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Uv and Ray: Pain-Free Python Dependencies in Clusters

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43 points robertnishihara | 4 comments | 27 Jun 25 06:14 UTC | HN request time: 0.784s | source
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lz400 ◴[27 Jun 25 08:36 UTC] No.44394984[source]▶
Unfortunately uv is usually insufficient for certain ML deployments in Python. It's a real pain to install pytorch/CUDA with all the necessary drivers and C++ dependencies so people tend to fall back to conda.

Any modern tips / life hacks for this situation?

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1. devjab ◴[27 Jun 25 08:56 UTC] No.44395089[source]▶
https://docs.astral.sh/uv/guides/integration/pytorch/#automa...

doesn't work?

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2. lz400 ◴[27 Jun 25 09:36 UTC] No.44395302[source]▶
the problem is that you still need to install all the low level stuff manually, conda does it automatically
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3. gcarvalho ◴[27 Jun 25 10:04 UTC] No.44395469[source]▶
I was pleasantly surprised to try the guide out and see that it just worked:

    λ uv venv && uv pip install torch --torch-backend=auto
    λ uv run python -c 'import torch; print(torch.cuda.is_available())'
    True
This is on Debian stable, and I don't remember doing any special setup other than installing the proprietary nvidia driver.
4. pcwelder ◴[27 Jun 25 11:11 UTC] No.44395818[source]▶
This script has been sufficient for me to configure gpu drivers on fresh ubuntu machines. It's just uv add torch after this.

https://cloud.google.com/compute/docs/gpus/install-drivers-g... (NOTE: not gcloud specific)