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Why not just use a tool like asdf (https://asdf-vm.com/) or mise (https://mise.jdx.dev/)?
These tools have the advantage of not being multi-taskers and can manage version for all your tools. You wouldn’t need pyenv and npm and rvm and…
We’ve even started committing the .mise.toml files for projects to our repos. That way, since we work on multiple projects that may need multiple versions of the same tool, it’s handled and documented.
Thanks for taking the time. Can you help me understand? Because I didn't get this from the devs.
Just looking at a random formula, am I correct to understand that this will use python 3.10 and NOT 3.12?[0] I understand ones like this[1] where there's a note about the issue with newer versions.
What I'm trying to understand is if the python version is specified by the formula or it will default to the newest version. If it requires it to be specified in the formula then doesn't this make it contingent on the maintainer upgrading it every python version? I didn't verify [0], but it looks like it should work with later versions of python, and it it is still using 3.10 then isn't that essentially the maintainers "fault?" Because that's my concern. I can't see how something like this stays updated when it requires a maintainer to update. Seems better to have a >=3.10 and then do ==3.10 if only 3.10 works (odd) or >=3.10 <3.12 if it works for 10 and 11 but not 12. Especially since formulas are often made by people that are not the package developers themselves and we're just reliant upon someone keeping up. I'd rather break upon a new version than have many old pythons installed. I know there's no perfect solution, but we're talking about failure modes. And fwiw, I'd rather it try to use the system or environment python rather than installing a unique version. It just gets confusing when you need to add dependencies for optional stuff that wasn't included in the formula and is extremely non-obvious to a new user.
[0] https://github.com/Homebrew/homebrew-core/blob/12a0f6bbbeda8...
[1] https://github.com/Homebrew/homebrew-core/blob/12a0f6bbbeda8...
Why not just use a tool like asdf (https://asdf-vm.com/) or mise (https://mise.jdx.dev/)?
These tools have the advantage of not being multi-taskers and can manage version for all your tools. You wouldn’t need pyenv and npm and rvm and…
We’ve even started committing the .mise.toml files for projects to our repos. That way, since we work on multiple projects that may need multiple versions of the same tool, it’s handled and documented.
I've been using Rye[0] lately, which has been pretty good. It's really just a wrapper around a bunch of underlying tools - it's nice to not have to worry about those and let Rye do it's thing.
All that being said, the creator of Rye is 100% cognizant of that XKCD comic, this [1] is a nice read.
I'm not super well versed in Python tooling at all. I've had to work a lot in Python in the past 6+ months, and I become super confused when I tried making a Python project in my spare time.
I settled on Rye because it just seemed to be the easiest to use.
[0]: https://rye-up.com/
https://github.com/astral-sh/uv
So fast it finally made virtual environments usable for me.
These builds are an alternative: https://github.com/indygreg/python-build-standalone
Those are what Rye and hatch use.
Drawbacks: late availability of patch versions, various quirks from how they are built (missing readline, missing some build info that self-compiled C python modules might need.)
We use Pyenv successfully for developing the Flower open-source project. We use a few simple Bash scripts to manage virtual environments with different Python versions via pyenv and the pyenv-virtualenv plugin.
The main scripts are `venv-create.sh`, `venv-delete.sh` and `bootstrap.sh`. `venv-reset.sh` pulls these three scripts together to make reinstalling your venv a single command.
Here's the link if anyone is interested: https://github.com/adap/flower/tree/main/dev
Point of clarification: asdf uses python-build which is from pyenv.
https://github.com/asdf-community/asdf-python?tab=readme-ov-...
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.
If I pin a version of Python, isn't that going to wreck any tooling that depends on it? Unless you're saying have multiple versions of Python installed.
This is practically the only remaining annoyance I have with the Python ecosystem (relative imports aside). I use some tools, like Glances [0] whose formula relies on a much newer version (3.12) than the actual package requires (3.8) [1].
So when there's a Python update, all of those update as well. I thought I'd fixed this with pipx, but in a way that's worse, because the venvs it builds depend on a specific version of Python existing, which doesn't work well with brew always wanting to upgrade it.
I want a stable, system-level Python that I don't touch, don't add packages to, and which only exists as a dependency for anything that needs it. If an update would break a package I have installed (due to Python library deprecation, etc.), it should warn me before updating. Otherwise, I don't care, as long as any symlinks are taken care of.
Separately, I want a stable, user-level Python that I can do whatever I want to. Nothing updates it automatically. I can accomplish this by compiling Python and using `make altinstall`, but if there's a better way, I'd love to hear about it.
[0]: https://github.com/Homebrew/homebrew-core/blob/20e744191e74d...
[1]: https://github.com/nicolargo/glances
> 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.