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e1c02e018c
* Add example for callback registry Resolves: #9036 * Update callback registry documentation * Added comments for other ways to register callback
116 lines
4.9 KiB
ReStructuredText
116 lines
4.9 KiB
ReStructuredText
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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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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Here is an example of how to register a custom callback with the PyTorch :class:`~transformers.Trainer`:
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.. code-block:: python
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class MyCallback(TrainerCallback):
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"A callback that prints a message at the beginning of training"
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def on_train_begin(self, args, state, control, **kwargs):
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print("Starting training")
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trainer = Trainer(
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model,
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args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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callbacks=[MyCallback] # We can either pass the callback class this way or an instance of it (MyCallback())
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)
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Another way to register a callback is to call ``trainer.add_callback()`` as follows:
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.. code-block:: python
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trainer = Trainer(...)
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trainer.add_callback(MyCallback)
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# Alternatively, we can pass an instance of the callback class
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trainer.add_callback(MyCallback())
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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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