typical workflow would begin with the use of a CI service such as GitHub Actions, GitLab CI, or Travis CI.

Example GitHub Actions Configuration

Create a file named .github/workflows/ci.yml:

name: CI Workflow

on:
  push:
    branches:
      - main
  pull_request:
    branches:
      - main

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.12'

    - name: Install dependencies
      run: |
        pip install -r requirements.txt

    - name: Run unit tests
      run: |
        make unit-test

Step 2: Define Unit Tests

Make sure that your tests are defined and can be invoked through the Makefile. Based on the provided Makefile:

unit-test:
    pytest tests/

This command runs your tests using pytest. The tests should be located in a tests/ directory.

Step 3: Building the Docker Image

Add another step in your CI workflow to build the Docker image using the Dockerfile provided.

    - name: Build Docker image
      run: |
        docker build -t my-image:${{ github.sha }} .

This command builds the Docker image and tags it with the current commit hash.

Step 4: Publish the Docker Image

After your tests pass, you can push the Docker image to a container registry. Add the following step in your CI workflow:

    - name: Log in to Docker Hub
      env:
        DOCKER_USERNAME: ${{ secrets.DOCKER_USERNAME }}
        DOCKER_PASSWORD: ${{ secrets.DOCKER_PASSWORD }}
      run: echo "${{ secrets.DOCKER_PASSWORD }}" | docker login -u "${{ secrets.DOCKER_USERNAME }}" --password-stdin

    - name: Push Docker image
      run: |
        docker push my-image:${{ github.sha }}

Make sure to set the DOCKER_USERNAME and DOCKER_PASSWORD secrets in your repository settings.

Step 5: Continuous Deployment (Optional)

If continuous deployment is part of your requirements, you can set up a deployment job after the build job:

deploy:
  runs-on: ubuntu-latest
  needs: build
  if: github.ref == 'refs/heads/main'

  steps:
  - name: Deploy
    run: |
      ssh user@your-server "docker pull my-image:${{ github.sha }} && docker run -d my-image:${{ github.sha }}"

Replace user@your-server with your actual remote server credentials, ensuring secure connections are established for deployment.

Conclusion

The suggested steps provide a foundational CI/CD workflow using docker/docker-py for automated testing, building, and deploying Docker images.

It is encouraged to customize this setup according to specific project needs and configurations. As CI/CD becomes a critical aspect of modern development practices, implementing and refining these automated processes can significantly enhance productivity and reliability.

Source: Code examples derived from the provided Dockerfile and Makefile.