Good writeup. PyTorch has generally been very good to me when I can mitigate its resource hogging at times. Production can be a little wonky but for everything else it works

Note that https://peps.python.org/pep-0440/#direct-references says:

> Public index servers SHOULD NOT allow the use of direct references in uploaded distributions. Direct references are intended as a tool for software integrators rather than publishers.

This means that PyPI will not accept your project metadata as you currently have it configured. See https://github.com/pypi/warehouse/issues/7136 for more details.

> Cross-Platform

  cpu = [
  "torch @ <https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl> ; python_version == '3.12'",
  "torch @ <https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl> ; python_version == '3.13'",
  ]

:-/ It reminds me of Microsoft calling their thing "cross platform" because it works on several copies of Windows

In all seriousness, I get the impression that pytorch is such a monster PITA to manage because it cares so much about the target hardware. It'd be like a blog post saying "I solved the assembly language nightmare"

> Setting up a Python project that relies on PyTorch, so that it works across different accelerators and operating systems, is a nightmare.

I would like to add some anecdata to this.

When I was a PhD student, I already had 12 years of using and administrating Linuxes as my personal OS, and I'd already had my share of package manager and dependency woes.

But managing Python, PyTorch, and CUDA dependencies were relatively new to me. Sometimes I'd lose an evening here or there to something silly. But I had one week especially dominated by these woes, to the point where I'd have dreams about package management problems at the terminal.

They were mundane dreams but I'd chalk them up as nightmares. The worst was having the pleasant dream where those problems went away forever, only to wake up to realize that was not the case.

In my opinion, anything that touch compiled packages like pytorch should be packaged with conda/mamba on conda-forge. I found it is the only package manager for python which will reliably detect my hardware and install the correct version of every dependency.