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Replace hardcoded default values in argparse help strings #1290

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Replace hardcoded default values in argparse help strings #1290

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8 changes: 4 additions & 4 deletions distributed/FSDP/T5_training.py
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Expand Up @@ -198,13 +198,13 @@ def fsdp_main(args):
# Training settings
parser = argparse.ArgumentParser(description='PyTorch T5 FSDP Example')
parser.add_argument('--batch-size', type=int, default=4, metavar='N',
help='input batch size for training (default: 64)')
help='input batch size for training (default: %(default)s)')
parser.add_argument('--test-batch-size', type=int, default=4, metavar='N',
help='input batch size for testing (default: 1000)')
help='input batch size for testing (default: %(default)s)')
parser.add_argument('--epochs', type=int, default=2, metavar='N',
help='number of epochs to train (default: 3)')
help='number of epochs to train (default: %(default)s)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
help='random seed (default: %(default)s)')
parser.add_argument('--track_memory', action='store_false', default=True,
help='track the gpu memory')
parser.add_argument('--run_validation', action='store_false', default=True,
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3 changes: 2 additions & 1 deletion distributed/ddp-tutorial-series/multigpu.py
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Expand Up @@ -97,7 +97,8 @@ 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, type=int, 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: %(default)s)')
args = parser.parse_args()

world_size = torch.cuda.device_count()
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3 changes: 2 additions & 1 deletion distributed/ddp-tutorial-series/multigpu_torchrun.py
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Expand Up @@ -105,7 +105,8 @@ 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, type=int, 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: %(default)s)')
args = parser.parse_args()

main(args.save_every, args.total_epochs, args.batch_size)
3 changes: 2 additions & 1 deletion distributed/ddp-tutorial-series/multinode.py
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Expand Up @@ -106,7 +106,8 @@ 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, type=int, 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: %(default)s)')
args = parser.parse_args()

main(args.save_every, args.total_epochs, args.batch_size)
7 changes: 4 additions & 3 deletions distributed/ddp-tutorial-series/single_gpu.py
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Expand Up @@ -11,7 +11,7 @@ def __init__(
train_data: DataLoader,
optimizer: torch.optim.Optimizer,
gpu_id: int,
save_every: int,
save_every: int,
) -> None:
self.gpu_id = gpu_id
self.model = model.to(gpu_id)
Expand Down Expand Up @@ -75,8 +75,9 @@ 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, type=int, 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: %(default)s)')
args = parser.parse_args()

device = 0 # shorthand for cuda:0
main(device, args.total_epochs, args.save_every, args.batch_size)
6 changes: 3 additions & 3 deletions distributed/rpc/batch/reinforce.py
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Expand Up @@ -21,11 +21,11 @@

parser = argparse.ArgumentParser(description='PyTorch RPC Batch RL example')
parser.add_argument('--gamma', type=float, default=1.0, metavar='G',
help='discount factor (default: 1.0)')
help='discount factor (default: %(default)s)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
help='random seed (default: 543)')
help='random seed (default: %(default)s)')
parser.add_argument('--num-episode', type=int, default=10, metavar='E',
help='number of episodes (default: 10)')
help='number of episodes (default: %(default)s)')
args = parser.parse_args()

torch.manual_seed(args.seed)
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6 changes: 3 additions & 3 deletions distributed/rpc/rl/main.py
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Expand Up @@ -21,11 +21,11 @@
parser.add_argument('--world-size', type=int, default=2, metavar='W',
help='world size for RPC, rank 0 is the agent, others are observers')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
help='discount factor (default: 0.99)')
help='discount factor (default: %(default)s)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
help='random seed (default: 543)')
help='random seed (default: %(default)s)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='interval between training status logs (default: 10)')
help='interval between training status logs (default: %(default)s)')
args = parser.parse_args()

torch.manual_seed(args.seed)
Expand Down
20 changes: 10 additions & 10 deletions gat/main.py
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Expand Up @@ -292,29 +292,29 @@ def test(model, criterion, input, target, mask):

