pixi VS hatch

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/

Exciting stuff! I view Hatch [1] as becoming the Cargo for Python because it's already close and has an existing (and growing) user base but I can definitely see depending on this for resolution and potentially not even using pip after it becomes more stable.

[1]: https://hatch.pypa.io/latest/

I was inspired enough by the hatch sync idea that I created a PR to add that functionality to hatch: https://github.com/pypa/hatch/pull/1094

Another concept I learned was about build backends, an import step which is used to initialize and install any dependencies of the app you're packaging. Since the tutorial went with using Hatch that is also what I went with, though it didn't provide a lot of useful details especially because it didn't show how to add any dependencies, so I took a look at the docs which were very nice and simple to follow.

Try using hatch: https://hatch.pypa.io/latest/

I'm trying to learn Hatch, I currently use [Poetry](python-poetry.org/) to manage my dependencies, and while I'm overall happy with it, I really like the features I'm reading about with Hatch. I'm also working on learning CI pipelines & Dockerizing Python applications, and Hatch seems like a really useful tool to learn for this (and just as a general use tool).

Poetry 1.2 has been a pain. Which was the dev's fault though. Switching to something new while deprecating a related feature is just plain bad. I've been looking into modern alternatives like PDM and Hatch, but haven't used them (yet).

For example, when it comes to Python, one option is to use the same packaging system that a huge number of open-source libraries and tools are published with. You can use setuptools or Hatch to build a "packaged" version of your code, and publish it to either the public PyPi repository or an internal one that you set up. Then your users can use pip to install your package, automatically fetch its dependencies, and keep it up to date, just like any other Python module.

Don't forget the new PyPa tool on the block: Hatch.

See also: https://github.com/pypa/hatch