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* Add early stopping patience and minimum threshold metric must improve to prevent early stopping to pytorch trainer * Add early stopping test * Set patience counter to 0 if best metric not defined yet * Make early stopping a callback. Add callback event for updating the best metric for early stopping callback to trigger on. * Run make style * make funciton name sensible * Improve new argument docstring wording and hope that flakey CI test passes. * Use on_evaluation callback instead of custom. Remove some debug printing * Move early stopping arguments and state into early stopping callback * Run make style * Remove old code * Fix docs formatting. make style went rogue on me. * Remove copied attributes and fix variable * Add assertions on training arguments instead of mutating them. Move comment out of public docs. * Make separate test for early stopping callback. Add test of invalid arguments. * Run make style... I remembered before CI this time! * appease flake8 * Add EarlyStoppingCallback to callback docs * Make docstring EarlyStoppingCallabck match other callbacks. * Fix typo in docs
78 lines
3.5 KiB
ReStructuredText
78 lines
3.5 KiB
ReStructuredText
Callbacks
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-----------------------------------------------------------------------------------------------------------------------
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Callbacks are objects that can customize the behavior of the training loop in the PyTorch
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:class:`~transformers.Trainer` (this feature is not yet implemented in TensorFlow) that can inspect the training loop
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state (for progress reporting, logging on TensorBoard or other ML platforms...) and take decisions (like early
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stopping).
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Callbacks are "read only" pieces of code, apart from the :class:`~transformers.TrainerControl` object they return, they
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cannot change anything in the training loop. For customizations that require changes in the training loop, you should
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subclass :class:`~transformers.Trainer` and override the methods you need (see :doc:`trainer` for examples).
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By default a :class:`~transformers.Trainer` will use the following callbacks:
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- :class:`~transformers.DefaultFlowCallback` which handles the default behavior for logging, saving and evaluation.
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- :class:`~transformers.PrinterCallback` or :class:`~transformers.ProgressCallback` to display progress and print the
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logs (the first one is used if you deactivate tqdm through the :class:`~transformers.TrainingArguments`, otherwise
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it's the second one).
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- :class:`~transformers.integrations.TensorBoardCallback` if tensorboard is accessible (either through PyTorch >= 1.4
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or tensorboardX).
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- :class:`~transformers.integrations.WandbCallback` if `wandb <https://www.wandb.com/>`__ is installed.
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- :class:`~transformers.integrations.CometCallback` if `comet_ml <https://www.comet.ml/site/>`__ is installed.
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- :class:`~transformers.integrations.MLflowCallback` if `mlflow <https://www.mlflow.org/>`__ is installed.
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- :class:`~transformers.integrations.AzureMLCallback` if `azureml-sdk <https://pypi.org/project/azureml-sdk/>`__ is
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installed.
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The main class that implements callbacks is :class:`~transformers.TrainerCallback`. It gets the
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:class:`~transformers.TrainingArguments` used to instantiate the :class:`~transformers.Trainer`, can access that
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Trainer's internal state via :class:`~transformers.TrainerState`, and can take some actions on the training loop via
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:class:`~transformers.TrainerControl`.
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Available Callbacks
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Here is the list of the available :class:`~transformers.TrainerCallback` in the library:
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.. autoclass:: transformers.integrations.CometCallback
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:members: setup
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.. autoclass:: transformers.DefaultFlowCallback
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.. autoclass:: transformers.PrinterCallback
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.. autoclass:: transformers.ProgressCallback
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.. autoclass:: transformers.EarlyStoppingCallback
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.. autoclass:: transformers.integrations.TensorBoardCallback
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.. autoclass:: transformers.integrations.WandbCallback
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:members: setup
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.. autoclass:: transformers.integrations.MLflowCallback
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:members: setup
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.. autoclass:: transformers.integrations.AzureMLCallback
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TrainerCallback
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TrainerCallback
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:members:
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TrainerState
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TrainerState
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:members:
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TrainerControl
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TrainerControl
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:members:
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