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Fix typos in docstrings

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This commit is contained in:
Pietro Monticone 2023-03-31 22:14:15 +02:00
parent 78c3235eee
commit 49e7f65452
3 changed files with 3 additions and 3 deletions
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2
core/custom_training_loop.py
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@ -115,7 +115,7 @@ def train(
dataset: data iterator for the training set dataset: data iterator for the training set
evaluation_iterators: data iterators for the different evaluation sets evaluation_iterators: data iterators for the different evaluation sets
scheduler: optional learning rate scheduler scheduler: optional learning rate scheduler
output_transform_for_metrics: optional transformation functions to transorm the model output_transform_for_metrics: optional transformation functions to transform the model
output and labels into a format the metrics can understand output and labels into a format the metrics can understand
""" """

2
core/debug_training_loop.py
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@ -1,6 +1,6 @@
"""This is a very limited feature training loop useful for interactive debugging. """This is a very limited feature training loop useful for interactive debugging.
It is not intended for actual model tranining (it is not fast, doesn't compile the model). It is not intended for actual model training (it is not fast, doesn't compile the model).
It does not support checkpointing. It does not support checkpointing.
suggested use: suggested use:

2
core/train_pipeline.py
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@ -57,7 +57,7 @@ def _wait_for_batch(batch: In, stream: Optional[torch.cuda.streams.Stream]) -> N
torch.cuda.current_stream().wait_stream(stream) torch.cuda.current_stream().wait_stream(stream)
# As mentioned in https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html, # As mentioned in https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html,
# PyTorch uses the "caching allocator" for memory allocation for tensors. When a tensor is # PyTorch uses the "caching allocator" for memory allocation for tensors. When a tensor is
# freed, its memory is likely to be reused by newly constructed tenosrs. By default, # freed, its memory is likely to be reused by newly constructed tensors. By default,
# this allocator traces whether a tensor is still in use by only the CUDA stream where it # this allocator traces whether a tensor is still in use by only the CUDA stream where it
# was created. When a tensor is used by additional CUDA streams, we need to call record_stream # was created. When a tensor is used by additional CUDA streams, we need to call record_stream
# to tell the allocator about all these streams. Otherwise, the allocator might free the # to tell the allocator about all these streams. Otherwise, the allocator might free the