README.md

Python container (Docker container for Python web development)

Docker container allowing you to build and test your Python project. This container mainly targets Python web project developments but you can try to make it work with your project even if your project is not using web technologies.

Supported tags and respective Dockerfile links

• 3.12, latest (3.12/Dockerfile)
• 3.8 (3.8/Dockerfile)
• 3.7 (3.7/Dockerfile)
• 3.6 (3.6/Dockerfile)

poetry is installed in every image to make Python dependencies installation easier.

The following dependencies are installed to make wheel installation work properly:

• build-essentials (g++, gcc, make and libc6-dev)
• python-dev (header files and static library for Python)

Use the container

We recommend you to use the dev user instead of root when running that container.

The container's working directory is /var/www/app so we advise you to mount your project directory onto this place.

Building the project interactively in the container

To run the container and open a bash in your python project run the following command:

docker run -it --entrypoint /bin/bash --user=dev --volume=<path-to-your-python-project-directory>:/var/www/app groovytron/python:latest

Commands configurations

• The python interpreter is usable through the python or python3
• You can install python packages using either pip or pip3
• You can manage your dependencies using either poetry or pipenv

Note: as the PIPENV_VENV_IN_PROJECT environment variable is set, pipenv will create the virtual environment in a folder called .venv in your project directory. This configuration is applied to root and dev users.

Note: as poetry's configuration is contained into config/poetry/config.toml and virtualenvs.in-project is set to true, poetry will create the virtual environment in a folder called .venv in your project directory. This configuration is applied to root and dev users.

Contributing

Pull requests, bug reports, and feature requests are welcome.

Develop locally

If you want to improve one of these images, you will need the following software installed on your system:

• docker
• docker-compose (if you want to build images using the file build.yml)
• make (if you want to build images using the file build.yml and abstract from it with the Makefile)

Once you have the above softwares installed, run make python3.7 to build the python3.7 image. To build all the images, run make all. If you want to clean your builds, simply run make clean.

License

MIT License. See the LICENSE file.