pixi VS pip-tools

re: C/C++ development: anybody using conda/pixi for dependency management? Here's an example of compiling a C++ SDL program using pixi and the SDL dependency from conda-forge [1].

Seems viable as a replacement for things like vckpg [2] which only builds from source.

I'm still researching this but it seems like rattler [3] is the tool to use to build/publish packages. The supported repos are: prefix.dev's own hosting, anaconda.org, artifactory or a self-hosted server.

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1: https://github.com/prefix-dev/pixi/blob/main/examples/cpp-sd...

2: https://github.com/microsoft/vcpkg

3: https://prefix-dev.github.io/rattler-build/latest/authentica...

I recently started using https://github.com/prefix-dev/pixi for Python projects. I really love it so far, but this tool looks a bit more mature, which makes sense considering pixi is relatively new.

Have you tried https://pixi.sh/ ? It brings Cargo/NPM/Poetry like commands and lock files to the Conda ecosystem, and now can manage and lock PyPI dependencies alongside by using uv under the hood.

I haven't been using anything CUDA, but the scientific geospatial stack is often a similar mess to install, and it's been handling it really well.

Isn't this basically what pixi wants to be? Wouldn't it be better to work together?

https://github.com/prefix-dev/pixi/

> Why is the "requirements.txt" file a stupid flat listing of all transitive dependencies with pinned versions? It makes it harder to change library versions even if there are no true conflicts.

My friend, here is what you seek: https://github.com/jazzband/pip-tools

requirements.txt is flat because it's really the output of `pip freeze`. It's supposed to completely and exactly rebuild the environment. Unfortunately it's far too flexible and people abuse it by putting in only direct dependencies etc.

If you're writing packages, you don't need a requirements.txt at all, by the way. Package dependencies (only direct dependencies) live in pyproject.toml with the rest of the package config. requirements.txt (and pip tools) are only for when you want to freeze the whole environment, like for a server deployment.

For all my projects I found myself regenerating manual lock files using complex shell commands with pip-compile to get a reproducible environments across devices using a custom pre-install-command. I finally decided that instead of hacking together the same solution on all my projects I would build a plugin that handles this complexity for me.

Instead of venv, we are using pip-tools in this starter kit. pip-tools take things further in dependency management. Check out what pip-tools does in their official GitHub repo. In short, it helps your project find the best match for the dependent packages. For example, you might need two packages A and B in your project that requires same package C under the hood. But A requires any version of C from 1.0.1 to 1.0.10 and B requires any version of C from 1.0.7 to 1.0.15. Pip tools will automatically compile the version of 'C' that suits for both of your packages.

I've created a small project called just-pip-tools that combines pip-tools and just to manage Python dependencies in a layered approach. This isn't a magic bullet; it's a set of files you can adapt to your needs.

For small projects I recommend pip-tools. Just write packet list in requirements.in and pip-compile compile a requirements.txt with comments.

Take a look at pip-tools, great package. https://github.com/jazzband/pip-tools

pip-tools

I recommend generating a lockfile to document this information, as you might do with pip-tools.

Check out pip-tools [1] which does exactly that, albeit in a slightly more polished way.

[1]: https://github.com/jazzband/pip-tools