[docs] MPS #28016
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[docs] MPS #28016
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amyeroberts
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Dec 14, 2023
| Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [multi-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into this section. | ||
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| Some PyTorch operations are not implemented in MPS yet and will throw an error. To avoid this, you should set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU kernels instead (you'll still see a `UserWarning`). |
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Is there a way to have trainer just use the CPU entirely and ignore the MPS backend?
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I think you can set use_cpu=True here, but cc'ing @pacman100 who'll know more about it 🙂
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| 2. Distributed setups `gloo` and `nccl` are not working with `mps` device. | ||
| This means that currently only single GPU of `mps` device type can be used. |
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Is this no longer the case?
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I believe its still true, I didn't see mps among the supported backends for torch.distributed (included in the second to last paragraph of the new doc)
amyeroberts
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Dec 14, 2023
iantbutler01
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Dec 16, 2023
* mps docs * toctree
staghado
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* mps docs * toctree
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As a part of a larger effort to clean up the
TrainerAPI docs in #27986, this PR moves the Trainer for accelerated PyTorch training on Mac section to the currently empty Training on Specialized Hardware page.Other updates include rewriting it a bit so it doesn't sound like it's copied directly from the blog post and removing the link to the paywalled article for setup 🙂