Don't copy the batch when training on a single gpu #1576
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| # Don't copy the batch since there is a single gpu that the batch could | ||
| # be referenced from and if there are multiple optimizers the batch will | ||
| # wind up copying it to the same device repeatedly. | ||
| batch = self.transfer_batch_to_gpu(batch, gpu_id) |
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what happens with multiple GPUs?
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This is in the "self.single_gpu" condition, so I don't think it's possible to have multiple gpus here? But I don't know this code very well at all.
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What does this PR do?
Fixes #1566 by removing the copy.copy(batch) when moving a batch onto the gpu. This way if there are multiple optimizers, the batch will only need to be moved to the gpu once.
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