So I’m no expert, but I have been a hobbyist C and Rust dev for a while now, and I’ve installed tons of programs from GitHub and whatnot that required manual compilation or other hoops to jump through, but I am constantly befuddled installing python apps. They seem to always need a very specific (often outdated) version of python, require a bunch of venv nonsense, googling gives tons of outdated info that no longer works, and generally seem incredibly not portable. As someone who doesn’t work in python, it seems more obtuse than any other language’s ecosystem. Why is it like this?
It… depends. There is some great tooling for Python – this was less true only a few years ago, mind you – but the landscape is very much in flux, and usage of the modern stuff is not yet widespread. And a lot of the legacy stuff has a whole host of pitfalls.
Things are broadly progressing in the right direction, and I’d say I’m cautiously optimistic, although if you have to deal with anything related to conda then for the time being: good luck, and sorry.
The reason you do stuff in a venv is to isolate that environment from other python projects on your system, so one Python project doesn’t break another. I use Docker for similar reasons for a lot of non-Python projects.
A lot of Python projects involve specific versions of libraries, because things break. I’ve had similar issues with non-Python projects. I’m not sure I’d say Python is particularly worse about it.
There are tools in place that can make the sharing of Python projects incredibly easy and portable and consistent, but I only ever see the best maintained projects using them unfortunately.
I’m not sure this can be really fixed with Python 3, maybe we just have to hope for Python 4
It’s fixed, and the python version had nothing to do with it. Just use hatch
Ah yes, the 15th standard we’ve been waiting for!
It’s not a standard, it’s built on standards.
You can also use Poetry (which recently grew standard metadata support) or plain
uv venvif you want to do things manually but fast.Just use this one… or any of this 4 others.
This is the issue for us, python outsiders. Each time we try we get a different answer with new tools. We are outside of the comtunity, we don’t know the trend, old and new, pro and cons.
Your first recommandation is hatch… first time I’ve heard of it. Uv seems trendy in this thread, but before that it was unknown to me too.
As I understands it, it should be pip’s job. When it detect I’m in a project it install packages in it and python use them. It can use any tool under the hood, but the default package manager shoud be able to do it on its own.
Uv and pip do the same thing, uv is just faster.
Hatch has the same role as Poetry or tox: managing environments for you.
Applications should be packaged properly, in a self contained installer for exactly this demographic. It’s not Python’s fault that this isn’t common practice.
Python developer here. Venv is good, venv is life. Every single project I create starts with
python3 -m venv venv
source venv/bin/activate
pip3 install {everything I need}
pip3 freeze > requirements.txt
Now write code!
Don’t forget to update your requirements.txt using pip3 freeze again anytime you add a new library with pip.
If you installed a lot of packages before starting to develop with virtual environments, some libraries will be in your OS python install and won’t be reflected in pip freeze and won’t get into your venv. This is the root of all evil. First of all, don’t do that. Second, you can force libraries to install into your venv despite them also being in your system by installing like so:
pip3 install --ignore-installed mypackage
If you don’t change between Linux and windows most libraries will just work between systems, but if you have problems on another system, just recreate the whole venv structure
rm -rf venv (…make a new venv, activate it) pip3 install -r requirements.txt
Once you get the hang of this you can make Python behave without a lot of hassle.
This is a case where a strength can also be a weakness.
OP sounds like a victim of Python 3, finding various Python 2 projects on the internet, a venv isn’t going to help
pip3 freeze > requirements.txt
I hate this. Because now I have a list of your dependencies, but also the dependencies of the dependencies, and I now have regular dependencies and dev-dependencies mixed up. If I’m new to Python I would have NO idea which libraries would be the important ones because it’s a jumbled mess.
I’ve come to love
uv(coming frompoetry, coming frompipwith arequirements/base.txtandrequirements/dev.txt- gotta keep regular dependencies and dev-dependencies separate).uv syncuv run <application>That’s it. I don’t even need to install a compatible Python version, as
uvtakes care of that for me. It’ll automatically create a local.venv/, and it’s blazingly fast.I’ve never really spent much time with uv, I’ll give it a try. It seems like it takes a few steps out of the process and some guesswork too.
Okay, now give me those steps but what to do if I clone an already existing repo please
The git repo should ignore the venv folder, so when you clone you then create a new one and activate it with those steps.
