←back to thread

Uv and Ray: Pain-Free Python Dependencies in Clusters

(www.anyscale.com)
43 points robertnishihara | 1 comments | 27 Jun 25 06:14 UTC | HN request time: 0.212s | source
Show context
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?

replies(4): >>44395089 #>>44395516 #>>44395563 #>>44395700 #
devjab ◴[27 Jun 25 08:56 UTC] No.44395089[source]▶
https://docs.astral.sh/uv/guides/integration/pytorch/#automa...

doesn't work?

replies(1): >>44395302 #
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
replies(2): >>44395469 #>>44395818 #
1. 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)