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Don't copy the batch when training on a single gpu #1576

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Apr 23, 2020
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Don't copy the batch when training on a single gpu #1576

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2e41dcf
fix
jkarlin Apr 23, 2020
e2cedee
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jkarlin Apr 23, 2020
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6 changes: 5 additions & 1 deletion pytorch_lightning/trainer/training_loop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -754,7 +754,11 @@ def training_forward(self, batch, batch_idx, opt_idx, hiddens):
gpu_id = 0
if isinstance(self.data_parallel_device_ids, list):
gpu_id = self.data_parallel_device_ids[0]
batch = self.transfer_batch_to_gpu(copy.copy(batch), gpu_id)

# 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.

args[0] = batch
output = self.model.training_step(*args)

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