No, I bought a Nvidia card and just use CUDA.

> OpenCL had just a weird dance to perform to get a kernel running...

Yeah but that entire list, if you step back and think big picture, probably isn't the problem. Programmers have a predictable response to that sort of silliness. Build a library over it & abstract it away. The sheer number of frameworks out there is awe-inspiring.

I gave up on OpenCL on AMD cards. It wasn't the long complex process that got me, it was the unavoidable crashes along the way. I suspect that is a more significant issue than I realised at the time (when I assumed it was just me) because it goes a long way to explain AMD's pariah-like status in the machine learning world. The situation is more one-sided than can be explained by just a well-optimised library. I've personally seen more success implementing machine learning frameworks on AMD CPUs than on AMD's GPUs, and that is a remarkable thing. Although I assume in 2024 the state of the game has changed a lot from when I was investigating the situation actively.

I don't think CUDA is the problem here, math libraries are commodity software that give a relatively marginal edge. The lack of CUDA is probably a symptom of deeper hardware problems once people stray off an explicitly graphical workflow. If the hardware worked to spec I expect someone would just build a non-optimised CUDA clone and we'd all move on. But AMD did build a CUDA clone and it didn't work for me at least - and the buzz suggests something is still going wrong for AMD's GPGPU efforts.