[pizza]

Somewhat related: starting a new Ubuntu-pytorch-cuda project? That’ll be 10-15 gigabytes, please. Is there some way to strip down the individual deps? I imagine it’s the way that it is because the drivers and the pytorch source are probably machine generated before compilation, to some extent. Is there any hope for triton becoming a lightweight interstitial frame in the codebase stack that can just allow me to do the hw codegen after pulling, without having to rebuild completely everything, and also simultaneously allowing more accelerators?

[kramerger]

Here is what really annoys me:

The dependencies are such a mess that even if you try to install only pytorch-cpu, at some point some random package will cause pytorch-cuda and those 10GBs to be installed.

[breakds]

I use Nix to manage my machine learning development environment: https://github.com/nixvital/ml-pkgs

Sure after the building the binary is HUGE. But I only have to build it once and cache it so that all my workstations and training servers can use it.