Callback — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)

Callbacks allow you to add arbitrary self-contained programs to your training. At specific points during the flow of execution (hooks), the Callback interface allows you to design programs that encapsulate a full set of functionality. It de-couples functionality that does not need to be in the lightning module and can be shared across projects.

Lightning has a callback system to execute them when needed. Callbacks should capture NON-ESSENTIAL logic that is NOT required for your lightning module to run.

A complete list of Callback hooks can be found in Callback.

An overall Lightning system should have:

Trainer for all engineering
LightningModule for all research code.
Callbacks for non-essential code.

Example:

from lightning.pytorch.callbacks import Callback

class MyPrintingCallback(Callback): def on_train_start(self, trainer, pl_module): print("Training is starting")

def on_train_end(self, trainer, pl_module):
    print("Training is ending")

trainer = Trainer(callbacks=[MyPrintingCallback()])

We successfully extended functionality without polluting our super cleanlightning module research code.

You can do pretty much anything with callbacks.

Built-in Callbacks¶

Lightning has a few built-in callbacks.

Note

For a richer collection of callbacks, check out ourbolts library.

BackboneFinetuning	Finetune a backbone model based on a learning rate user-defined scheduling.
BaseFinetuning	This class implements the base logic for writing your own Finetuning Callback.
BasePredictionWriter	Base class to implement how the predictions should be stored.
BatchSizeFinder	Finds the largest batch size supported by a given model before encountering an out of memory (OOM) error.
Callback	Abstract base class used to build new callbacks.
DeviceStatsMonitor	Automatically monitors and logs device stats during training, validation and testing stage.
EarlyStopping	Monitor a metric and stop training when it stops improving.
GradientAccumulationScheduler	Change gradient accumulation factor according to scheduling.
LambdaCallback	Create a simple callback on the fly using lambda functions.
LearningRateFinder	The LearningRateFinder callback enables the user to do a range test of good initial learning rates, to reduce the amount of guesswork in picking a good starting learning rate.
LearningRateMonitor	Automatically monitor and logs learning rate for learning rate schedulers during training.
ModelCheckpoint	Save the model periodically by monitoring a quantity.
ModelPruning	Model pruning Callback, using PyTorch's prune utilities.
ModelSummary	Generates a summary of all layers in a LightningModule.
ProgressBar	The base class for progress bars in Lightning.
RichModelSummary	Generates a summary of all layers in a LightningModule with rich text formatting.
RichProgressBar	Create a progress bar with rich text formatting.
StochasticWeightAveraging	Implements the Stochastic Weight Averaging (SWA) Callback to average a model.
Timer	The Timer callback tracks the time spent in the training, validation, and test loops and interrupts the Trainer if the given time limit for the training loop is reached.
TQDMProgressBar	This is the default progress bar used by Lightning.

Save Callback state¶

Some callbacks require internal state in order to function properly. You can optionally choose to persist your callback’s state as part of model checkpoint files usingstate_dict() and load_state_dict(). Note that the returned state must be able to be pickled.

When your callback is meant to be used only as a singleton callback then implementing the above two hooks is enough to persist state effectively. However, if passing multiple instances of the callback to the Trainer is supported, then the callback must define a state_key property in order for Lightning to be able to distinguish the different states when loading the callback state. This concept is best illustrated by the following example.

class Counter(Callback): def init(self, what="epochs", verbose=True): self.what = what self.verbose = verbose self.state = {"epochs": 0, "batches": 0}

@property
def state_key(self) -> str:
    # note: we do not include `verbose` here on purpose
    return f"Counter[what={self.what}]"

def on_train_epoch_end(self, *args, **kwargs):
    if self.what == "epochs":
        self.state["epochs"] += 1

def on_train_batch_end(self, *args, **kwargs):
    if self.what == "batches":
        self.state["batches"] += 1

def load_state_dict(self, state_dict):
    self.state.update(state_dict)

def state_dict(self):
    return self.state.copy()

two callbacks of the same type are being used

trainer = Trainer(callbacks=[Counter(what="epochs"), Counter(what="batches")])

A Lightning checkpoint from this Trainer with the two stateful callbacks will include the following information:

{ "state_dict": ..., "callbacks": { "Counter{'what': 'batches'}": {"batches": 32, "epochs": 0}, "Counter{'what': 'epochs'}": {"batches": 0, "epochs": 2}, ... } }

The implementation of a state_key is essential here. If it were missing, Lightning would not be able to disambiguate the state for these two callbacks, and state_keyby default only defines the class name as the key, e.g., here Counter.

