I get that Nvidia is the favorite GPU (because CUDA) and that library maintainers want to chase the latest and best to do the most. But I don't get why support for older hardware (including CUDA stuff) is just deprecated or abandoned, nor why support for other GPU architectures is lacking across many popular ML libraries.
A lot of this is down to driver availability and software stacks...but is all of it? Game engines/engineers seem to be able to be productive on a wide variety of GPU hardware, why do so many ML libraries just not provide any support at all? Sure 75% of potential performance is less satisfying than 100%, but it's also infinitely better than 0%. How come every ML library doesn't at least have an OpenCL fallback or the like?
A lot of this is down to driver availability and software stacks...but is all of it? Game engines/engineers seem to be able to be productive on a wide variety of GPU hardware, why do so many ML libraries just not provide any support at all? Sure 75% of potential performance is less satisfying than 100%, but it's also infinitely better than 0%. How come every ML library doesn't at least have an OpenCL fallback or the like?