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Examples

This directory contains practical examples demonstrating common use cases for the Python Container Builder.

Available Examples

1. FastAPI Web Service

Directory: fastapi/

The most common use case - a web service with external dependencies.

What you'll learn:

  • Building a web API with FastAPI
  • Using uv for fast dependency installation
  • Multi-stage builds to distroless runtime
  • Best practices for web services

Use this when: You're building REST APIs, microservices, or web applications.

View FastAPI Example →

2. Poetry CLI Tool

Directory: poetry-cli/

Demonstrates using Poetry for dependency management instead of requirements.txt.

What you'll learn:

  • Using Poetry in Docker builds
  • Exporting Poetry dependencies for optimal builds
  • Building CLI applications with Click
  • Separating dev and production dependencies

Use this when: You prefer Poetry for dependency management or are building CLI tools.

View Poetry Example →

3. Data Science Application

Directory: data-science/

Shows how to handle packages with native extensions that require system dependencies.

What you'll learn:

  • Installing packages like NumPy, Pandas that need compilation
  • Adding system build dependencies (gcc, gfortran)
  • Copying runtime libraries to distroless
  • Troubleshooting common compilation issues

Use this when: You're working with data science, ML, or any packages requiring native compilation.

View Data Science Example →

Quick Start

Each example is self-contained with:

  • ✅ Complete, working Dockerfile
  • ✅ Example application code
  • ✅ Dependencies file (requirements.txt or pyproject.toml)
  • ✅ Detailed README with explanations

Build and Run Any Example

# Navigate to an example directory
cd fastapi/

# Build the image
docker build -t example .

# Run the container
docker run -p 8000:8000 example

Choosing the Right Example

Your Use Case Recommended Example
Web API or microservice FastAPI
CLI tool or script Poetry CLI
Using Poetry for dependencies Poetry CLI
Data analysis, ML, scientific computing Data Science
Packages that need compilation (Pillow, psycopg2, lxml) Data Science
Simple Python scripts with dependencies FastAPI (simplify as needed)

Common Patterns Across Examples

Multi-Stage Build Structure

All examples follow this pattern:

# Stage 1: Build dependencies
FROM ghcr.io/jski/python-container-builder:3.12 as build-venv
COPY requirements.txt /requirements.txt
RUN uv pip install -r /requirements.txt

# Stage 2: Minimal runtime
FROM gcr.io/distroless/python3-debian12
COPY --from=build-venv /.venv /.venv
COPY app.py /app/app.py
WORKDIR /app
ENTRYPOINT ["/.venv/bin/python3", "-u", "app.py"]

Version Selection

Choose your Python version by changing the tag:

  • :3.12 - Python 3.12 (recommended for most users)
  • :3.14 or :latest - Python 3.14 (newest features)
  • :3.11 - Python 3.11 (long-term stable)
  • :3.9, :3.10 - Older versions (use debian11 distroless)

Adding System Dependencies

If your packages need compilation:

RUN apt-get update && apt-get install -y --no-install-recommends \
    gcc \
    libpq-dev \
    && rm -rf /var/lib/apt/lists/*

See the Data Science example for details.

Next Steps

  1. Pick an example that matches your use case
  2. Read the README in that directory
  3. Build and run the example locally
  4. Customize for your application
  5. Check TROUBLESHOOTING.md if you encounter issues

Need Help?

Contributing Examples

Have a useful example to share? Contributions are welcome! Each example should:

  • Solve a real-world use case
  • Include complete, working code
  • Have clear documentation
  • Follow the multi-stage build pattern
  • Be under 100 lines (Dockerfile + code)