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.
Google is working on integrating CUDA directly into TensorFlow with the end goal being that you can use any GPU to do your computation. Basically a leapfrog over the inherently closed system that Nvidia has tried to implement.
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.
by the time Google v NVidia will hit the highest courts, those will be stacked with AI overlords. I wonder what they'd think about being stuck in a proprietary API. :D
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.
Yeah I’m sure stuff like this can work without code rewrite but my guess is that it’s far from plug and play. I couldn’t use that to run my model on an AMD card tommorrow without some effort.
