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> Nothing actually works on non-NVidia hardware. "Actually works" is different from "theoretically works." A lot of what I use theoretically supports ROCm, but

> Nothing actually works on non-NVidia hardware. "Actually works" is different from "theoretically works." A lot of what I use theoretically supports ROCm, but in practice, when you try to use it, you have enough bugs big and small, and things crash or don't work right. I was surprised when I started playing around with Stable Diffusion a couple of weeks ago and it just kind of worked with ROCm. That being said, I did run into a couple of very annoying issues... almost running out of VRAM seems to trigger GPU timeouts but as far as I can tell the GPU itself is fine and the memory allocation crawled to a halt. This also seems to be much more likely when running at half precision, which is a shame given that the relatively large 16GB VRAM effectively gets cut in half when running at full precision... But this is already a big improvement over "ROCm doesn't support RDNA2" [0] which was still the case ~1.5 years ago. I just really hope that AMD keeps up the effort and fixes all the small and big issues that are still around... [0]: https://github.com/RadeonOpenCompute/ROCm/issues/1180 https://github.com/RadeonOpenCompute/ROCm/issues/1180

blagie 4y ago
> I was surprised when I started playing around with Stable Diffusion a couple of weeks ago and it just kind of worked with ROCm. Stable Diffusion is literally the single highest-profile application of machine learning. There has been extensive effort to make it work in all sorts of places. If it were the year 2000, someone would port it to a Beowulf cluster... I'm more concerned about SpaCy, CuPy, the hundreds (thousands) of models on Hugging Face, etc. I do use a lot of that stuff. Buying a GPU or accelerator which only runs the high-profile stuff is a bad buy.
- HansHamster 4y ago
  The other models on Hugging Face I tried also worked fine... I'm not saying that everything is running perfectly or even that buying an AMD card for compute is a good idea at the moment, but there has been quite some progress even compared to 2-3 years ago so I'm cautiously optimistic... I still remember trying to train a model with TensorFlow on AMD maybe 2 years ago. The only option was OpenCL and it was disappointingly slow (and barely any load on the GPU) and in the end I switched back to my old 1060 which worked much better.
Scaevolus 4y ago
ROCm also has significant startup latency, and for Stable Diffusion additional latency compiling the new operations whenever you switch resolutions. I found that running out of memory would lock up my X session, spam kernel logs about GPU ioctls timing out, and require a reboot. That's much less friendly than CUDA reporting an error to the requesting application and carrying on!

blagie 4y ago

> I was surprised when I started playing around with Stable Diffusion a couple of weeks ago and it just kind of worked with ROCm. Stable Diffusion is literally the single highest-profile application of machine learning. There has been extensive effort to make it work in all sorts of places. If it were the year 2000, someone would port it to a Beowulf cluster... I'm more concerned about SpaCy, CuPy, the hundreds (thousands) of models on Hugging Face, etc. I do use a lot of that stuff. Buying a GPU or accelerator which only runs the high-profile stuff is a bad buy.

HansHamster 4y ago
The other models on Hugging Face I tried also worked fine... I'm not saying that everything is running perfectly or even that buying an AMD card for compute is a good idea at the moment, but there has been quite some progress even compared to 2-3 years ago so I'm cautiously optimistic... I still remember trying to train a model with TensorFlow on AMD maybe 2 years ago. The only option was OpenCL and it was disappointingly slow (and barely any load on the GPU) and in the end I switched back to my old 1060 which worked much better.

Scaevolus 4y ago

ROCm also has significant startup latency, and for Stable Diffusion additional latency compiling the new operations whenever you switch resolutions. I found that running out of memory would lock up my X session, spam kernel logs about GPU ioctls timing out, and require a reboot. That's much less friendly than CUDA reporting an error to the requesting application and carrying on!