119 Callbacks Aa2140e7

Callbacks - Comprehensive Guide

Overview

Callbacks enable adding arbitrary self-contained programs to training without cluttering your LightningModule research code. They execute custom logic at specific hooks during the training lifecycle.

Architecture

Lightning organizes training logic across three components:

• Trainer - Engineering infrastructure
• LightningModule - Research code
• Callbacks - Non-essential functionality (monitoring, checkpointing, custom behaviors)

Creating Custom Callbacks

Basic structure:

from lightning.pytorch.callbacks import Callback

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

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

# Use with Trainer
trainer = L.Trainer(callbacks=[MyCustomCallback()])

Built-in Callbacks

ModelCheckpoint

Save models based on monitored metrics.

Key Parameters:

• dirpath - Directory to save checkpoints
• filename - Checkpoint filename pattern
• monitor - Metric to monitor
• mode - "min" or "max" for monitored metric
• save_top_k - Number of best models to keep
• save_last - Save last epoch checkpoint
• every_n_epochs - Save every N epochs
• save_on_train_epoch_end - Save at train epoch end vs validation end

Examples:

from lightning.pytorch.callbacks import ModelCheckpoint

# Save top 3 models based on validation loss
checkpoint_callback = ModelCheckpoint(
    dirpath="checkpoints/",
    filename="model-{epoch:02d}-{val_loss:.2f}",
    monitor="val_loss",
    mode="min",
    save_top_k=3,
    save_last=True
)

# Save every 10 epochs
checkpoint_callback = ModelCheckpoint(
    dirpath="checkpoints/",
    filename="model-{epoch:02d}",
    every_n_epochs=10,
    save_top_k=-1  # Save all
)

# Save best model based on accuracy
checkpoint_callback = ModelCheckpoint(
    dirpath="checkpoints/",
    filename="best-model",
    monitor="val_acc",
    mode="max",
    save_top_k=1
)

trainer = L.Trainer(callbacks=[checkpoint_callback])

Accessing Saved Checkpoints:

# Get best model path
best_model_path = checkpoint_callback.best_model_path

# Get last checkpoint path
last_checkpoint = checkpoint_callback.last_model_path

# Get all checkpoint paths
all_checkpoints = checkpoint_callback.best_k_models

EarlyStopping

Stop training when a monitored metric stops improving.

Key Parameters:

• monitor - Metric to monitor
• patience - Number of epochs with no improvement after which training stops
• mode - "min" or "max" for monitored metric
• min_delta - Minimum change to qualify as an improvement
• verbose - Print messages
• strict - Crash if monitored metric not found

Examples:

from lightning.pytorch.callbacks import EarlyStopping

# Stop when validation loss stops improving
early_stop = EarlyStopping(
    monitor="val_loss",
    patience=10,
    mode="min",
    verbose=True
)

# Stop when accuracy plateaus
early_stop = EarlyStopping(
    monitor="val_acc",
    patience=5,
    mode="max",
    min_delta=0.001  # Must improve by at least 0.001
)

trainer = L.Trainer(callbacks=[early_stop])

LearningRateMonitor

Track learning rate changes from schedulers.

Key Parameters:

• logging_interval - When to log: "step" or "epoch"
• log_momentum - Also log momentum values

Example:

from lightning.pytorch.callbacks import LearningRateMonitor

lr_monitor = LearningRateMonitor(logging_interval="step")
trainer = L.Trainer(callbacks=[lr_monitor])

# Logs learning rate automatically as "lr-{optimizer_name}"

DeviceStatsMonitor

Log device performance metrics (GPU/CPU/TPU).

Key Parameters:

• cpu_stats - Log CPU stats

Example:

from lightning.pytorch.callbacks import DeviceStatsMonitor

device_stats = DeviceStatsMonitor(cpu_stats=True)
trainer = L.Trainer(callbacks=[device_stats])

# Logs: gpu_utilization, gpu_memory_usage, etc.

ModelSummary / RichModelSummary

Display model architecture and parameter count.

Example:

from lightning.pytorch.callbacks import ModelSummary, RichModelSummary

# Basic summary
summary = ModelSummary(max_depth=2)

# Rich formatted summary (prettier)
rich_summary = RichModelSummary(max_depth=3)

trainer = L.Trainer(callbacks=[rich_summary])

Timer

Track and limit training duration.

Key Parameters:

• duration - Maximum training time (timedelta or dict)
• interval - Check interval: "step", "epoch", or "batch"

Example:

from lightning.pytorch.callbacks import Timer
from datetime import timedelta

# Limit training to 1 hour
timer = Timer(duration=timedelta(hours=1))

# Or using dict
timer = Timer(duration={"hours": 23, "minutes": 30})

trainer = L.Trainer(callbacks=[timer])

BatchSizeFinder

Automatically find the optimal batch size.

