Mimimum NVIDA chip for training about ml-compiler-opt HOT 7 OPEN

We didn't use any kind of acceleration, but if you want to, I'd assume anything that Tensorflow supports.

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Anything that Tensorflow supports should work, but note (that at least with how the code is setup currently) that copying the data over to the GPU/doing the training iterations on the GPU/copying everything back over to the CPU takes longer than just doing the training on the CPU, at least when I last tested it. There has been some experimentation with much larger models where acceleration (GPU/TPU) has definitely helped, but none of those models are upstream in this repository.

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So even then a modern mac m1 should work?

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I see, just a modern CPU with 96 HW threads? "for local training, which is currently the only supported mode, we recommend a high-performance workstation (e.g. 96 hardware threads)."

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Yup. The bottleneck is currently compile time.

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Do you know if anyone is using a mac for this?

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There was some experimentation with using a Mac (see patches like #260), but it's not really a platform where all the tooling here is guaranteed to work. The tooling in this repository is almost exclusively developed and run on Linux, although running it on a Mac should theoretically work, maybe minus seem slight issues.

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