Comment by baby_souffle
17 hours ago
I largely agree but don't want to entirely discount the effect that using a compiled language had.
At least in my limited experience, the selling point with the most traction is that you don't already need a working python install to get UV. And once you have UV, you can just go!
If I had a dollar for every time I've helped somebody untangle the mess of python environment libraries created by an undocumented mix of python delivered through the distributions package management versus native pip versus manually installed...
At least on paper, both poetry and UV have a pretty similar feature set. You do however need a working python environment to install and use poetry though.
> the selling point with the most traction is that you don't already need a working python install to get UV. And once you have UV, you can just go!
I still genuinely do not understand why this is a serious selling point. Linux systems commonly already provide (and heavily depend upon) a Python distribution which is perfectly suitable for creating virtual environments, and Python on Windows is provided by a traditional installer following the usual idioms for Windows end users. (To install uv on Windows I would be expected to use the PowerShell equivalent of a curl | sh trick; many people trying to learn to use Python on Windows have to be taught what cmd.exe is, never mind PowerShell.) If anything, new Python-on-Windows users are getting tripped up by the moving target of attempts to make it even easier (in part because of things Microsoft messed up when trying to coordinate with the CPython team; see for example https://stackoverflow.com/questions/58754860/cmd-opens-windo... when it originally happened in Python 3.7).
> If I had a dollar for every time I've helped somebody untangle the mess of python environment libraries created by an undocumented mix of python delivered through the distributions package management versus native pip versus manually installed...
Sure, but that has everything to do with not understanding (or caring about) virtual environments (which are fundamental, and used by uv under the hood because there is really no viable alternative), and nothing to do with getting Python in the first place. I also don't know what you mean about "native pip" here; it seems like you're conflating the Python installation process with the package installation process.
Linux systems commonly already provide an outdated system Python you don’t want to use, and it can’t be used to create a venv of a version you want to use. A single Python version for the entire system fundamentally doesn’t work for many people thanks to shitty compat story in the vast ecosystem.
Even languages with great compat story are moving to support multi-toolchains natively. For instance, go 1.22 on Ubuntu 24.04 LTS is outdated, but it will automatically download the 1.25 toolchain when it seems go 1.25.0 in go.mod.
> Linux systems commonly already provide an outdated system Python you don’t want to use
They can be a bit long in the tooth, yes, but from past experience another Python version I don't want to use is anything ending in .0, so I can cope with them being a little older.
That's in quite a bit of contrast to something like Go, where I will happily update on the day a new version comes out. Some care is still needed - they allow security changes particularly to be breaking, but at least those tend to be deliberate changes.
> Linux systems commonly already provide an outdated system Python you don’t want to use
Even with LTS Ubuntu updated only at EOL, Python will not be EOL most of the time.
> A single Python version for the entire system fundamentally doesn’t work for many people thanks to shitty compat story in the vast ecosystem.
My experience has been radically different. Everyone is trying their hardest to provide wheels for a wide range of platforms, and all the most popular projects succeed. Try adding `--only-binary=:all:` to your pip invocations and let me know the next time that actually causes a failure.
Besides which, I was very specifically talking about the user story for people who are just learning to program and will use Python for it. Because otherwise this problem is trivially solved by anyone competent. In particular, building and installing Python from source is just the standard configure / make / make install dance, and it Just Works. I have done it many times and never needed any help to figure it out even though it was the first thing I tried to build from C source after switching to Linux.
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Why not just use a Python container rather than rely on having the latest binary installed on the system? Then venv inside the container. That would get you the “venv of a version” that you are referring to
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So basically, it avoids the whole chicken-and-egg problem. With UV you've simply always got "UV -> project Python 1.23 -> project". UV is your dependency manager, and your Python is just another dependency.
With other dependency managers you end up with "system Python 3.45 -> dep manager -> project Python 1.23 -> project". Or worse, "system Python 1.23 -> dep manager -> project Python 1.23 -> project". And of course there will be people who read about the problem and install their own Python manager, so they end up with a "system Python -> virtualenv Python -> poetry Python -> project" stack. Or the other way around, and they'll end up installing their project dependencies globally...
Sorry, but that is simply incorrect, on many levels.
