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If you ever get the chance to write something in OpenCL and then in CUDA I promise you will understand immediately why Google didn't push for it.
There is a lot more boilerplate to it, read/write to the buffer, queueing being handled explicitly. Here's an example that illustrate what I mean: https://github.com/rsnemmen/OpenCL-examples/blob/master/mand...
For comparison here is an implementation in CUDA: http://selkie.macalester.edu/csinparallel/modules/CUDAArchit...
Notice how the CUDA code is more readable.
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coremltools
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
Instead 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
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.
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in today’s “lol AMD” news, geohotz got another $10m in funding or some shit to do an AI training company, chose to focus on AMD GPUs, and then publicly got really mad that AMD drivers are bad
https://github.com/RadeonOpenCompute/ROCm/issues/2198#issuec...
