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def test_function(test_case): | |
move_zeros_to_left(test_case[0]) | |
if test_case[0] == test_case[1]: | |
print("Pass") | |
else: | |
print("Fail") |
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def move_zeros_to_left(array): | |
i = len(array) - 1 | |
j = len(array) - 1 | |
while i >= 0: | |
if array[i] != 0: | |
array[j] = array[i] | |
j -= 1 | |
i -= 1 | |
while j >= 0: | |
array[j] = 0 |
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# Save checkpoint | |
checkpoint = { | |
'model': model.state_dict(), | |
'optimizer': optimizer.state_dict(), | |
'amp': amp.state_dict() | |
} | |
torch.save(checkpoint, 'amp_checkpoint.pt') | |
... | |
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rank = args.nr * args.gpus + gpu | |
dist.init_process_group( | |
backend='nccl', | |
init_method='env://', | |
world_size=args.world_size, | |
rank=rank) | |
torch.manual_seed(0) | |
model = ConvNet() | |
torch.cuda.set_device(gpu) |
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python src/mnist-distributed.py -n 4 -g 8 -nr i |
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python src/mnist-distributed.py -n 4 -g 8 -nr 0 |
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def train(gpu, args): | |
############################################################ | |
rank = args.nr * args.gpus + gpu | |
dist.init_process_group( | |
backend='nccl', | |
init_method='env://', | |
world_size=args.world_size, | |
rank=rank | |
) | |
############################################################ |
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def main(): | |
parser = argparse.ArgumentParser() | |
parser.add_argument('-n', '--nodes', default=1, | |
type=int, metavar='N') | |
parser.add_argument('-g', '--gpus', default=1, type=int, | |
help='number of gpus per node') | |
parser.add_argument('-nr', '--nr', default=0, type=int, | |
help='ranking within the nodes') | |
parser.add_argument('--epochs', default=2, type=int, | |
metavar='N', |
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def train(gpu, args): | |
torch.manual_seed(0) | |
model = ConvNet() | |
model = nn.DataParallel(model) | |
torch.cuda.set_device(gpu) | |
model.cuda(gpu) | |
batch_size = 100 | |
# define loss function (criterion) and optimizer | |
criterion = nn.CrossEntropyLoss().cuda(gpu) | |
optimizer = torch.optim.SGD(model.parameters(), 1e-4) |
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class ConvNet(nn.Module): | |
def __init__(self, num_classes=10): | |
super(ConvNet, self).__init__() | |
self.layer1 = nn.Sequential( | |
nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2), | |
nn.BatchNorm2d(16), | |
nn.ReLU(), | |
nn.MaxPool2d(kernel_size=2, stride=2)) | |
self.layer2 = nn.Sequential( | |
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2), |
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