I’m looking to buy a new GPU. My main use case will be training and running neural nets (tensorflow+pytorch); gaming isn’t really a priority.
Thing is, I use wayland (via sway), and so I’d really prefer to get an AMD GPU. Nvidia doesn’t seem very linux friendly at the moment, especially when it comes to wayland unfortunately.
On the other hand, Nvidia seems to be the clear frontrunner right now when it comes to NN acceleration. I’m worried that if I got an AMD GPU to accelerate my NN work, I’d just be wasting my money.
What do you all think?
Edit: I’ve used GPUs to accelerate NN models in the past, but they weren’t my own, they were provided by my uni’s research infra and/or google collab. So this would be the first time I’d be using my own GPU hardware for this purpose.
Get something new enough and continue getting something new enough when AMD pushes them out. The drivers suck for anything older than an RX580, and things like Blender require even newer GPUs despite the hardware being more than capable.
Run Arch and use the ROCm’d PyTorch from the repos. Those packagers know what they’re doing.
Other than that, expect everything premade to be made for CUDA. There are some tools like https://github.com/ROCm-Developer-Tools/HIPIFY but they aren’t “there”.
Source: Been running Stable Diffusion on an RX580.
Thanks! Sounds doable but definitely frustrating… I’m surprised this is the state of things at the moment. I mean, when you buy a CPU, you don’t really think about whether your choice limits you in some ways. But with a GPU, it’s a big consideration.
Yeah GPUs never got standardized like x86 did from the old IBM machine days. GPUs are still operating on the mindset of “specific hardware” rather than something generic. If GPUs could be programmed on as easily as CPUs we could target something like vulkan for ML.
Even ARM faces similar, but different problems of the lack of standard boot methods.