dataloader workers

[alicera]

When I train the yolo ,it will see the message.
"Using 1 dataloader workers" , how to set the number of the dataloader workers, and how to define it.

Analyzing anchors... anchors/target = 5.92, Best Possible Recall (BPR) = 0.9999
Image sizes 640 train, 640 test
Using 1 dataloader workers
Logging results to runs/exp6
Starting training for 300 epochs...

[glenn-jocher]

@alicera please read the argparser of each file to see settable arguments:

yolov5/train.py

Lines 413 to 442 in 577f298

	parser = argparse.ArgumentParser()
	parser.add_argument('--weights', type=str, default='yolov5s.pt', help='initial weights path')
	parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
	parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
	parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')
	parser.add_argument('--epochs', type=int, default=300)
	parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
	parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
	parser.add_argument('--rect', action='store_true', help='rectangular training')
	parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
	parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
	parser.add_argument('--notest', action='store_true', help='only test final epoch')
	parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
	parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
	parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
	parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
	parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
	parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
	parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
	parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
	parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
	parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
	parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
	parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100')
	parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
	parser.add_argument('--project', default='runs/train', help='save to project/name')
	parser.add_argument('--name', default='exp', help='save to project/name')
	parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
	opt = parser.parse_args()

[alicera]

parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
the workers correspond to the cpu core?
like, 4 workers correspond to 4 core

[glenn-jocher]

1 worker uses 1 python thread I believe.

[github-actions]

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