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 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.

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

TrainerCallback

[[autodoc]] TrainerCallback

Here is an example of how to register a custom callback with the PyTorch [Trainer]:

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:

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