Example:

from lightning.pytorch.callbacks import BatchSizeFinder

batch_finder = BatchSizeFinder(mode="power", steps_per_trial=3)

trainer = L.Trainer(callbacks=[batch_finder])
trainer.fit(model, datamodule=dm)

# Optimal batch size is set automatically

GradientAccumulationScheduler

Schedule gradient accumulation steps dynamically.

Example:

from lightning.pytorch.callbacks import GradientAccumulationScheduler

# Accumulate 4 batches for first 5 epochs, then 2 batches
accumulator = GradientAccumulationScheduler(scheduling={0: 4, 5: 2})

trainer = L.Trainer(callbacks=[accumulator])

StochasticWeightAveraging (SWA)

Apply stochastic weight averaging for better generalization.

Example:

from lightning.pytorch.callbacks import StochasticWeightAveraging

swa = StochasticWeightAveraging(swa_lrs=1e-2, swa_epoch_start=0.8)

trainer = L.Trainer(callbacks=[swa])

Custom Callback Examples

Simple Logging Callback

class MetricsLogger(Callback):
    def __init__(self):
        self.metrics = []

    def on_validation_end(self, trainer, pl_module):
        # Access logged metrics
        metrics = trainer.callback_metrics
        self.metrics.append(dict(metrics))
        print(f"Validation metrics: {metrics}")

Gradient Monitoring Callback

class GradientMonitor(Callback):
    def on_after_backward(self, trainer, pl_module):
        # Log gradient norms
        for name, param in pl_module.named_parameters():
            if param.grad is not None:
                grad_norm = param.grad.norm().item()
                pl_module.log(f"grad_norm/{name}", grad_norm)

Custom Checkpointing Callback

class CustomCheckpoint(Callback):
    def __init__(self, save_dir):
        self.save_dir = save_dir

    def on_train_epoch_end(self, trainer, pl_module):
        epoch = trainer.current_epoch
        if epoch % 5 == 0:  # Save every 5 epochs
            filepath = f"{self.save_dir}/custom-{epoch}.ckpt"
            trainer.save_checkpoint(filepath)
            print(f"Saved checkpoint: {filepath}")

Model Freezing Callback

class FreezeUnfreeze(Callback):
    def __init__(self, freeze_until_epoch=10):
        self.freeze_until_epoch = freeze_until_epoch

    def on_train_epoch_start(self, trainer, pl_module):
        epoch = trainer.current_epoch

        if epoch < self.freeze_until_epoch:
            # Freeze backbone
            for param in pl_module.backbone.parameters():
                param.requires_grad = False
        else:
            # Unfreeze backbone
            for param in pl_module.backbone.parameters():
                param.requires_grad = True

Learning Rate Finder Callback

class LRFinder(Callback):
    def __init__(self, min_lr=1e-5, max_lr=1e-1, num_steps=100):
        self.min_lr = min_lr
        self.max_lr = max_lr
        self.num_steps = num_steps
        self.lrs = []
        self.losses = []

    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
        if batch_idx >= self.num_steps:
            trainer.should_stop = True
            return

        # Exponential LR schedule
        lr = self.min_lr * (self.max_lr / self.min_lr) ** (batch_idx / self.num_steps)
        optimizer = trainer.optimizers[0]
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr

        self.lrs.append(lr)
        self.losses.append(outputs['loss'].item())

    def on_train_end(self, trainer, pl_module):
        # Plot LR vs Loss
        import matplotlib.pyplot as plt
        plt.plot(self.lrs, self.losses)
        plt.xscale('log')
        plt.xlabel('Learning Rate')
        plt.ylabel('Loss')
        plt.savefig('lr_finder.png')

Prediction Saver Callback

class PredictionSaver(Callback):
    def __init__(self, save_path):
        self.save_path = save_path
        self.predictions = []

    def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
        self.predictions.append(outputs)

    def on_predict_end(self, trainer, pl_module):
        # Save all predictions
        torch.save(self.predictions, self.save_path)
        print(f"Predictions saved to {self.save_path}")

Available Hooks

Setup and Teardown

• setup(trainer, pl_module, stage) - Called at beginning of fit/test/predict
• teardown(trainer, pl_module, stage) - Called at end of fit/test/predict

Training Lifecycle

• on_fit_start(trainer, pl_module) - Called at start of fit
• on_fit_end(trainer, pl_module) - Called at end of fit
• on_train_start(trainer, pl_module) - Called at start of training
• on_train_end(trainer, pl_module) - Called at end of training

