set type of batch_size argument to int in ddp-tutorial-series (#1104)

set type of batch_size argument to int

Author: ConradStack

distributed/ddp-tutorial-series/multigpu.py

@@ -96,7 +96,7 @@ def main(rank: int, world_size: int, save_every: int, total_epochs: int, batch_s
     parser = argparse.ArgumentParser(description='simple distributed training job')
     parser.add_argument('total_epochs', type=int, help='Total epochs to train the model')
     parser.add_argument('save_every', type=int, help='How often to save a snapshot')
-    parser.add_argument('--batch_size', default=32, help='Input batch size on each device (default: 32)')
+    parser.add_argument('--batch_size', default=32, type=int, help='Input batch size on each device (default: 32)')
     args = parser.parse_args()
 
     world_size = torch.cuda.device_count()

distributed/ddp-tutorial-series/multigpu_torchrun.py

@@ -104,7 +104,7 @@ def main(save_every: int, total_epochs: int, batch_size: int, snapshot_path: str
     parser = argparse.ArgumentParser(description='simple distributed training job')
     parser.add_argument('total_epochs', type=int, help='Total epochs to train the model')
     parser.add_argument('save_every', type=int, help='How often to save a snapshot')
-    parser.add_argument('--batch_size', default=32, help='Input batch size on each device (default: 32)')
+    parser.add_argument('--batch_size', default=32, type=int, help='Input batch size on each device (default: 32)')
     args = parser.parse_args()
 
     main(args.save_every, args.total_epochs, args.batch_size)

distributed/ddp-tutorial-series/multinode.py

@@ -105,7 +105,7 @@ def main(save_every: int, total_epochs: int, batch_size: int, snapshot_path: str
     parser = argparse.ArgumentParser(description='simple distributed training job')
     parser.add_argument('total_epochs', type=int, help='Total epochs to train the model')
     parser.add_argument('save_every', type=int, help='How often to save a snapshot')
-    parser.add_argument('--batch_size', default=32, help='Input batch size on each device (default: 32)')
+    parser.add_argument('--batch_size', default=32, type=int, help='Input batch size on each device (default: 32)')
     args = parser.parse_args()
 
     main(args.save_every, args.total_epochs, args.batch_size)

distributed/ddp-tutorial-series/single_gpu.py

@@ -75,7 +75,7 @@ def main(device, total_epochs, save_every, batch_size):
     parser = argparse.ArgumentParser(description='simple distributed training job')
     parser.add_argument('total_epochs', type=int, help='Total epochs to train the model')
     parser.add_argument('save_every', type=int, help='How often to save a snapshot')
-    parser.add_argument('--batch_size', default=32, help='Input batch size on each device (default: 32)')
+    parser.add_argument('--batch_size', default=32, type=int, help='Input batch size on each device (default: 32)')
     args = parser.parse_args()
 
     device = 0  # shorthand for cuda:0