snakepacker/python

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A simple way to build a Python project This repository provides and demonstrates a way to pack python package into a compact Docker image, based on modern Ubuntu Jammy operation system. Awailable images This project is available both in the official docker repository and also on the Github Container Registry (ghcr.io). ghcr.io docker.io ghcr.io/snakepacker/python/all snakepacker/python:all ghcr.io/snakepacker/python/all-pillow snakepacker/python:all-pillow ghcr.io/snakepacker/python/3.12 snakepacker/python:3.12 ghcr.io/snakepacker/python/3.12-pillow snakepacker/python:3.12-pillow ghcr.io/snakepacker/python/3.11 snakepacker/python:3.11 ghcr.io/snakepacker/python/3.11-pillow snakepacker/python:3.11-pillow ghcr.io/snakepacker/python/3.10 snakepacker/python:3.10 ghcr.io/snakepacker/python/3.10-pillow snakepacker/python:3.10-pillow ghcr.io/snakepacker/python/3.9 snakepacker/python:3.9 ghcr.io/snakepacker/python/3.9-pillow snakepacker/python:3.9-pillow ghcr.io/snakepacker/python/3.8 snakepacker/python:3.8 ghcr.io/snakepacker/python/3.8-pillow snakepacker/python:3.8-pillow ghcr.io/snakepacker/python/3.7 snakepacker/python:3.7 ghcr.io/snakepacker/python/3.7-pillow snakepacker/python:3.7-pillow ghcr.io/snakepacker/python/pylama snakepacker/python:pylama ghcr.io/snakepacker/python/pylava snakepacker/python:pylava ghcr.io/snakepacker/python/ipython snakepacker/python:ipython ghcr.io/snakepacker/python/certbot snakepacker/python:certbot ghcr.io/snakepacker/python/black snakepacker/python:black ghcr.io/snakepacker/python/gray snakepacker/python:gray ghcr.io/snakepacker/python/ruff snakepacker/python:ruff ghcr.io/snakepacker/python/jupyterlab snakepacker/python:jupyterlab ghcr.io/snakepacker/python/base snakepacker/python:base Image descriptions The images according to their purpose and features: Tag Info Purpose Features all build stage all available python versions, libpython headers and compiler all-pillow build stage all available python versions, libpython headers, graphics libraries headers and compiler 3.12 target stage pure python 3.12 3.11 target stage pure python 3.11 3.10 target stage pure python 3.10 3.9 target stage pure python 3.9 3.8 target stage pure python 3.8 3.7 target stage pure python 3.7 3.12-pillow target stage pure python 3.12 with graphics libraries binaries 3.11-pillow target stage pure python 3.11 with graphics libraries binaries 3.10-pillow target stage pure python 3.10 with graphics libraries binaries 3.9-pillow target stage pure python 3.9 with graphics libraries binaries 3.8-pillow target stage pure python 3.8 with graphics libraries binaries 3.7-pillow target stage pure python 3.7 with graphics libraries binaries pylama ready to use pylama application image (useful for CI) pylava ready to use pylava application image (useful for CI) ipython ready to use ipython application image certbot ready to use certbot application image black ready to use black application image (useful for CI) gray ready to use gray application image (useful for CI) ruff ready to use ruff linter image (useful for CI) jupyterlab ready to use jupyterlab image base common layers Concept The main idea of this method is to build a virtualenv for your package using heavy full-powered image (e.g. ghcr.io/snakepacker/python:all, that contains all necessary headers, libraries, compiler, etc.), and then copy it into thin base image with suitable Python version. Reasons Why so complex? You could just COPY directory with your python project into Docker container, and for the first point of view this seems to be reasonable. But just copying directory with python project cause several problems: Generated on different operating system .pyc files can be put into Docker image accidentally. Thus, python would try to rewrite .pyc with correct ones each time when Docker image would be started. If you would run Docker image in read-only mode - your application would break. Large possibility that you would also pack garbage files: pytest and tox cache, developer's virtualenv and other files, that just increate the size of the resulting image. No explicit entrypoint. It is not obvious what commands end user is able to run (we hope you've implemented -h or --help arguments). By default, tox interprets your package as python module, e.g. it tries to run pip install . when preparing environment. Yes, of course, you can solve all of those problems using hacks, specific settings, .dockeridnore file, and other tricks. But it would be non-intuitive and non-obvious for your users. So, we recommend to spend a little more time and pack your package carefully, so your users would run it with pleasure. Example For example, you may build the jupyter notebook. Just create a Dockerfile with the following content: ################################################################# ####################### BUILD STAGE ############################# ################################################################# # This image contains: # 1. All the Python versions # 2. required python headers # 3. C compiler and developer tools FROM ghcr.io/snakepacker/python:all as builder # Create virtualenv on python 3.10 # Target folder should be the same on the build stage and on the target stage RUN python3.10 -m venv /usr/share/python3/app # Install target package RUN /usr/share/python3/app/bin/pip install -U pip 'ipython[notebook]' # Will be find required system libraries and their packages RUN find-libdeps /usr/share/python3/app > /usr/share/python3/app/pkgdeps.txt ################################################################# ####################### TARGET STAGE ############################ ################################################################# # Use the image version used on the build stage FROM ghcr.io/snakepacker/python:3.10 # Copy virtualenv to the target image COPY --from=builder /usr/share/python3/app /usr/share/python3/app # Install the required library packages RUN xargs -ra /usr/share/python3/app/pkgdeps.txt apt-install # Create a symlink to the target binary (just for convenience) RUN ln -snf /usr/share/python3/app/bin/ipython /usr/bin/ CMD ["ipython"] And just build this: docker build -t ipython . Useful tools All images contain ready to use and simple wrappers for easy image building. apt-install Pretty simple bash script. The main purpose is removing the apt cache and temporary files after installation when you want to install something through apt-get install. Otherwise, you have to write something like this apt-get update && \ apt-get install -y tcpdump && \ rm -fr /var/lib/apt/lists /var/lib/cache/* /var/log/* It might be replaced like this: apt-install tcpdump wait-for-port Python script which waits for availability one or multiple TCP ports. It's very useful for tests and with docker-compose. wait-for-port --period=0.5 --timeout=600 postgres:5432 pgbouncer:6432 && python myscript.py Or shorter (values from previous example are defaults): wait-for-port postgres:5432 pgbouncer:6432 && python myscript.py This script will be trying to make connections to passed endpoints until timeout would be reached or endpoints stay connectable. find-libdeps A shell script which find binary *.so files and resolve required system package for install library dependencies. Save required packages find-libdeps /usr/share/python3/app > /usr/share/python3/app/pkgdeps.txt Install saved packages xargs -ra /usr/share/python3/app/pkgdeps.txt apt-install