Uv and Ray: Pain-Free Python Dependencies in Clusters

https://www.anyscale.com/blog/uv-ray-pain-free-python-dependencies-in-clusters

44•robertnishihara•7mo ago

Comments

jessekv•7mo ago

It would be a fun callback if the demo was a factorial server:

https://joearms.github.io/#2013-11-21%20My%20favorite%20Erla...

lz400•7mo ago

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?

devjab•7mo ago

https://docs.astral.sh/uv/guides/integration/pytorch/#automa...

doesn't work?

lz400•7mo ago

the problem is that you still need to install all the low level stuff manually, conda does it automatically

gcarvalho•7mo ago

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.

pcwelder•7mo ago

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)

Kydlaw•7mo ago

You should give a try to https://pixi.sh/latest/ (I am not involve in the project).

They are a little more focus on scientific computing than uv, which is more general. They might be a better option in your case.

miohtama•7mo ago

Would it be possible to use Docker to manage native dependencies?

rsfern•7mo ago

Are there particular libraries that make your setup difficult? I just manually set the index and source following the docs (didn’t know about the auto backend feature) and pin a specific version if I really have to with `uv add “torch==2.4”`. This works pretty well for me for projects that use dgl, which heavily uses C++ extensions and can be pretty finicky about working with particular versions

This is in a conventional HPC environment, and I’ve found it way better than conda since the dependency solves are so much faster and I no longer experience PyTorch silently getting downgraded to cpu version of I install a new library. Maybe I’ve been using conda poorly though?

chatmasta•7mo ago

A whole blog post and nine comments and nobody has made the pun about UV Rays?!

OpenCiv3: Open-source, cross-platform reimagining of Civilization III

The Waymo World Model

How we made geo joins 400× faster with H3 indexes

A century of hair samples proves leaded gas ban worked

Monty: A minimal, secure Python interpreter written in Rust for use by AI

Show HN: Look Ma, No Linux: Shell, App Installer, Vi, Cc on ESP32-S3 / BreezyBox

Dark Alley Mathematics

Show HN: I spent 4 years building a UI design tool with only the features I use

Microsoft open-sources LiteBox, a security-focused library OS

Show HN: If you lose your memory, how to regain access to your computer?

Sheldon Brown's Bicycle Technical Info

Hackers (1995) Animated Experience

PC Floppy Copy Protection: Vault Prolok

Show HN: ARM64 Android Dev Kit

An Update on Heroku

Female Asian Elephant Calf Born at the Smithsonian National Zoo

Delimited Continuations vs. Lwt for Threads

Show HN: R3forth, a ColorForth-inspired language with a tiny VM

How to effectively write quality code with AI

Introducing the Developer Knowledge API and MCP Server

I spent 5 years in DevOps – Solutions engineering gave me what I was missing

Learning from context is harder than we thought

Understanding Neural Network, Visually

I now assume that all ads on Apple news are scams

FORTH? Really!?

I'm going to cure my girlfriend's brain tumor

Evaluating and mitigating the growing risk of LLM-discovered 0-days

WebView performance significantly slower than PWA

How virtual textures work

Show HN: Smooth CLI – Token-efficient browser for AI agents