Add Accelerator API to Imagenet Example #1349
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Signed-off-by: eromomon <[email protected]>
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dvrogozh
reviewed
Jun 13, 2025
imagenet/main.py
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| if use_accel: | ||
| if args.gpu is not None: | ||
| torch.accelerator.set_device_index(args.gpu) | ||
| print("Use GPU: {} for training".format(args.gpu)) |
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This print seems off as you don't add it in other cases for example in validate. Is this a leftover debug print? Isn't the print on line 116 not enough?
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Solved, print removed
Signed-off-by: Edgar Romo Montiel <[email protected]>
msaroufim
approved these changes
Jun 16, 2025
soumith
approved these changes
Jun 17, 2025
jataylo
reviewed
Jun 30, 2025
| target = target.cuda(args.gpu, non_blocking=True) | ||
| if use_accel: | ||
| if args.gpu is not None and device.type=='cuda': | ||
| torch.accelerator.set_device_index(argps.gpu) |
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Looks like a typo here argps instead of args
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Wanna send me a patch? I'll merge
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Refactor Imagenet example to utilize torch.accelerator API. torch.accelerator API allows to abstract some of the accelerator specifics in the user scripts. By leveraging this API, the code becomes more adaptable to various hardware accelerators.
CC: @msaroufim, @malfet, @dvrogozh