Best Practices¶

The following are best practices when using/designing callbacks.

Callbacks should be isolated in their functionality.
Your callback should not rely on the behavior of other callbacks in order to work properly.
Do not manually call methods from the callback.
Directly calling methods (eg. on_validation_end) is strongly discouraged.
Whenever possible, your callbacks should not depend on the order in which they are executed.

Entry Points¶

Lightning supports registering Trainer callbacks directly throughEntry Points. Entry points allow an arbitrary package to include callbacks that the Lightning Trainer can automatically use, without you having to add them to the Trainer manually. This is useful in production environments where it is common to provide specialized monitoring and logging callbacks globally for every application.

Here is a callback factory function that returns two special callbacks:

factories.py¶

def my_custom_callbacks_factory(): return [MyCallback1(), MyCallback2()]

If we make this factories.py file into an installable package, we can define an entry point for this factory function. Here is a minimal example of the setup.py file for the package my-package:

setup.py¶

from setuptools import setup

setup( name="my-package", version="0.0.1", install_requires=["lightning"], entry_points={ "lightning.pytorch.callbacks_factory": [ # The format here must be [any name]=[module path]:[function name] "monitor_callbacks=factories:my_custom_callbacks_factory" ] }, )

The group name for the entry points is lightning.pytorch.callbacks_factory and it contains a list of strings that specify where to find the function within the package.

Now, if you pip install -e . this package, it will register the my_custom_callbacks_factory function and Lightning will automatically call it to collect the callbacks whenever you run the Trainer!

To unregister the factory, simply uninstall the package with pip uninstall “my-package”.

Callback API¶

Here is the full API of methods available in the Callback base class.

The Callback class is the base for all the callbacks in Lightning just like the LightningModule is the base for all models. It defines a public interface that each callback implementation must follow, the key ones are:

Properties¶

state_key¶

Callback.state_key

Identifier for the state of the callback.

Used to store and retrieve a callback’s state from the checkpoint dictionary bycheckpoint["callbacks"][state_key]. Implementations of a callback need to provide a unique state key if 1) the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.

Hooks¶

setup¶

Callback.setup(trainer, pl_module, stage)[source]

Called when fit, validate, test, predict, or tune begins.

Return type:

Callback — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)

Built-in Callbacks¶

Save Callback state¶

two callbacks of the same type are being used

Best Practices¶

Entry Points¶

Callback API¶

Properties¶

state_key¶

Hooks¶

setup¶

teardown¶

on_fit_start¶

on_fit_end¶

on_sanity_check_start¶

on_sanity_check_end¶

on_train_batch_start¶

on_train_batch_end¶

on_train_epoch_start¶

on_train_epoch_end¶

on_validation_epoch_start¶

on_validation_epoch_end¶

on_test_epoch_start¶

on_test_epoch_end¶

on_predict_epoch_start¶

on_predict_epoch_end¶

on_validation_batch_start¶

on_validation_batch_end¶

on_test_batch_start¶

on_test_batch_end¶

on_predict_batch_start¶

on_predict_batch_end¶

on_train_start¶

on_train_end¶

on_validation_start¶

on_validation_end¶

on_test_start¶

on_test_end¶

on_predict_start¶

on_predict_end¶

on_exception¶

state_dict¶

on_save_checkpoint¶

load_state_dict¶

on_load_checkpoint¶

on_before_backward¶

on_after_backward¶

on_before_optimizer_step¶

on_before_zero_grad¶