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Just in case other people who have AMD GPU and run Windows have the same needs as I have, that is, to train or run machine learning models, please checkout torc

Just in case other people who have AMD GPU and run Windows have the same needs as I have, that is, to train or run machine learning models, please checkout torch-directml and tensorflow-directml.

bornfreddy 3y ago
Does that work? I might be in market for a new GPU if AMD had something that beats NVidia for ML (for sane price)... I can't really justify buying NVidia GPU, anything decent is too expensive.
- netheril96 3y ago
  It works for me. I have no issues training a WGAN with it. But I don’t know how much slower it is compared to CUDA on a similar priced NVIDIA card.
skocznymroczny 3y ago
How does it work? Last time I tried DirectML it wasn't well supposed and there was little software which supported it. Also the performance seemed to be not too great. I am currently using a Linux install because with ROCm I can use popular tools like Automatic111 webui and oobabooga.
- netheril96 3y ago
  I trained a WGAN on torch-directml with no issues so the software seems quite supported. But I can’t speak of performance because I have nothing to compare against.
  - skocznymroczny 3y ago
    I gave it a try. Yeah seems like it works well on the functional side, but the performance was at least 4x slower than what ROCm on Linux gives me.
HarHarVeryFunny 3y ago
I'm not sure this really makes any more sense than AMD chasing CUDA compatibility with ROCm/MiOpen/HIP. CUDA and DirectX seem too low level to be used as a compatibility API over widely divergent hardware (AMD vs NVidia) without giving up a lot of performance. cuDNN being higher level offers more opportunity for compatibility without losing performance (i.e different implementations of kernels fine-tuned for optimal performance on AMD vs NVidia hardware), but the trouble is that so much of what frameworks like PyTorch do is based on custom kernels, not just cuDNN. It seems the best bet for AMD would be a rock solid low level API (not a moving target) and support of high level optimizing ML compilers to reduce the level of effort for the framework (PyTorch, TensorFlow, JAX ...) vendors to provide framework-level support on top of that. Ultimately they'd need to work very closely with the framework vendors to provide this support, since they are the ones who would be benefiting from it. It's odd how much of an afterthought ML support has seemed to be for AMD over the years... maybe the relative size of the consumer ML market vs graphics/gaming market didn't seem to make it worth their effort, but as NVidia has shown this is a path to gaining much more lucrative data center wins.

Just in case other people who have AMD GPU and run Windows have the same needs as I have, that is, to train or run machine learning models, please checkout torch-directml and tensorflow-directml.

bornfreddy 3y ago

Does that work? I might be in market for a new GPU if AMD had something that beats NVidia for ML (for sane price)... I can't really justify buying NVidia GPU, anything decent is too expensive.

netheril96 3y ago
It works for me. I have no issues training a WGAN with it. But I don’t know how much slower it is compared to CUDA on a similar priced NVIDIA card.

skocznymroczny 3y ago

How does it work? Last time I tried DirectML it wasn't well supposed and there was little software which supported it. Also the performance seemed to be not too great. I am currently using a Linux install because with ROCm I can use popular tools like Automatic111 webui and oobabooga.

netheril96 3y ago
I trained a WGAN on torch-directml with no issues so the software seems quite supported. But I can’t speak of performance because I have nothing to compare against.
- skocznymroczny 3y ago
  I gave it a try. Yeah seems like it works well on the functional side, but the performance was at least 4x slower than what ROCm on Linux gives me.

HarHarVeryFunny 3y ago

I'm not sure this really makes any more sense than AMD chasing CUDA compatibility with ROCm/MiOpen/HIP. CUDA and DirectX seem too low level to be used as a compatibility API over widely divergent hardware (AMD vs NVidia) without giving up a lot of performance. cuDNN being higher level offers more opportunity for compatibility without losing performance (i.e different implementations of kernels fine-tuned for optimal performance on AMD vs NVidia hardware), but the trouble is that so much of what frameworks like PyTorch do is based on custom kernels, not just cuDNN. It seems the best bet for AMD would be a rock solid low level API (not a moving target) and support of high level optimizing ML compilers to reduce the level of effort for the framework (PyTorch, TensorFlow, JAX ...) vendors to provide framework-level support on top of that. Ultimately they'd need to work very closely with the framework vendors to provide this support, since they are the ones who would be benefiting from it. It's odd how much of an afterthought ML support has seemed to be for AMD over the years... maybe the relative size of the consumer ML market vs graphics/gaming market didn't seem to make it worth their effort, but as NVidia has shown this is a path to gaining much more lucrative data center wins.