Virtual environments are the fundamental way of setting up a Python project, whether or not you use uv, which creates and manages them for you. And these virtual environments can freely either use or not use the system environment, whether or not you use uv to create them. It's literally a single-line difference in the `pyvenv.cfg` file, which is a standard required part of the environment (see https://peps.python.org/pep-0405/), created whether or not you use uv.
Most of the time you don't need a different Python version from the system one. When you do, uv can install one for you, but it doesn't change what your dependency chain actually is.
Python-native tools like Poetry, Hatch etc. also work by managing standards-defined virtual environments (which can be created using the standard library, and you don't even have to bootstrap pip into them if you don't want to) in fundamentally the same way that uv does. Some of them can even grab Python builds for you the same way that uv does (of course, uv doesn't need a "system Python" to exist first). "system Python -> virtualenv Python -> poetry Python -> project" is complete nonsense. The "virtualenv Python" is the system Python — either a symlink or a stub executable that launches that Python — and the project will be installed into that virtual environment. A tool like Poetry might use the system Python directly, or it might install into its own separate virtual environment; but either way it doesn't cause any actual complication.
Anyone who "ends up installing their project dependencies globally" has simply not read and understood Contemporary Python Development 101. In fact, anyone doing this on a reasonably new Linux has gone far out of the way to avoid learning that, by forcefully bypassing multiple warnings (such as described in https://peps.python.org/pep-0668/).
No matter what your tooling, the only sensible "stack" to end up with, for almost any project, is: base Python (usually the system Python but may be a separately installed Python) -> virtual environment (into which both the project and its dependencies are installed). The base Python provides the standard library; often there will be no third-party libraries, and even if there are they will usually be cut off intentionally. (If your Linux comes with pre-installed third-party libraries, they exist primarily to service tools that are part of your Linux distribution; you may be able to use them for some useful local hacking, but they are not appropriate for serious, publishable development.)
Your tooling sits parallel to, and isolated from, that as long as it is literally anything other than pip — and even with pip you can have that isolation (it's flawed but it works for common cases; see for example https://zahlman.github.io/posts/2025/02/28/python-packaging-... for how I set it up using a vendored copy of pip provided by Pipx), and have been able to for three years now.
> Most of the time you don't need a different Python version from the system one.
Except for literally anytime you’re collaborating with anyone, ever? I can’t even begin to imagine working on a project where folks just use whatever python version their OS happens to ship with. Do you also just ship the latest version of whatever container because most of the time nothing has changed?
> has simply not read and understood Contemporary Python Development 101.
They haven't. At the end of the day, they just want their program to work. You and I can design a utopian packaging system, but the physics PhD with a hand-me-down windows laptop and access to her university's Linux research cluster don't care about python other than it has a PITA library situation that UV addresses.
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1000% this. uv is trivially installable and is completely unrelated to installations of python.
If I want to install Python on Windows and start using pip, I grab an installer from python.org and follow a wizard. On Linux, I almost certainly already have it anyway.
If I want to bootstrap from uv on Windows, the simplest option offered involves Powershell.
Either way, I can write quite a bit with just the standard library before I have to understand what uv really is (or what pip is). At that point, yes, the pip UX is quite a bit messier. But I already have Python, and pip itself was also trivially installable (e.g. via the standard library `ensurepip`, or from a Linux system package manager — yes, still using the command line, but this hypothetical is conditioned on being a Linux user).
Not many normal people want to install python. Instead, author of the software they are trying to use wants them to install python. So they follow readme, download windows installer as you say, pip this pipx, pipx that conda, conda this requirements.txt, and five minutes later they have magic error telling that tensorflow version they are installing is not compatible with pytorch version they are installing or some such.
The aftertaste python leaves is lasting-disgusting.
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Traditional Windows install didn’t include things Microsoft doesn’t make. But, any PC distributor could always include Python as part of their base Windows install with all the other stuff that bloats the typical third party Windows installs. They don’t which indicates the market doesn’t want it. Your indictment of the lack of Python out of the box is less on Windows than on the “distro” served by PC manufacturers
I wonder how much Rust's default to statically link almost everything helped here? That should make deployment of uv even easier?
I don't think this makes a meaningful difference. The installation is a `curl | sh`, which downloads a tarball, which gets extracted to some directory in $PATH.
It currently includes two executables, but having it contain two executables and a bunch of .so libraries would be a fairly trivial change. It only gets messy when you want it to make use of system-provided versions of the libraries, rather than simply vendoring them all yourself.
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