101–110 of 312 posts
Yup, thank the hobbyists. Pytorch is allowing other hardware. Stable diffusion working on m chips, intel arc, and Amd. Now what I'd like to see is real benchmarks for compute power. Might even get a few startups to compete in this new area.
Re: startups, Geohotz raised a few million for this already. https://tinygrad.org/
Earlier quoted context omitted.
Didn't he do what he always does. Rake in a ton of money, fart around and then cash out exclaiming it's everyone else's fault? The way he stole Fail0verflow's work with the PS3 security leak after failing to find a hypervisor exploit for months absolutely soured any respect I had for him at the time
Yep, did exactly that. IMO he threw a fit, even though AMD was working with him squashing bugs. https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...
Earlier quoted context omitted.
Didn't he do what he always does. Rake in a ton of money, fart around and then cash out exclaiming it's everyone else's fault? The way he stole Fail0verflow's work with the PS3 security leak after failing to find a hypervisor exploit for months absolutely soured any respect I had for him at the time
Yep, did exactly that. IMO he threw a fit, even though AMD was working with him squashing bugs. https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...
I was able to use ROCm recently with Pytorch and after pulling some hair it worked quite well. The Radeon GPU I had on hand was a bit old and underpowered (RDNA2) and it only supported matmul on fp64, but for the job I needed done I saw a 200x increase in it/s over CPU despite the need to cast everywhere, and that made me super happy. Best of all is that I simply set the device to `torch.device('cuda')` rather than o…
Man oh man where did we go wrong that cuda is the more compatible option over OpenCL?
I was able to use ROCm recently with Pytorch and after pulling some hair it worked quite well. The Radeon GPU I had on hand was a bit old and underpowered (RDNA2) and it only supported matmul on fp64, but for the job I needed done I saw a 200x increase in it/s over CPU despite the need to cast everywhere, and that made me super happy. Best of all is that I simply set the device to `torch.device('cuda')` rather than o…
Sigh. It's great that these container images exist to give people an easy on-ramp, but they definitely don't work for every use case (especially once you're in embedded where space matters and you might not be online to pull multi-gb updates from some registry). So it's important that vendors don't feel let off the hook to provide sane packaging just because there's an option to use a kitchen-sink container image the…
https://github.com/RadeonOpenCompute/ROCm-docker/blob/master...
They also have some for Fedora. Looks like for this you need to install their repo:
curl -sL https://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - \
&& printf "deb [arch=amd64] https://repo.radeon.com/rocm/apt/$ROCM_VERSION/ jammy main" | tee /etc/apt/sources.list.d/rocm.list \
&& printf "deb [arch=amd64] https://repo.radeon.com/amdgpu/$AMDGPU_VERSION/ubuntu jammy main" | tee /etc/apt/sources.list.d/amdgpu.list \
So they are doing the packaging. There might even be documentation elsewhere for that type of setup.
Earlier quoted context omitted.
I use a rolling distro (OpenSUSE Tumbleweed) and have had zero issues with my NVIDIA card despite it pulling the kernel and driver updates as they get released. The driver repo is maintained by NVIDIA itself, which is amazing.
Do you use wayland, multiple monitors, and/or play games or is it just for ML/AI?
I'm not here to desparage anyone experiencing issues, but my experience on the NixOS rolling-release channel has also been pretty boring. There was a time when my old 1050 Ti struggled, but the modern upstream drivers feel just as smooth as my Intel system does.
People complain about Nvidia being anticompetitive with CUDA, but I don't really see it. They saw a gap in the standards for on-GPU compute and put tons of effort into a proprietary alternative. They tied CUDA to their own hardware, which sorta makes technical sense given the optimizations involved, but it's their choice anyway. They still support the open standards, but many prefer CUDA and will pay the Nvidia premi…
I was able to use ROCm recently with Pytorch and after pulling some hair it worked quite well. The Radeon GPU I had on hand was a bit old and underpowered (RDNA2) and it only supported matmul on fp64, but for the job I needed done I saw a 200x increase in it/s over CPU despite the need to cast everywhere, and that made me super happy. Best of all is that I simply set the device to `torch.device('cuda')` rather than o…
> Best of all is that I simply set the device to `torch.device('cuda')` rather than openCL, which does wonders for compatibility Man oh man where did we go wrong that cuda is the more compatible option over OpenCL?
AMD should just get it's shit together. This is ridiculous. Not the name, but the fact that you can only do FP64 on a GPU. Everybody is moving to FP16 and AMD is stuck on doubles?
CUDA is the only reason I have an Nvidia card, but if more projects start migrating to a more agnostic environment, I'll be really grateful. Running Nvidia in Linux isn't as much fun. Fedora and Debian can be incredibly reliable systems, but when you add an Nvidia card, I feel like I am back in Windows Vista with kernel crashes from time to time.
I see these complains from time to time and I never understand them. I've literally been running nvidia on linux since the TNT2 days and have _never_ had this sort of issue. That's across many drivers and many cards over the many many years.
My guess: something like laptop GPU switching failed badly in the nvidia binary, earning it a reputation.
Yup, thank the hobbyists. Pytorch is allowing other hardware. Stable diffusion working on m chips, intel arc, and Amd. Now what I'd like to see is real benchmarks for compute power. Might even get a few startups to compete in this new area.
Pytorch is just using Google's OpenXLA now, & OpenXLA is the actual cross platform thing, no? I'm not very well versed in this area, so pardon if mistaken. https://pytorch.org/blog/pytorch-2.0-xla-path-forward/
If you use model.compile() in PyTorch, you use TorchInductor and OpenAIs Triton by default.