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PyTorch 1.8, with AMD ROCm support

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Re: PyTorch 1.8, with AMD ROCm support

#11
post #4

The supported ROCm version is 4.0 which only the latest AMD instinct supports it. There's still a long way before its supported in the latest consumer RDNA 2 GPUs ( RX 6000 series )

Lack of ROCm support in consumer RDNA 2 GPUs really makes it impossible for regular people to use ROCm. As an owner of an AMD Radeon RX 6800 I'm pretty salty about it.

At least they promised support.

They're actively refusing to comment on 5000-series.

Re: PyTorch 1.8, with AMD ROCm support

#13
post #4

The supported ROCm version is 4.0 which only the latest AMD instinct supports it. There's still a long way before its supported in the latest consumer RDNA 2 GPUs ( RX 6000 series )

Lack of ROCm support in consumer RDNA 2 GPUs really makes it impossible for regular people to use ROCm. As an owner of an AMD Radeon RX 6800 I'm pretty salty about it.

It's the last thing that keeps me on Nvidia with proprietary Linux drivers. I wouldn't mind ML training on a AMD card to be slower but I need my workload to be at least GPU-accelerated.

Re: PyTorch 1.8, with AMD ROCm support

#14
post #9

This is pretty neat since it is the first time in years that a top-tier deep learning framework has official support for any training accelerator with open source kernel drivers. I guess the TPU also doesn't require kernel drivers because you talk to it over network instead of PCIE. But you cannot buy a TPU, only the int8 edge TPU is for sale. (And I've heard that the edge TPU's are absolutely top-notch for performan…

I believe TensorFlow is a top-tier deep learning framework, and it had ROCm support since 2018. > edge TPU's are absolutely top-notch for performance per $ and Watt right now Do you mean "aren't"? The performance per $ and Watt is not awesome even when it was released, I was hoping for great toolchain support but that also didn't happen.

Are there any If it exists, I wanna buy it

Re: PyTorch 1.8, with AMD ROCm support

#15
post #9

This is pretty neat since it is the first time in years that a top-tier deep learning framework has official support for any training accelerator with open source kernel drivers. I guess the TPU also doesn't require kernel drivers because you talk to it over network instead of PCIE. But you cannot buy a TPU, only the int8 edge TPU is for sale. (And I've heard that the edge TPU's are absolutely top-notch for performan…

I believe TensorFlow is a top-tier deep learning framework, and it had ROCm support since 2018. > edge TPU's are absolutely top-notch for performance per $ and Watt right now Do you mean "aren't"? The performance per $ and Watt is not awesome even when it was released, I was hoping for great toolchain support but that also didn't happen.

Tensorflow doesn't seem to officially support ROCm, only unofficial community projects do. This is official support from PyTorch.

Re: PyTorch 1.8, with AMD ROCm support

#16
post #8

Now that major frameworks finally started supporting ROCm, AMD has half-abandoned it (IIRC the last consumer cards supported were the Vega ones, cards from 2 generations ago). I hope this will change.

Oh oof. Thanks for saving me time not having to look up ROCm benchmarks. I find it really surprising they they don't wanna compete on performance/$ at all by not supporting consumer cards

Re: PyTorch 1.8, with AMD ROCm support

#17

Earlier quoted context omitted.

RX580 had a pro version which got ROCm support. It's the newer cards and older cards which aren't supported.

Can confirm, I made an 8x rx580 rig for an r&d project specifically for that reason.

Time to re-sell into the hot GPU market now? :P

Re: PyTorch 1.8, with AMD ROCm support

#19
post #16
post #8

Now that major frameworks finally started supporting ROCm, AMD has half-abandoned it (IIRC the last consumer cards supported were the Vega ones, cards from 2 generations ago). I hope this will change.

Oh oof. Thanks for saving me time not having to look up ROCm benchmarks. I find it really surprising they they don't wanna compete on performance/$ at all by not supporting consumer cards

No worries, for future reference you can check here (hopefully that page will report improved support in the future)

https://github.com/RadeonOpenCompute/ROCm#supported-gpus

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