My tf or pytorch code usually uses custom CUDA extensions, so sadly it will never be plug and play on AMD gpus for me. I hate that scientific computing and ML settled on a (admittedly superior) proprietary API.
I’m going to be glad when that finally happens. It’s not healthy for machine learning to be so dependent on a single provider like that.
What does this mean? And how does having CUDA in TF make it better for vendors that don't support CUDA?
Could you post a link to this work?
How is it going to untie it from Nvidia? The only way to do it, is not to use CUDA, but something portable that can target any GPU.
IIRC ROCm (in the form of HIP) defines a new C/C++ API that maps to either AMD intrincis or CUDA depending on a compile time flag.
It required converting your CUDA source code to ROCm code, though there was a code translation tool to help you with that.
To be honest: I don't really understand what ROCm stands for. AMD has been redefining their GP compute platforms so many times that it's easy to lose track.
https://rocm-documentation.readthedocs.io/en/latest/Programm...