I highly, highly recommend uv. It solves & installs dependencies incredibly fast, and the CLI is very intuitive once you've memorized a couple commands. It handles monorepos well with the "workspaces" concept, it can replace pipx with "uv tool install," handle building & publishing, and the docker image is great, you just add a FROM line to the top and copy the bin from /uv.
I've used 'em all, pip + virtualenv, conda (and all its variants), Poetry, PDM (my personal favorite before switching to uv). Uv handles everything I need in a way that makes it so I don't have to reach for other tools, or really even think about what uv is doing. It just works, and it works great.
I even use it for small scripts. You can run "uv init --script <script_name.py>" and then "uv add package1 package2 package3 --script <script_name.py>". This adds an oddly formatted comment to the top of the script and instructs uv which packages to install when you run it. The first time you run "uv run <script_name.py>," uv installs everything you need and executes the script. Subsequent executions use the cached dependencies so it starts immediately.
If you're going to ask me to pitch you on why it's better than your current preference, I'm not going to do that. Uv is very easy to install & test, I really recommend giving it a try on your next script or pet project!
The script thing is great. By the way those 'oddly formatted' comments at the top are not a uv thing, it's a new official Python metadata format, specifically designed to make it possible for 3rd party tools like uv to figure out and install relevant packages.
And in case it wasn't clear to readers of your comment, uv run script.py creates an ephemeral venv and runs your script in that, so you don't pollute your system env or whatever env you happen to be in.
I generally agree but one thing I find very frustrating (i.e. have not figured out yet) is how deal with extras well, particularly with pytorch. Some of my machines have GPU, some don't and things like "uv add" end up uninstalling everything and installing the opposite forcing a resync with the appropriate --extra tag. The examples in the docs do things like CPU on windows and GPU on Linux but all my boxes are linux. There has to be a way to tell it that "hey I want --extra GPU" always on this box. But I haven't figured it out yet.
Getting the right version of PyTorch installed to have the correct kind of acceleration on each different platform you support has been a long-standing headache across many Python dependency management tools, not just uv. For example, here's the bug in poetry regarding this issue: https://github.com/python-poetry/poetry/issues/6409
As I understand it, recent versions of PyTorch have made this process somewhat easier, so maybe it's worth another try.
I'm not sure if I got your issue, but I can do platform-dependent `index` `pytorch` installation using the following snippet in `pyproject.toml` and `uv sync` just handles it accordingly.
This happened to me too, that is why I stopped using it for ML related projects and stuck to good old venv. For other Python projects I can see it being very useful however.
I happened to use uv recently for a pet project, and I totally agree with you. It's really really good. I couldn't believe its dependency resolution and pulling can be so fast. Imho, it's the python package manager (I don't know the most suitable name to categorize it) done right, everything just works, the correct way.
Can confirm this is all true. I used to be the "why should I switch" guy. The productivity improvement from not context switching while pip installs a requirements file is completely worth it.
As a person who don’t work often on python code but occasionally need to run server or tool I find UV blessing.
Before that I would beg people to help me just not to figure out what combination of obscure python tools I need. Now doing “uv run server.py” usually works.
That scripting trick is awesome! One of the really nice things about Elixir and its dependency manager is that you can just write Mix.install(…) in your script and it’ll fetch those dependencies for you, with the same caching you mentioned too.
Does uv work with Jupyter notebooks too? When I used it a while ago dependencies were really annoying compared to Livebook with that Mix.install support.
uv offers another useful feature for inline dependencies, which is the exclude-newer field[1]. It improves reproducibility by excluding packages released after a specified date during dependency resolution.
I once investigated whether this feature could be integrated into Mix as well, but it wasn't possible since hex.pm doesn't provide release timestamps for packages.
I've used 'em all, pip + virtualenv, conda (and all its variants), Poetry, PDM (my personal favorite before switching to uv). Uv handles everything I need in a way that makes it so I don't have to reach for other tools, or really even think about what uv is doing. It just works, and it works great.
I even use it for small scripts. You can run "uv init --script <script_name.py>" and then "uv add package1 package2 package3 --script <script_name.py>". This adds an oddly formatted comment to the top of the script and instructs uv which packages to install when you run it. The first time you run "uv run <script_name.py>," uv installs everything you need and executes the script. Subsequent executions use the cached dependencies so it starts immediately.
If you're going to ask me to pitch you on why it's better than your current preference, I'm not going to do that. Uv is very easy to install & test, I really recommend giving it a try on your next script or pet project!