Then when you are installing requirements with pip, the repo you cloned will likely have a requirements.txt file in it, so you ‘pip install -r requirements.txt’
You have been in lala land for too long. That list of things to do is insane. Venv is possibly one of the worst solutions around, but many Python devs are incapable of seeing how bad it is. Just for comparison, so you can understand, in Ruby literally everything you did is covered by one command
bundle. On every system.This is the way
This is the way.
I agree. Python is my language of choice 80% or so of the time.
But my god, it does packaging badly! Especially if it’s dependent on linking to compiled code!
Why it is like that, I couldn’t tell. The language is older than git, so that might be part of it.
However, you’re installing python libraries from github? I very very rarely have to do that. In what context do you have to do that regularly?
Yeah the tooling sucks. The only tooling I’ve liked is Poetry, I never have trouble installing or packaging the apps that use it.
This isn’t the answer you want, but Go(lang) is super easy to learn and has a ton of speed on python. Yes, it’s more difficult, but once you understand it, it’s got a lot going for it.
it’s also not at all relevant. go is great, but this is about python.
I’m sorry I offended you.
this is not about offense! nobody is offended. but if you ask me for help with an apple pie and i tell you to make meatballs… it’s a confusing lack of relevance.
I did lead with an appropriate request for a sidebar. I just feel the rip about context was even less appropriate. And apple cobbler would be a better comparison. Apples, just different.
it’s not though. op has issues installing programs built in python. suggesting they rebuild those programs in go is 100% an apples to meatballs comparison, and way off topic.
They should get those same programs, but for Go. I’m sure someone has made whatever they’re doing. It would work better.
such a weird take.
You’re not wrong, but you have offended the python guys for suggesting they use something other than their toy language.
I personally look away when I find programs I want to use that are written in python. I don’t have time to play with all that BS just to run a small software on my machine. Go is my go-to (heh) but any other modern language would be fine.
The difficulty with python tooling is that you have to learn which tools you can and should completely ignore.
Unless you are a 100x engineer managing 500 projects with conflicting versions, build systems, docker, websites, and AAAH…
- you don’t really need venvs
- you should not use more than on package manager (I recommend pip) and you should cling to it with all your might and never switch. Mixing e.g. conda, on linux system installers like apt, is the problem. Just using one is fine.
- You don’t “need” need any other tools. They are bonuses that you should use and learn how to use, exactly when you need them and not before. (type hinting checker, linting, testing, etc…)
Why is it like this?
Isolation for reliability, because it costs the businesses real $$$ when stuff goes down.
venvs exists to prevent the case that “project 1” and “project 2” use the same library “foobar”. Except, “project 1” is old, the maintainer is held up and can’t update as fast and “project 2” is a cutting edge start up that always uses the newest tech.
When python imports a library it would use “the libary” that is installed. If project 2 uses foobar version 15.9 which changed functionality, and project 1 uses foobar uses version 1.0, you get a bug, always, in either project 1 or project 2. Venvs solve this by providing project specific sets of libraries and interpreters.
In practice for many if not most users, this is meaningless, because if you’re making e.g. a plot with matplotlib, that won’t change. But people have “best practices” so they just do stuff even if they don’t need it.
It is a tradeoff between being fine with breakage and fixing it when it occurs and not being fine with breakage. The two approaches won’t mix.
very specific (often outdated) version of python,
They are giving you the version that they know worked. Often you can just remove the specific version pinning and it will work fine, because again, it doesn’t actually change that much. But still, the project that’s online was the working state.
Coming at this from the JS world… Why the heck would 2 projects share the same library? Seems like a pretty stupid idea that opens you up to a ton of issues, so what, you can save 200kb on you hard drive?
Why the heck would 2 projects share the same library?
Coming from the olden days, with good package management, infrequent updates and the idea that you wanted to indeed save that x number of bytes on the disk and in memory, only installing one was the way to go.
Python also wasn’t exactly a high brow academic effort to brain storm the next big thing, it was built to be a simple tool and that included just fetching some library from your system was good enough. It only ended up being popular because it is very easy to get your feet wet and do something quick.
Yeah, not sure I would listen to this guy. Setting up a venv for each project is about a bare minimum for all the teams I’ve worked on.
