Change default number of dataloader workers
as suggested by pytorch
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@ -81,10 +81,9 @@ def add_common_args(parser):
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# "batches" is kept for now for backward compatibility
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# "batches" is kept for now for backward compatibility
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parser.add_argument('--batch-size', '--batches', type=int, required=True,
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parser.add_argument('--batch-size', '--batches', type=int, required=True,
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help='mini-batch size, per GPU in training or in total in testing')
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help='mini-batch size, per GPU in training or in total in testing')
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parser.add_argument('--loader-workers', default=-8, type=int,
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parser.add_argument('--loader-workers', default=8, type=int,
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help='number of subprocesses per data loader. '
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help='number of subprocesses per data loader. '
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'0 to disable multiprocessing; '
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'0 to disable multiprocessing')
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'negative number to multiply by the batch size')
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parser.add_argument('--callback-at', type=lambda s: os.path.abspath(s),
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parser.add_argument('--callback-at', type=lambda s: os.path.abspath(s),
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help='directory of custorm code defining callbacks for models, '
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help='directory of custorm code defining callbacks for models, '
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@ -186,8 +185,7 @@ def int_tuple(s):
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def set_common_args(args):
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def set_common_args(args):
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if args.loader_workers < 0:
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pass
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args.loader_workers *= - args.batch_size
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def set_train_args(args):
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def set_train_args(args):
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