parser = argparse.ArgumentParser(description='PyTorch Graph Attention Network')
parser.add_argument('--epochs', type=int, default=300,
help='number of epochs to train (default: 300)')
help='number of epochs to train (default: %(default)s)')
parser.add_argument('--lr', type=float, default=0.005,
help='learning rate (default: 0.005)')
help='learning rate (default: %(default)s)')
parser.add_argument('--l2', type=float, default=5e-4,
help='weight decay (default: 6e-4)')
help='weight decay (default: %(default)s)')
parser.add_argument('--dropout-p', type=float, default=0.6,
help='dropout probability (default: 0.6)')
help='dropout probability (default: %(default)s)')
parser.add_argument('--hidden-dim', type=int, default=64,
help='dimension of the hidden representation (default: 64)')
help='dimension of the hidden representation (default: %(default)s)')
parser.add_argument('--num-heads', type=int, default=8,
help='number of the attention heads (default: 4)')
help='number of the attention heads (default: %(default)s)')
parser.add_argument('--concat-heads', action='store_true', default=False,
help='wether to concatinate attention heads, or average over them (default: False)')
help='wether to concatinate attention heads, or average over them (default: %(default)s)')
parser.add_argument('--val-every', type=int, default=20,
help='epochs to wait for print training and validation evaluation (default: 20)')
help='epochs to wait for print training and validation evaluation (default: %(default)s)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--no-mps', action='store_true', default=False,
help='disables macOS GPU training')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
parser.add_argument('--seed', type=int, default=13, metavar='S',
help='random seed (default: 13)')
help='random seed (default: %(default)s)')
args = parser.parse_args()

torch.manual_seed(args.seed)
Expand Down Expand Up @@ -372,4 +372,4 @@ def test(model, criterion, input, target, mask):
if args.dry_run:
break
loss_test, acc_test = test(gat_net, criterion, (features, adj_mat), labels, idx_test)
print(f'Test set results: loss {loss_test:.4f} accuracy {acc_test:.4f}')
print(f'Test set results: loss {loss_test:.4f} accuracy {acc_test:.4f}')
18 changes: 9 additions & 9 deletions gcn/main.py
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Expand Up @@ -203,27 +203,27 @@ def test(model, criterion, input, target, mask):

parser = argparse.ArgumentParser(description='PyTorch Graph Convolutional Network')
parser.add_argument('--epochs', type=int, default=200,
help='number of epochs to train (default: 200)')
help='number of epochs to train (default: %(default)s)')
parser.add_argument('--lr', type=float, default=0.01,
help='learning rate (default: 0.01)')
help='learning rate (default: %(default)s)')
parser.add_argument('--l2', type=float, default=5e-4,
help='weight decay (default: 5e-4)')
help='weight decay (default: %(default)s)')
parser.add_argument('--dropout-p', type=float, default=0.5,
help='dropout probability (default: 0.5)')
help='dropout probability (default: %(default)s)')
parser.add_argument('--hidden-dim', type=int, default=16,
help='dimension of the hidden representation (default: 16)')
help='dimension of the hidden representation (default: %(default)s)')
parser.add_argument('--val-every', type=int, default=20,
help='epochs to wait for print training and validation evaluation (default: 20)')
help='epochs to wait for print training and validation evaluation (default: %(default)s)')
parser.add_argument('--include-bias', action='store_true', default=False,
help='use bias term in convolutions (default: False)')
help='use bias term in convolutions (default: %(default)s)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--no-mps', action='store_true', default=False,
help='disables macOS GPU training')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
parser.add_argument('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)')
help='random seed (default: %(default)s)')
args = parser.parse_args()

use_cuda = not args.no_cuda and torch.cuda.is_available()
Expand Down Expand Up @@ -260,4 +260,4 @@ def test(model, criterion, input, target, mask):
break

loss_test, acc_test = test(gcn, criterion, (features, adj_mat), labels, idx_test)
print(f'Test set results: loss {loss_test:.4f} accuracy {acc_test:.4f}')
print(f'Test set results: loss {loss_test:.4f} accuracy {acc_test:.4f}')
14 changes: 7 additions & 7 deletions imagenet/main.py
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Expand Up @@ -27,34 +27,34 @@

parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet',
help='path to dataset (default: imagenet)')
help='path to dataset (default: %(default)s)')
parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
choices=model_names,
help='model architecture: ' +
' | '.join(model_names) +
' (default: resnet18)')
' (default: %(default)s)')
parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
help='number of data loading workers (default: 4)')
help='number of data loading workers (default: %(default)s)')
parser.add_argument('--epochs', default=90, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N',
help='mini-batch size (default: 256), this is the total '
help='mini-batch size (default: %(default)s), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
metavar='W', help='weight decay (default: 1e-4)',
metavar='W', help='weight decay (default: %(default)s)',
dest='weight_decay')
parser.add_argument('-p', '--print-freq', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
metavar='N', help='print frequency (default: %(default)s)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
help='path to latest checkpoint (default: %(default)s)')
parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
help='evaluate model on validation set')
parser.add_argument('--pretrained', dest='pretrained', action='store_true',
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8 changes: 4 additions & 4 deletions legacy/snli/util.py
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Expand Up @@ -23,18 +23,18 @@ def get_args():
parser.add_argument('--epochs', type=int, default=50,
help='the number of total epochs to run.')
parser.add_argument('--batch_size', type=int, default=128,
help='batch size. (default: 128)')
help='batch size. (default: %(default)s)')
parser.add_argument('--d_embed', type=int, default=100,
help='the size of each embedding vector.')
parser.add_argument('--d_proj', type=int, default=300,
help='the size of each projection layer.')
parser.add_argument('--d_hidden', type=int, default=300,
help='the number of features in the hidden state.')
parser.add_argument('--n_layers', type=int, default=1,
help='the number of recurrent layers. (default: 50)')
help='the number of recurrent layers. (default: %(default)s)')
parser.add_argument('--log_every', type=int, default=50,
help='iteration period to output log.')
parser.add_argument('--lr',type=float, default=.001,
parser.add_argument('--lr', type=float, default=.001,
help='initial learning rate.')
parser.add_argument('--dev_every', type=int, default=1000,
help='log period of validation results.')
Expand All @@ -51,7 +51,7 @@ def get_args():
parser.add_argument('--train_embed', action='store_false', dest='fix_emb',
help='enable embedding word training.')
parser.add_argument('--gpu', type=int, default=0,
help='gpu id to use. (default: 0)')
help='gpu id to use. (default: %(default)s)')
parser.add_argument('--save_path', type=str, default='results',
help='save path of results.')
parser.add_argument('--vector_cache', type=str, default=os.path.join(os.getcwd(), '.vector_cache/input_vectors.pt'),
Expand Down
12 changes: 6 additions & 6 deletions mnist/main.py
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Expand Up @@ -73,23 +73,23 @@ def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
help='input batch size for training (default: 64)')
help='input batch size for training (default: %(default)s)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
help='input batch size for testing (default: 1000)')
help='input batch size for testing (default: %(default)s)')
parser.add_argument('--epochs', type=int, default=14, metavar='N',
help='number of epochs to train (default: 14)')
help='number of epochs to train (default: %(default)s)')
parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
help='learning rate (default: 1.0)')
help='learning rate (default: %(default)s)')
parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
help='Learning rate step gamma (default: 0.7)')
help='Learning rate step gamma (default: %(default)s)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--no-mps', action='store_true', default=False,
help='disables macOS GPU training')
parser.add_argument('--dry-run', action='store_true', default=False,
help='quickly check a single pass')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
help='random seed (default: %(default)s)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--save-model', action='store_true', default=False,
Expand Down
6 changes: 3 additions & 3 deletions mnist_forward_forward/main.py
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Expand Up @@ -92,14 +92,14 @@ def train(self, x_pos, x_neg):
type=int,
default=1000,
metavar="N",
help="number of epochs to train (default: 1000)",
help="number of epochs to train (default: %(default)s)",
)
parser.add_argument(
"--lr",
type=float,
default=0.03,
metavar="LR",
help="learning rate (default: 0.03)",
help="learning rate (default: %(default)s)",
)
parser.add_argument(
"--no_cuda", action="store_true", default=False, help="disables CUDA training"
Expand All @@ -108,7 +108,7 @@ def train(self, x_pos, x_neg):
"--no_mps", action="store_true", default=False, help="disables MPS training"
)
parser.add_argument(
"--seed", type=int, default=1, metavar="S", help="random seed (default: 1)"
"--seed", type=int, default=1, metavar="S", help="random seed (default: %(default)s)"
)
parser.add_argument(
"--save_model",
Expand Down
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