That being said python env can be GBs in size (especially when doing data science).
especially when doing data science
500MB for Ray, another 500MB for Polars (though that was a bug IIRC), a few more megs for whatever binaries to read out those weird weather files (NetCDF and Grib2).
The venv stuff is pretty annoying, I agree.
As a baby Python Dev, I’m glad it’s not just me.
I’ve been full time writing python professionally since 2015. You get used to it. It starts to just make sense and feel normal. Then when you move to a different language environment you wonder why their tooling doesn’t use a virtualenv.
Yes it’s terrible. The only hope on the horizon is
uv. It’s significantly better than all the other tooling (Poetry, pip, pipenv, etc.) so I think it has a good chance of reducing the options to just Pip oruvat least.But I fully expect the Python Devs to ignore it, and maybe even make life deliberately difficult for it like they did for static analysers. They have some strange priorities sometimes.
uv is good but it needs a little more time in the oven.
For the moment I would definitely recommend poetry if you are not a library developer. Poetry’s biggest sin is it’s atrocious performance but it has most of the features you need to work with Python apps today.
Why do you say it needs more time in the oven? I’ve had zero issues with it as a drop-in replacement for Pip in a large commercial project, which is an extremely impressive achievement. (And it was 10x faster.)
I tried Poetry once and it failed to resolve dependencies on the first thing I tried it on. If anything Poetry needs more time in the oven. It also wasn’t 10x faster.
I like the idea of
uv, but I hate the name. Libuv is already a very popular C library, and used in everything from NodeJS to Julia to Python (through the popularuvloopmodule). Every time I see someone mentionuvI get confused and think they’re talking about uvloop until I remember the Astral project, and then reconfirm to myself how much I disapprove of their name choice.I don’t think
libuvis really that popular, nor is it that confusing.But I do agree it’s not a very good name. “Rye” is a much better name. Probably too late anyway.
UV is a game changer for python.
I hated the tooling until I found it.
Python’s packaging is not great. Pip and venvs help but, it’s lightyears behind anything you’re used to. My go-to is using a venv for everything.
venv nonsense
I mean, the fact that it isn’t more end-user invisible to me is annoying, and I wish that it could also include a version of Python, but I think that venv is pretty reasonable. It handles non-systemwide library versioning in what I’d call a reasonably straightforward way. Once you know how to do it, works the same way for each Python program.
Honestly, if there were just a frontend on venv that set up any missing environment and activated the venv, I’d be fine with it.
And I don’t do much Python development, so this isn’t from a “Python awesome” standpoint.
pyenv and uv let you install and switch between multiple Python versions.
As for uv, those come from the Python build standalone project, if I remember correctly, pyenv also installs from there, but don’t quote me on that.
It’s something of a “14 competing standards” situation, but uv seems to be the nerd favourite these days.
This! Haven’t used that one personally, but seeing how good ruff is I bet it’s darn amazing, next best thing that I used has been PDM and Poetry, because Python’s first party tooling has always been lackluster, no cohesive way to define a project and actually work it until relatively recently
I bet it’s darn amazing,
It is. In this older article (by Anna-Lena Popkes) uv is still not in the middle, but I would claim it’s the new King of Project Management, when it comes to Python.
uv init --name <some name> --package --appand you’re off to the races.Are you cloning a repo that’s
uv-enabled? Justuv syncand you’re done!Heck, you can now add dependencies to a script and just
uv run --script script.py(IIRC) and you don’t need to install anything -uvwill take care of it all, including a needed Python version.Only downside is that it’s not 1.0 yet, so the API can change at any update. That is the last hurdle for me.
I moved all our projects (and devs) from poetry to uv. Reasons were poetry’s non standard pyproject.toml syntax and speed, plus some weird quirks, e. g. if poetry asks for input and is not run with the verbose flag, devs often don’t notice and believe it is stuck (even though it’s in the default project README).
Personally, I update uv on my local machine as soon as a new release is available so I can track any breaking changes. Couple of months in, I can say there were some hiccups in the beginning, but currently, it’s smooth sailing, and the speed gain really affects productivity as well, mostly due to being able to not break away from a mental “flow” state while staring at updates, becoming suspicious something might be wrong. Don’t get me wrong, apart from the custom syntax (poetry partially predates the pyproject PEP), poetry worked great for us for years, but uv feels nicer.
