Hello y’all, i was using this guide to try and set up llama again on my machine, i was sure that i was following the instructions to the letter but when i get to the part where i need to run setup_cuda.py install i get this error
File "C:\Users\Mike\miniconda3\Lib\site-packages\torch\utils\cpp_extension.py", line 2419, in _join_cuda_home raise OSError('CUDA_HOME environment variable is not set. ' OSError: CUDA_HOME environment variable is not set. Please set it to your CUDA install root. (base) PS C:\Users\Mike\text-generation-webui\repositories\GPTQ-for-LLaMa>
i’m not a huge coder yet so i tried to use setx to set CUDA_HOME to a few different places but each time doing echo %CUDA_HOME doesn’t come up with the address so i assume it failed, and i still can’t run setup_cuda.py
Anyone have any idea what i’m doing wrong?
I mean Linux is an option but haven’t people been saying nvida drivers are a huge hassle to use on Linux?
Nah. There are some nvidia issues with wayland (that are starting to get cleared up), and nvidia’s drivers not being open-source rubs some people the wrong way, but getting nvidia and cuda up and running on linux is pretty easy/reliable in my experience.
WSL is a bit different but there are steps to get that up and running too.
They can be, I suppose. However, the AI libraries that I was tinkering with seemed to all be based around Ubuntu and Nvidia. With Docker, GPU passthrough is much better under Linux and Nvidia.
WSL improved things a bit after I got an older GTX 1650. For my AMD GPU, ROCm support is (was?) garbage under Windows using either Docker or WSL. I don’t remember having much difficulty with Nvidia drivers though… I think there might have been some strange dependency problems I was able to work through though.
AMD GPU passthrough on Windows to Docker containers was a no-go. I remember that fairly clear though.
My apologies. It has been a few months since I messed with this stuff.