pip-tools

There are a few ways to manage dependencies: conda, poetry, pipenv. I recently discovered a different way, pip-tools. It’s actually very easy to use and in particular easy to integrate with a docker image. You simply create a requirements.in file which pip-compile converts to a requirements.txt file that you can then pip install inside your image by doing pip install -r requirements.txt.

There are multiple comparisons of poetry, pipenv, and pip-tools out there, including this one that compares specifically in the context of combining with docker, and that one that did a dec 2019 update and still declares pip-tools the winner. I also found that blog post useful as it shows a quick example of how to write a requirements.in.

You can install pip-tools through pip, pip install pip-tools. The only things you need to be careful with are the python version and OS you use to convert you requirements.in file to requirements.txt file. These needs to be the same as what you’ll use for your virtual environment. With Docker, this can be controlled by applying pip-tools inside a running container, then re-building that image.