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As a person who has successfully used uv for ml workloads, I'm curious what makes you still stay with Conda.
by miki123211 1y ago
As a person who has successfully used uv for ml workloads, I'm curious what makes you still stay with Conda.
- disgruntledphd2 1y agoConda is much, much better for the C/Fortran/C++ parts of data science/machine learning workloads. Like, I had real issues with GDAL and SQLite/spatialite on MacOS (easy on Linux) which uv was of no help with. I ended up re-architecting rather than go back to conda as uv is just that good (and it was early stage so wasn't that big of a deal).
- insane_dreamer 1y agocuda-nvcc was a blocker for us but it looks like a stable version is coming to pypi
- blactuary 1y agoI stay with it because last time I tried uv it was still directory/project focused vs. environment. With conda it doesn't matter where I am, I can activate any of my environments and run code