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Switching to AMP precision promises huge speed-ups for both training and inference, and can be done easily with pytorch.
Just doing something like:
with torch.autocast(): output = model(input) loss = loss_fn(output, target)
Can lead to speed-ups of 50%-150%.
The text was updated successfully, but these errors were encountered:
Forgot to mention: At the cost of almost no performance drop.
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Switching to AMP precision promises huge speed-ups for both training and inference, and can be done easily with pytorch.
Just doing something like:
Can lead to speed-ups of 50%-150%.
The text was updated successfully, but these errors were encountered: