knowledge_base/main_classes_callback.txt

Callbacks
Callbacks are objects that can customize the behavior of the training loop in the PyTorch
[Trainer] (this feature is not yet implemented in TensorFlow) that can inspect the training loop
state (for progress reporting, logging on TensorBoard or other ML platforms) and take decisions (like early
stopping).
Callbacks are "read only" pieces of code, apart from the [TrainerControl] object they return, they
cannot change anything in the training loop. For customizations that require changes in the training loop, you should
subclass [Trainer] and override the methods you need (see trainer for examples).
By default, TrainingArguments.report_to is set to "all", so a [Trainer] will use the following callbacks.

[DefaultFlowCallback] which handles the default behavior for logging, saving and evaluation.
[PrinterCallback] or [ProgressCallback] to display progress and print the
  logs (the first one is used if you deactivate tqdm through the [TrainingArguments], otherwise
  it's the second one).
[~integrations.TensorBoardCallback] if tensorboard is accessible (either through PyTorch >= 1.4
  or tensorboardX).
[~integrations.WandbCallback] if wandb is installed.
[~integrations.CometCallback] if comet_ml is installed.
[~integrations.MLflowCallback] if mlflow is installed.
[~integrations.NeptuneCallback] if neptune is installed.
[~integrations.AzureMLCallback] if azureml-sdk is
  installed.
[~integrations.CodeCarbonCallback] if codecarbon is
  installed.
[~integrations.ClearMLCallback] if clearml is installed.
[~integrations.DagsHubCallback] if dagshub is installed.
[~integrations.FlyteCallback] if flyte is installed.
[~integrations.DVCLiveCallback] if dvclive is installed.

If a package is installed but you don't wish to use the accompanying integration, you can change TrainingArguments.report_to to a list of just those integrations you want to use (e.g. ["azure_ml", "wandb"]). 
The main class that implements callbacks is [TrainerCallback]. It gets the
[TrainingArguments] used to instantiate the [Trainer], can access that
Trainer's internal state via [TrainerState], and can take some actions on the training loop via
[TrainerControl].
Available Callbacks
Here is the list of the available [TrainerCallback] in the library:
[[autodoc]] integrations.CometCallback
    - setup
[[autodoc]] DefaultFlowCallback
[[autodoc]] PrinterCallback
[[autodoc]] ProgressCallback
[[autodoc]] EarlyStoppingCallback
[[autodoc]] integrations.TensorBoardCallback
[[autodoc]] integrations.WandbCallback
    - setup
[[autodoc]] integrations.MLflowCallback
    - setup
[[autodoc]] integrations.AzureMLCallback
[[autodoc]] integrations.CodeCarbonCallback
[[autodoc]] integrations.NeptuneCallback
[[autodoc]] integrations.ClearMLCallback
[[autodoc]] integrations.DagsHubCallback
[[autodoc]] integrations.FlyteCallback
[[autodoc]] integrations.DVCLiveCallback
    - setup
TrainerCallback
[[autodoc]] TrainerCallback
Here is an example of how to register a custom callback with the PyTorch [Trainer]:
thon
class MyCallback(TrainerCallback):
    "A callback that prints a message at the beginning of training"
def on_train_begin(self, args, state, control, **kwargs):
    print("Starting training")

trainer = Trainer(
    model,
    args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    callbacks=[MyCallback],  # We can either pass the callback class this way or an instance of it (MyCallback())
)

Another way to register a callback is to call trainer.add_callback() as follows:
thon
trainer = Trainer()
trainer.add_callback(MyCallback)
Alternatively, we can pass an instance of the callback class
trainer.add_callback(MyCallback())

TrainerState
[[autodoc]] TrainerState
TrainerControl
[[autodoc]] TrainerControl