Recently, “uv build” was introduced, which simplified things. I wish there was an command to update the lock file while also updating the dependency specs in the project file. I ran some command today and by accident discovered that custom dependency groups (apart from e. g. “dev”) have made it to uv, too.
“uv pip” does some things differently, in particular when resolving packages (it’s possible to switch to pip’s behavior now), but I do agree with the decisions, in particular the changes to prevent “dependency confusion” attacks.
As for the original question: Python really has a bit of a history of project management and build tools, I do feel however that the community and maintainers are finally getting somewhere.
cargo is a bit of an “unfair” comparison since its development happened much more aligned with Rust and its whole ecosystem and not as an afterthought by third party developers, but I agree: cargo is definitely a great benchmark how project and dependency management plus building should look like, along with rustup, it really makes the developer experience quite pleasant.
The need for virtual environments exists so that different projects can use different versions of dependencies and those dependencies can be installed in a project specific location vs a global, system location. Since Python is interpreted, these dependencies need to stick around for the lifetime of the program so they can be imported at runtime. poetry managed those in a separate folder in e. g. the user’s cache directory, whereas uv for example stores the virtual environment in the project folder, which I strongly prefer.
cargo will download the matching dependencies (along with doing some caching) and link the correct version to the project, so a conceptual virtual environment doesn’t need to exist for Rust. By default, rust links everything apart from the C runtime statically, so the dependencies are no longer neesed after the build - except you probably want to rebuild the project later, so there is some caching.
Finally, I’d also recommend to go and try setting up a project using astral’s uv. It handles sane pyproject.toml files, will create/initialize new projects from a template, manages virtual environments and has CLI to build e. g. wheels or source distribution (you will need to specify which build backend to use. I use hatchling), but thats just a decision you make and express as one line in the project file. Note: hatchling is the build backend, hatch is pypa’s project management, pretty much an alternative to poetry or uv.
uv will also install complete Python distributions (e. g. Python 3.12) if you need a different interpreter version for compatibility reasons
If you use workspaces in cargo, uv also does those.
uv init, uv add, uv lock --upgrade, uv sync, uv build and how uv handles tools you might want to install and run should really go a long way and probably provide an experience somewhat similar to cargo.
I think you responded to the wrong comment, I didn’t question the need for uv or other tools like that
I did that on purpose, i. e. I wanted to confirm your thoughts about uv, drifted off into a general rant, remembered OP’s original question and later realized it would have been better framed as a top level comment. In my defense, I was in an altered state of mind at the time.
I still do the python3 -m venv venv && source venv/bin/activate
How can uv help me be a better person?
- let
pyproject.tomltrack the dependencies and dev-dependencies you actually care about
- dependencies are what you need to run your application
- dev-dependencies are not necessary to run your app, but to develop it (formatting, linting, utilities, etc)
- it can track exactly what’s needed ot run the application via the
uv.lockfile that contains each and every lib that’s needed. - uv will install the needed Python version for you, completely separate from what your system is running.
uv syncanduv run <application>is pretty much all you need to get going- it’s blazingly fast in everything
Thank you for explaining so clearly. Point 3 is indeed something I’ve ran into before!
- let
If you’re happy with your solution, that’s great!
uv combines a bunch of tools into one simple, incredibly fast interface, and keeps a lock file up to date with what’s installed in the project right now. Makes docker and collaboration easier. Its main benefit for me is that it minimizes context switching/cognitive load
Ultimately, I encourage you to use what makes sense to you tho :)
And pip install -r requirements.txt
Fuck it, I just use sudo and live with the consequences.
the software equivalent of leaving the dirt on your vegetables to harden your immune system
Oh no
Python never had much of a central design team. People mostly just scratched their own itch, so you get lots of different tools that do only a small part each, and aren’t necessarily compatible.
Tried to install Automatic1111 for Stable Diffusion in an Arch distrobox, and despite editing the .sh file to point to the older tarballed Python version as advised on Github, it still tells me it uses the most up to date one that’s installed system wide and thus can’t install pytorch. And that’s pretty much where my personal knowledge ends, and apparently that of those (i.e. that one person) on Github. ¯\_(ツ)_/¯
Always funny when people urge you to ask for help but no one ends up actually helping.
Lol this is exactly why I made this post. I ended up using ComfyUI instead which has other, different python issues, but I got it working (kinda, no GPU but it’s fine it works)
