Uv overtakes pip in CI

Try not to cut yourself while grinding that axe.

Python may not be the fastest language, but it's easy to learn, compilation times aren't an issue, you'll never have to fight the borrow checker, etc.

Every language has its warts.

Simple: uv run script.py just works on a clean box/CI, the lockfile keeps runs reproducible, and my CI “install deps” step is way faster now.

Dependency management has always felt complicated. However, environment management I think is actually way simpler than people realize. Python basically just walks up directories trying to find its packages dir. A python "env" is just a copy of the python binary in its own directory. That's pretty much it. Basically all difficulties I've ever had with Python environments have been straightened out by going back to that basic understanding. I feel like the narrative about virtualenvs has always seemed scary but the reality really isn't.

IMO, Python should only be used for what it was intended for: as a scripting language. I tend to use it as a kind of middle ground between shell scripting and compiled languages like Rust or C. It's a truly phenomenal language for gluing together random libraries and data formats, and whenever I have some one-off task where I need to request some data from some REST API, build a mapping from the response, categorize it, write the results as JSON, then push some result to another API -- I reach for Python.

But as soon as I have any suspicion that the task is going to perform any non-trivial computation, or when I notice the structure of the program starts to grow beyond a couple of files, that's when Python no longer feels suitable to the task.

Being forced to use a tool you don't want to use sucks, no matter how awesome that tool may or may not actually be. *conda and uv have roughly the same goals which means they're quite similar. For me, the speed of uv really does set it apart. For python programs with lots of dependencies, it's faster enough that I found it worth it to climb its learning curve. (ChatGPT makes that curve rather flat.) pip install -r requirements.txt went from a coffee break to me watching uv create the venv. But okay, speed gains aren't going to convince you.

Both of them manage venvs, but where the venv goes (by default) makes a difference, imo. Conda defaults to a user level directory eg ~/.conda/envs/my-venv. uv prefers a .venv dir in the project's folder. It's small, but it means per-project venvs are slightly more ergonomic with uv. Wereas with conda, because they're shared under homedir, it's easy to get lazy once you have a working venv and reuse that good working venv across multiple programs, and then it breaks when one program needs its dependencies updated and now it's broken for all of them. Naturally that would never happen to a skilled conda operator, so I'll just say per-project uv venv creation and recreation flows just that tiny bit smoother, because I can just run "rm -rf .venv" and not worry about breaking other things. One annoyance I have with uv is that it really wants to use the latest version of python it knows about, and that version is too new for a program or one of its dependencies, and the program won't run. Running "uv venv --python 3.12" instead of"uv venv" isn't onerous, but it's annoying enough to mention. (pyproject.toml lets projects specify version requirements, but they're not always right.) Arguably that's a python issue and not uv's, but as users, we just want things to work, dammit. That's always the first thing I look for when things don't work.

As mentioned, with uv the project venv lives in .venv inside the project's directory which lets "uv run program.py" cheat. Who amongst us hasn't forgotten to "source .venv/bin/activate" and been confused when things "suddenly" stopped working. So if you're in the project directory, "uv run" will automatically use the project's .venv dir.

As far as it being pushed to promote rust. I'm sure there's a non-zero amount of people for whom that's true, but personally as that makes it harder to contribute to uv, it's actually a point against it. Sometimes I wonder how fast it would be if it was written in python using the same algorithms, but run under pypy.

Anyway, I wouldn't say any of that's revolutionary. Programs exist to translate between the different project file types (requirements.txt/environment.yml/pyproject.toml) so if you're already comfortable with conda and don't want to use uv, and you're not administering any shared system(s), I'd just stick the command to generate environment.yml from pyproject.toml on a cheat sheet somewhere.

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One bug I ran into with one of the condas; I forgot which, is that it called out to pip under the hood in interactive mode and pip got stuck waiting for user input and that conda just sat there waiting for input that would never come. Forums were filled with reports by users talking about letting it run for hours or even days. I fixed that, but it soured me on *conda, unfortunately.