Updated ModelCheckpoint callback documentation #6873
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Signed-off-by: gca <[email protected]>
george-gca
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| Save the model after every epoch by monitoring a quantity. Every metric logged with | ||
| :meth:`~pytorch_lightning.core.lightning.log` or :meth:`~pytorch_lightning.core.lightning.log_dict` in | ||
| LightningModule is a candidate for the monitor key. For more information, see | ||
| :ref:`common/weights_loading:Checkpoint saving`. |
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not 100% sure, but I think we can only reference sections by title within the same document.
You probably have to refer to just :ref:_weights_loading
| class ModelCheckpoint(Callback): | ||
| r""" | ||
| Save the model after every epoch by monitoring a quantity. | ||
| Save the model after every epoch by monitoring a quantity. Every metric logged with |
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Is this an old version of Lightning? the "after every epoch" isn't true - there are more options being added for greater flexibility. See https://github.com/PyTorchLightning/pytorch-lightning/blob/19e67d18c472c3a03dec4dd9bfcef031e9ca8719/pytorch_lightning/callbacks/model_checkpoint.py#L96-L107
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Actually it was like that in the documentation, that's why I didn't change the after every epoch part. But you are right. What do you think about this before the part that I added:
Save the model after every every_n_train_steps training steps or every every_n_val_epochs validation epochs by monitoring a quantity. These options are mutually exclusive.
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I would not mention the arguments before they are introduced later on.
How about:
Save the model periodically by monitoring a quantity. ...
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Signed-off-by: gca [email protected]
What does this PR do?
Fixes #6117. It tries to clarify the documentation regarding monitoring metric values.
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