nix-cde VS pyenv

This is what I'm using with gitlab: https://github.com/takeda/nix-cde/blob/master/contrib/gitlab...

I myself use https://github.com/takeda/nix-cde it just wraps other projects in an opinionated way and contains the boiler plate that I would normally use otherwise.

There's a project that does this with using Nix: https://github.com/takeda/nix-cde (this is a wrapper around https://github.com/nix-community/poetry2nix)

I personally use this: https://github.com/takeda/nix-cde it has the benefit of a reproducible build environment, but unfortunately anything involving Nix has a steep learning curve.

I use this myself: https://github.com/takeda/nix-cde

I don't use NixOS myself, but have Nix installed on my Mac, and it seems to provide all functionality of package or version managers I needed.

I think though it is more complex because it is a programming language that provides this functionality instead of purpose build tool like asdf.

For my needs I created a framework for development: https://github.com/takeda/nix-cde to avoid cruft of including the same things over and over in my projects.

Not an OP, but I became a big fan of using poetry for managing dependencies. For managing python version I started using Nix package manager. It allows to describe all dependencies via code, but with time that code became a boilerplate, so I created this: https://github.com/takeda/nix-cde

It works very well for me so far.

If you have a requirement for multiple, specific Python versions, why not just use pyenv?

https://github.com/pyenv/pyenv

For more information visit: pyenv repository

Note that virtual environments assume you are using the same global version of Python. Often, this is not the case and additional tools like pyenv can be used alongside virtual environments when you need to switch between versions of Python itself on your local machine.

Python version manager pyenv

First of all, install the Python virtual environment from these links: 1 and 2. I developed my GPT-based API in Python version 3.8.18. Pick any Python versions >= 3.7.

Note: Most modern systems will probably have a system environment that meets this requirement, but if yours does not or if you prefer not to install anything in your system environment (even if it's just PDM) check out asdf or pyenv to help install and manage additional Python environments.

When dealing with software development, reproducibility is key. This is why we encourage you to use Python virtual environments to set up an isolated environment for your project. Virtual environments allow the isolation of dependencies, which plays a crucial role to avoid breaking compatibility between different projects. We cannot cover all the details about virtual environments in this post, but we encourage you to learn more about venv, pyenv or conda for a better understanding on how to create and manage virtual environments.

For Python, with your use case I would avoid system packages, no matter the distro. It sounds like it would be worth setting up pyenv and working exclusively with virtual environments.

For OSX there is homebrew or pyenv (pyenv is another solution on Linux). As pyenv compiles from source it will require setting up XCode (the Apple IDE) tools to support this which can be pretty bulky. Windows users have chocolatey but the issue there is it works off the binaries. That means it won't have the latest security release available since those are source only. Conda is also another solution which can be picked up by Visual Studio Code as available versions of Python making development easier. In the end it might be best to consider using WSL on Windows for installing a Linux version and using that instead.