GitLab CI/CD Pipeline Examples for Python, Angular, and Django Projects

GitLab CI/CD Pipeline Examples for Python, Angular, and Django Projects#

Learn practical GitLab CI/CD pipeline examples for three common project types: Python, Angular, and Django. This guide covers lint, test, build, cache, artifacts, Docker image builds, deployment rules, variables, and best practices.

Table of contents#

• Quick answer
• Common GitLab pipeline structure
• Python pipeline example
• Angular pipeline example
• Django pipeline example
• Cache and artifacts
• Docker image examples
• Deployment rules
• Variables and secrets
• Common mistakes
• Best practices
• Related Melochip guides

Quick answer#

Python#

Use pip, requirements files, pytest, linting, reports, and optional package artifacts.

Angular#

Use npm ci, lint, unit tests, production build, and dist/ artifacts.

Django#

Use pip, migrations check, tests, collectstatic, Docker image builds, and environment variables.

1. Common GitLab pipeline structure#

Most project pipelines can start with a simple structure:

stages: - validate - test - build - package - deploy

The exact commands change by project type, but the idea remains the same: validate code, run tests, build output, package if required, and deploy only when it is safe.

2. GitLab pipeline for Python project#

A simple Python project may use requirements.txt , pytest , and optional lint tools such as ruff or flake8 .

image: python:3.12 stages: - validate - test cache: paths: - .cache/pip before_script: - python --version - pip install --cache-dir .cache/pip -r requirements.txt lint_python: stage: validate script: - pip install ruff - ruff check . run_pytest: stage: test script: - pip install pytest pytest-cov - pytest --junitxml=pytest-report.xml --cov=. --cov-report=xml artifacts: when: always reports: junit: pytest-report.xml paths: - coverage.xml

3. GitLab pipeline for Angular project#

Angular projects usually use Node.js, npm, lint, tests, and production builds. The most important build output is usually the dist/ folder.

image: node:20 stages: - install - validate - test - build cache: key: files: - package-lock.json paths: - .npm/ install_dependencies: stage: install script: - npm ci --cache .npm --prefer-offline lint_angular: stage: validate script: - npm run lint unit_tests: stage: test script: - npm test -- --watch=false --browsers=ChromeHeadless build_angular: stage: build script: - npm run build -- --configuration production artifacts: paths: - dist/ expire_in: 1 week

4. GitLab pipeline for Django project#

Django pipelines normally need dependencies, environment variables, database service setup, migration checks, tests, and static file collection.

image: python:3.12 services: - postgres:16 variables: POSTGRES_DB: test_db POSTGRES_USER: test_user POSTGRES_PASSWORD: test_password DATABASE_URL: postgres://test_user:test_password@postgres:5432/test_db DJANGO_SETTINGS_MODULE: config.settings stages: - validate - test - build before_script: - pip install -r requirements.txt check_migrations: stage: validate script: - python manage.py makemigrations --check --dry-run run_django_tests: stage: test script: - python manage.py test collect_static: stage: build script: - python manage.py collectstatic --noinput artifacts: paths: - staticfiles/ expire_in: 1 week

5. Cache and artifacts by project type#

6. Docker image examples#

For production deployments, many teams package Python, Angular, and Django applications as Docker images.

build_docker_image: stage: package image: docker:27 services: - docker:27-dind variables: DOCKER_TLS_CERTDIR: "/certs" IMAGE_TAG: "$CI_REGISTRY_IMAGE:$CI_COMMIT_SHORT_SHA" script: - echo "$CI_REGISTRY_PASSWORD" | docker login "$CI_REGISTRY" -u "$CI_REGISTRY_USER" --password-stdin - docker build -t "$IMAGE_TAG" . - docker push "$IMAGE_TAG" rules: - if: '$CI_COMMIT_BRANCH == "main"'

7. Branch rules and deployment jobs#

deploy_development: stage: deploy script: - echo "Deploying to development" environment: name: development rules: - if: '$CI_COMMIT_BRANCH == "develop"' deploy_production: stage: deploy script: - echo "Deploying to production" environment: name: production rules: - if: '$CI_COMMIT_BRANCH == "main"' when: manual - when: never

8. Variables and secrets#

Common mistakes#

Best practices#

• Keep project-specific commands in package scripts, Makefiles, or documented scripts.
• Run lint and tests before building Docker images.
• Use cache only for speed.
• Use artifacts for build output and reports.
• Use protected variables for production secrets.
• Use branch rules and manual production deployments.
• Use Docker image tags based on commit SHA or release version.
• Keep CI/CD YAML readable and documented.

Screenshot checklist#

1. Python pipeline showing lint and pytest jobs.
2. Angular pipeline showing production build and dist/ artifact.
3. Django pipeline showing migrations check and tests.
4. Docker image pushed to registry.
5. Manual production deployment button.
6. CI/CD variables page with values hidden.

• GitLab CI/CD Learning Path
• GitLab Pipeline for Node.js Project
• GitLab CI/CD YAML Syntax Explained
• GitLab Artifacts vs Cache Explained
• GitLab CI/CD Docker Build Pipeline
• GitLab CI/CD Best Practices for DevOps Teams

Official references#

• GitLab CI/CD YAML syntax reference
• GitLab CI/CD cache documentation
• GitLab job artifacts documentation
• GitLab environments documentation

Conclusion#

Python, Angular, and Django projects use different commands, but the GitLab CI/CD pattern is similar: validate, test, build, package, and deploy safely. Once you understand cache, artifacts, variables, Docker images, and deployment rules, you can adapt GitLab pipelines to almost any project type.