Created
May 13, 2019 07:41
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| import torch | |
| import torch.nn as nn | |
| from parallel import DataParallelModel, DataParallelCriterion | |
| model = BERT(args) | |
| model = DataParallelModel(model) | |
| model.cuda() | |
| criterion = nn.NLLLoss() | |
| criterion = DataParallelCriterion(criterion) | |
| ... | |
| for i, (inputs, labels) in enumerate(trainloader): | |
| outputs = model(inputs) | |
| loss = criterion(outputs, labels) | |
| optimizer.zero_grad() | |
| loss.backward() | |
| optimizer.step() |
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