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Indeed, are you aware of the current state of AMD's equivalent to CUDA? And how far behind would be, and what would need to be done to near equivalence? It seem
by dpflan 3y ago
Indeed, are you aware of the current state of AMD's equivalent to CUDA? And how far behind would be, and what would need to be done to near equivalence? It seems like the opportunity and differentiator. I wonder how Apple silicon software is doing...
- llm_nerd 3y agoInstead of trying to integrate the whole stack of, say, pytorch, Apple's primary approach has been converting models to work with Apple's stack. https://github.com/apple/coremltools https://github.com/apple/coremltools Clearly no one is going to be doing training or even fine tuning on Apple hardware at any scale (it competes at the low end, but at scale you invariably will be using nvidia hardware), but once you have a decent model it's a robust way of using it on Apple devices.
- coredog64 3y agoI’ve tried a few models and none have worked. It’s not that they need more resources, just that it freezes and then dies with an inscrutable stack trace somewhere in the OS. If someone from AMD sees the parent comment, please don’t copy Apple!
- dpflan 3y agoMind sharing which ones and your approach? Haven't there been shared posts of success to follow? Also, yes, anyone from AMD, I think we all want more variety in the market, what do you need help with?
- llm_nerd 3y agoNot every model feature and op is supported (though it is growing with every release), but personally I've had surprising success with it. It has allowed me to leverage some models efficiently on both Intel, Apple Silicon and iPhone/iPad devices. I mean...the number of people using PyTorch models ported to CoreML is probably several magnitude greater than the number of people actually having success with AMD's initiatives in the space thus far...