I feel like a lot of the problems people encounter with Python are due to the non-Python dependencies that projects use and are mostly restricted to certain fields. My guess is that the people complaining are a mix of:

not on linux
using numpy or something similar that has non-python dependencies
trying to do machine learning, signal processing, algorithmic trading,...

Hopefully the next blog post will walk through examples of "this is the problem people encounter with existing tools, this is what it looks like, this is how it looks like with our tools, this is how it works". The "how it works" could be split off into another blog post too.

Right now, this blog post just made me aware of pyx but that's about it.

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[–] sugar_in_your_tea@sh.itjust.works 2 points 5 months ago (12 children)

not on Linux

We're on macOS using docker, which is basically Linux, and building numpy et al is a pain. Sometimes we have failures on our build pipelines (Linux), sometimes on our dev machines, and it always takes forever.

We do a little of everything:

signal processing
machine learning - to optimize our simulations
heavy math for simulations (lots of trig)
work with 3D models (we use Blender's bpy)

That said, we won't be paying for pyx. Our problems can be solved via building our dev docker images in our pipelines and just pulling them down for devs, we just haven't bothered because the above is somewhat rare.

I don't know who this is for, because rebuilding numpy et al should be somewhat rare, and the annoyance is usually only a few min when it happens.

[–] onlinepersona@programming.dev 2 points 5 months ago* (last edited 5 months ago) (11 children)

MacOS and docker is asking for trouble. It requires all kinds of hacks and awareness of where things are being built or mounted, which architecture is being emulated because it runs in a VM, memory limits, swapping, and so on. It's no surprise build times are a problem.

I imagine your build pipeline is for aarch64, you have made modifications to numpy itself (build flags or something), you're using a special distro in the pipelines, or the rebuild is unnecessary. For me numpy has always been a simple "pip install numpy" or "poetry install" when not on nixOS.

My guess is that if you used non-emulated Linux, your local build issues would be reduced and also sped up. But I can't say that with any certainty because I don't have nor require nvidia in my Linux rig, nor do I know your setup very well.

You do however fall quite nicely into the criteria I shared. Were you outside of that, python would probably be much much easier (aka no non-pytjon deps).

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[–] BatmanAoD@programming.dev 0 points 5 months ago (1 children)

MacOS and docker is asking for trouble.

That's a real problem! MacOS and Docker are extremely common for development, and a scripting language like Python should just work with them, not just at the language level (which is good enough) but in the ecosystem and tooling. The latter is what Astral is trying to improve.

[–] onlinepersona@programming.dev 1 points 5 months ago* (last edited 5 months ago) (1 children)

I'm not talking specifically Python. Just macOS and docker. That combination has proven time and time again to be a massive timesink in every place I've seen it used. Devs can spend days (not joking, I've seen this happen multiple times) getting it setup for whichever purpose.

It went from all kinds of fancy wrappers around QEMU to claims that Rosetta and Intel docker would work, to Apple's VMs, docker's proprietary VM (after they dropped qemu), Ubuntu's Multi-pass, and that's where I stopped following the news. Every single one of these solutions claimed to be the best and they all ran into such massive problems that they needed hacks and workarounds and "novel" solutions.

And these weren't even for niche or hardware related stuff. Simply mounting the repository's folder or mounting a node_module folder absolutely killed performance on Macs because it wasn't direct hardware access or filesystem access but everything had to go through a socket or some sort from the VM to Mac. Anything with many files or file access completely tanked: java, Kotlin, C/C++, Rust, anything compilation, JavaScript and it's modules, Perl too.

One would expect the logical conclusion to be "Mac isn't the best development platform for Linux deployables" but instead a few glossy, rounded corners later and people cling on to it and then blame the tools for not adapting to Mac hacks.

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[–] BatmanAoD@programming.dev 1 points 5 months ago

Well, that's fair; first-class support for running an actual Linux kernel, with the containerization support that implies, is one of the reasons I prefer Windows with WSL as a dev environment when using Docker (at least, in companies where the IT team won't just let you run native Linux).

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