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cleaning up demos

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This commit is contained in:
William Falcon
2019-10-05 15:32:54 -04:00
parent 45f36d3e74
commit 6be5fb3cf7
1 changed files with 62 additions and 53 deletions
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pytorch_lightning/trainer/trainer.py
+62 -53
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@@ -636,70 +636,79 @@ class Trainer(TrainerIO):
self.get_test_dataloaders = model.test_dataloader
self.get_val_dataloaders = model.val_dataloader
if self.use_ddp and not isinstance(self.get_train_dataloader().sampler, DistributedSampler):
msg = """
You're using multiple gpus and multiple nodes without using a DistributedSampler
to assign a subset of your data to each process. To silence this warning, pass a
DistributedSampler to your DataLoader.
if self.proc_rank == 0:
if self.use_ddp and not isinstance(self.get_train_dataloader().sampler, DistributedSampler):
msg = """
You're using multiple gpus and multiple nodes without using a DistributedSampler
to assign a subset of your data to each process. To silence this warning, pass a
DistributedSampler to your DataLoader.
ie: this:
dataset = myDataset()
dataloader = Dataloader(dataset)
ie: this:
dataset = myDataset()
dataloader = Dataloader(dataset)
becomes:
dataset = myDataset()
dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = Dataloader(dataset, sampler=dist_sampler)
becomes:
dataset = myDataset()
dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = Dataloader(dataset, sampler=dist_sampler)
If you want each process to load the full dataset, ignore this warning.
"""
warnings.warn(msg)
If you want each process to load the full dataset, ignore this warning.
"""
warnings.warn(msg)
if self.use_ddp and self.get_val_dataloaders is not None:
for dataloader in self.get_val_dataloaders():
if not isinstance(dataloader.sampler, DistributedSampler):
msg = """
Your val_dataloader(s) don't use DistributedSampler.
You're using multiple gpus and multiple nodes without using a DistributedSampler
to assign a subset of your data to each process. To silence this warning, pass a
DistributedSampler to your DataLoader.
if self.use_ddp and self.get_val_dataloaders is not None:
for dataloader in self.get_val_dataloaders():
if not isinstance(dataloader.sampler, DistributedSampler):
msg = """
Your val_dataloader(s) don't use DistributedSampler.
You're using multiple gpus and multiple nodes without using a DistributedSampler
to assign a subset of your data to each process. To silence this warning, pass a
DistributedSampler to your DataLoader.
ie: this:
dataset = myDataset()
dataloader = Dataloader(dataset)
ie: this:
dataset = myDataset()
dataloader = Dataloader(dataset)
becomes:
dataset = myDataset()
dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = Dataloader(dataset, sampler=dist_sampler)
becomes:
dataset = myDataset()
dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = Dataloader(dataset, sampler=dist_sampler)
If you want each process to load the full dataset, ignore this warning.
"""
warnings.warn(msg)
break
If you want each process to load the full dataset, ignore this warning.
"""
warnings.warn(msg)
break
if self.use_ddp and self.get_test_dataloaders is not None:
for dataloader in self.get_test_dataloaders():
if not isinstance(dataloader.sampler, DistributedSampler):
msg = """
Your test_dataloader(s) don't use DistributedSampler.
You're using multiple gpus and multiple nodes without using a DistributedSampler
to assign a subset of your data to each process. To silence this warning, pass a
DistributedSampler to your DataLoader.
if self.use_ddp and self.get_test_dataloaders is not None:
for dataloader in self.get_test_dataloaders():
if not isinstance(dataloader.sampler, DistributedSampler):
msg = """
Your test_dataloader(s) don't use DistributedSampler.
You're using multiple gpus and multiple nodes without using a DistributedSampler
to assign a subset of your data to each process. To silence this warning, pass a
DistributedSampler to your DataLoader.
ie: this:
dataset = myDataset()
dataloader = Dataloader(dataset)
ie: this:
dataset = myDataset()
dataloader = Dataloader(dataset)
becomes:
dataset = myDataset()
dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = Dataloader(dataset, sampler=dist_sampler)
becomes:
dataset = myDataset()
dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = Dataloader(dataset, sampler=dist_sampler)
If you want each process to load the full dataset, ignore this warning.
"""
warnings.warn(msg)
break
If you want each process to load the full dataset, ignore this warning.
"""
warnings.warn(msg)
break
# wait for all processes to catch up
dist.barrier()
# load each dataloader
self.get_train_dataloader()
self.get_test_dataloaders()
self.get_val_dataloaders()
# -----------------------------
# MODEL TRAINING