<< I'm often asked by users with no experience in the space why we don't target AMD, Coral TPUs (don't even get me started), etc. It's almost impossible to understand "why CUDA" unless you've fought these battles and spent time with "alternatives".
Would you be willing to elaborate ( ie. I would love to hear you get started )? I absolutely agree that some competition is needed in this space. I am absolutely not an expert so it is hard for me to understand why there is no real alternative to CUDA. Are they just too hard to set up? Not popular enough to have any support?
Probably the best reference is HN itself. Look at the dozens of GPGPU projects, articles, papers, etc that hit the front page in any given week. Then look to see how many of them support AMD/ROCm. Spoiler alert: virtually zero.
That's compelling enough to justify the position here but you can do further research to explore the challenges with other platforms (like ROCm). Just glance at issue trackers for Pytorch, Tensorflow, and higher level projects that (rarely) support ROCm - you will notice a clear trend. Even though CUDA is more capable and outnumbers AMD use 10:1 the issues reported currently are:
Pytorch
Search for ROCm - 2,557 issues open (~10% market share)
Search for CUDA - 5,430 issues open (at least 80% market share)
Even in the limited cases where it's attempted the capability and experience with AMD/ROCm is significantly worse, to the point of "almost no one even bothers anymore" (see my top paragraph).
Would you be willing to elaborate ( ie. I would love to hear you get started )? I absolutely agree that some competition is needed in this space. I am absolutely not an expert so it is hard for me to understand why there is no real alternative to CUDA. Are they just too hard to set up? Not popular enough to have any support?