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Comment by jchw | Hacker News Reader

I run Ryzen on Linux 5.5 on my desktop workstation at home. Admittedly I am always running the latest kernel as long as I reboot often enough, but it has been good. I also use an AMD GPU, and that has been very nice. Intel probably still has an edge on supporting the Linux graphics stack but not by much, and AMD GPUs have a lot more muscle, so it’s not a bad trade off imo. Anything to avoid NVIDIA really.

(To those who love NVIDIA on Linux: I get it. However, Linux needs to move on from X11 and the old, broken, fragmented graphics driver “model.” NVIDIA will always be a second class citizen on Wayland until they change their tune regarding open source. But hey, maybe they’re OK with losing Linux and Mac users, and if that’s their position I’m perfectly happy losing them.)

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Do AMD GPUs work well with ML drivers (CUDA and competitors)? Are higher-level drivers like PyTorch and TensorFlow compatible (and optimized, or else no point buying an expensive GPU to get only half-performance) with AMD backends?

TensorFlow works with AMD's CUDA alternative named ROCm. You'd need to check installation instructions though.

Yes, ROCm is upstreamed into Tensorflow. On our Hopsworks platform, you can run the same TensorFlow code on either ROCm or Cuda. No code changes needed:

https://www.logicalclocks.com/blog/welcoming-amd-rocm-to-hop...

Performance ain't so bad on AMD GPUs anymore - and none of the EULA crap. Just buy a Vega R7 for 500 bucks and get 16 GB of memory and the performance of close to a 2080 Ti (for convnets, at least) - but with no data center restrictions:

https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/...

How about Pytorch?

You have to compile it yourself, which sucks. For some reason PyTorch aren't agreeing to upstream ROCm, yet.

You don't need to compile it, AMD provides docker images.

For example I was able to run fast.ai/pytorch oh my amd gpu https://github.com/briangorman/fastai_rocm_docker

My current impression is that AMD is not the best option for ML but is getting better over time. So YMMV. That said, I do not use my local machine for ML (and don’t do ML in any serious capacity.)

In general, no. The highest performance software only runs on Nvidia GPUs (or internal hardware, like Google’s TPUs).

I think this depends very much on what exactly you mean by "highest performance software". Some frameworks have upstreamed support for ROCm. A few of my colleagues have been doing some testing and mentioned to me they were reasonably impressed with performance on newer hardware.

It's far better today than it used to be, but it's still a work in progress. Over the past year or two, they've been doing a lot of hiring to bring ROCm HIP up to par as a CUDA competitor.

I wasn't exactly thrilled to see yet another GPU interface, but they seem to be trying to match the CUDA way of doing things. That ought to make supporting AMD much easier than it once was.

> NVIDIA will always be a second class citizen on Wayland

More like Wayland will always be a second class citizen on machines with NVIDIA cards.

They are the only such graphics vendor, but sure. Either way NVIDIA users lose.

Or is it Wayland users that lose? On which side will the coin land? :)

The coin landed long enough that you can’t even see it under the dust. Xorg is effectively unmaintained aside from Xwayland. If you had hopes of Wayland disappearing, you can safely end them now.

I don't have hopes of that, I'm just waiting for the dust to eventually settle so we can just use it and stop waiting for it.

Do you know if Ubuntu 20.04 will be using it by default?

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