Add Accelerator API to Imagenet Example#1349
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Signed-off-by: eromomon <edgar.romo.montiel@intel.com>
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dvrogozh
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Jun 13, 2025
| 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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Signed-off-by: Edgar Romo Montiel <edgar.romo.montiel@intel.com>
msaroufim
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Jun 16, 2025
soumith
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Jun 17, 2025
jataylo
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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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I was doing something else on nvidia system and had a spare minute. Here is a PR to fix and typo and add respective test:
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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