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Fixing logging and errors blocking multi GPU trianing of Torch models #1509

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merged 11 commits into from
Feb 21, 2023
Merged

Fixing logging and errors blocking multi GPU trianing of Torch models #1509

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f2de6c0
added fix for multi GPU as per https://pytorch-lightning.readthedocs.…
solalatus Jan 14, 2023
54fcf1b
trying to add complete logging in case of distributed to avoid deadlock
solalatus Jan 19, 2023
74477d9
fixing the logging on epoch end for multigpu training
solalatus Jan 21, 2023
c5bd70e
Merge branch 'unit8co:master' into master
solalatus Jan 23, 2023
7038548
Merge branch 'unit8co:master' into master
solalatus Jan 24, 2023
e5d2910
Black fixes for formatting errors
solalatus Jan 24, 2023
5be81d7
Merge branch 'master' into master
hrzn Jan 27, 2023
bc64a1f
Merge branch 'master' into master
hrzn Feb 16, 2023
5e5a006
Merge branch 'master' into master
madtoinou Feb 16, 2023
762d2a9
Merge branch 'unit8co:master' into master
solalatus Feb 18, 2023
f2fbf8b
Added description of multi GPU setup do User Guide.
solalatus Feb 18, 2023
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17 changes: 15 additions & 2 deletions darts/models/forecasting/pl_forecasting_module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -150,7 +150,13 @@ def training_step(self, train_batch, batch_idx) -> torch.Tensor:
-1
] # By convention target is always the last element returned by datasets
loss = self._compute_loss(output, target)
self.log("train_loss", loss, batch_size=train_batch[0].shape[0], prog_bar=True)
self.log(
"train_loss",
loss,
batch_size=train_batch[0].shape[0],
prog_bar=True,
sync_dist=True,
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The PTL doc says Use with care as this may lead to a significant communication overhead..
Do we have any idea if/when this could cause issues?

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So far in practical testing on 8 GPU-s I noticed no adverse effects. Thus said, it depends also on the distribution strategy also. I used the default ddp_spawn, as mentioned.

)
self._calculate_metrics(output, target, self.train_metrics)
return loss

Expand All @@ -159,7 +165,13 @@ def validation_step(self, val_batch, batch_idx) -> torch.Tensor:
output = self._produce_train_output(val_batch[:-1])
target = val_batch[-1]
loss = self._compute_loss(output, target)
self.log("val_loss", loss, batch_size=val_batch[0].shape[0], prog_bar=True)
self.log(
"val_loss",
loss,
batch_size=val_batch[0].shape[0],
prog_bar=True,
sync_dist=True,
)
self._calculate_metrics(output, target, self.val_metrics)
return loss

Expand Down Expand Up @@ -274,6 +286,7 @@ def _calculate_metrics(self, output, target, metrics):
on_step=False,
logger=True,
prog_bar=True,
sync_dist=True,
)

def configure_optimizers(self):
Expand Down