It is quite weird to talk about all the frameworks which are built on top of CUDA eventually, but not talking about Rocm or OpenCL.
OpenAI's Triton compiles down to CUDA atm (if I read their github right), and only supports Nvidia GPUs.
PyTorch 2.0's installation page only mentions CPU and CUDA targets, therefor it's effectively all Nvidia GPUs.
While all the frameworks and abstractions could offer other back-ends in theory the story of anything ML related on the other big name in the industry, AMD, is still poor.
If anybody looses business because of bad decisions it is AMD, not Nvidia, who lead the whole industry. I am not convinced that anything will change in the near future.
> AMD ROcm supports Pytorch, TensorFlow, MlOpen, rocBLAS on NVIDIA and AMD GPUs: https://rocmdocs.amd.com/en/latest/Deep_learning/Deep-learni... [...]
> ROCm-Developer-Tools/HIPIFY https://github.com/ROCm-Developer-Tools/HIPIFY :
>> hipify-clang is a clang-based tool for translating CUDA sources into HIP sources. It translates CUDA source into an abstract syntax tree, which is traversed by transformation matchers. After applying all the matchers, the output HIP source is produced. [...]
From https://github.com/RadeonOpenCompute/clang-ocl :
> RadeonOpenCompute/OpenCL compilation with clang compiler