Epoch Boundaries

• on_train_epoch_start(trainer, pl_module) - Called at start of training epoch
• on_train_epoch_end(trainer, pl_module) - Called at end of training epoch
• on_validation_epoch_start(trainer, pl_module) - Called at start of validation
• on_validation_epoch_end(trainer, pl_module) - Called at end of validation
• on_test_epoch_start(trainer, pl_module) - Called at start of test
• on_test_epoch_end(trainer, pl_module) - Called at end of test

Batch Boundaries

• on_train_batch_start(trainer, pl_module, batch, batch_idx) - Before training batch
• on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx) - After training batch
• on_validation_batch_start(trainer, pl_module, batch, batch_idx) - Before validation batch
• on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx) - After validation batch

Gradient Events

• on_before_backward(trainer, pl_module, loss) - Before loss.backward()
• on_after_backward(trainer, pl_module) - After loss.backward()
• on_before_optimizer_step(trainer, pl_module, optimizer) - Before optimizer.step()

Checkpoint Events

• on_save_checkpoint(trainer, pl_module, checkpoint) - When saving checkpoint
• on_load_checkpoint(trainer, pl_module, checkpoint) - When loading checkpoint

Exception Handling

• on_exception(trainer, pl_module, exception) - When exception occurs

State Management

For callbacks requiring persistence across checkpoints:

class StatefulCallback(Callback):
    def __init__(self):
        self.counter = 0

    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
        self.counter += 1

    def state_dict(self):
        return {"counter": self.counter}

    def load_state_dict(self, state_dict):
        self.counter = state_dict["counter"]

    @property
    def state_key(self):
        # Unique identifier for this callback
        return "my_stateful_callback"

Best Practices

1. Keep Callbacks Isolated

Each callback should be self-contained and independent:

# Good: Self-contained
class MyCallback(Callback):
    def __init__(self):
        self.data = []

    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
        self.data.append(outputs['loss'].item())

# Bad: Depends on external state
global_data = []

class BadCallback(Callback):
    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
        global_data.append(outputs['loss'].item())  # External dependency

2. Avoid Inter-Callback Dependencies

Callbacks should not depend on other callbacks:

# Bad: Callback B depends on Callback A
class CallbackA(Callback):
    def __init__(self):
        self.value = 0

class CallbackB(Callback):
    def __init__(self, callback_a):
        self.callback_a = callback_a  # Tight coupling

# Good: Independent callbacks
class CallbackA(Callback):
    def __init__(self):
        self.value = 0

class CallbackB(Callback):
    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
        # Access trainer state instead
        value = trainer.callback_metrics.get('metric')

3. Never Manually Invoke Callback Methods

Let Lightning call callbacks automatically:

# Bad: Manual invocation
callback = MyCallback()
callback.on_train_start(trainer, model)  # Don't do this

# Good: Let Trainer handle it
trainer = L.Trainer(callbacks=[MyCallback()])

4. Design for Any Execution Order

Callbacks may execute in any order, so don't rely on specific ordering:

# Good: Order-independent
class GoodCallback(Callback):
    def on_train_epoch_end(self, trainer, pl_module):
        # Use trainer state, not other callbacks
        metrics = trainer.callback_metrics
        self.log_metrics(metrics)

5. Use Callbacks for Non-Essential Logic

Keep core research code in LightningModule, use callbacks for auxiliary functionality:

# Good separation
class MyModel(L.LightningModule):
    # Core research logic here
    def training_step(self, batch, batch_idx):
        return loss

# Non-essential monitoring in callback
class MonitorCallback(Callback):
    def on_validation_end(self, trainer, pl_module):
        # Monitoring logic
        pass

Common Patterns

Combining Multiple Callbacks

from lightning.pytorch.callbacks import (
    ModelCheckpoint,
    EarlyStopping,
    LearningRateMonitor,
    DeviceStatsMonitor
)

callbacks = [
    ModelCheckpoint(monitor="val_loss", mode="min", save_top_k=3),
    EarlyStopping(monitor="val_loss", patience=10, mode="min"),
    LearningRateMonitor(logging_interval="step"),
    DeviceStatsMonitor()
]

trainer = L.Trainer(callbacks=callbacks)

Conditional Callback Activation

class ConditionalCallback(Callback):
    def __init__(self, activate_after_epoch=10):
        self.activate_after_epoch = activate_after_epoch

    def on_train_epoch_end(self, trainer, pl_module):
        if trainer.current_epoch >= self.activate_after_epoch:
            # Only active after specified epoch
            self.do_something(trainer, pl_module)

Multi-Stage Training Callback

class MultiStageTraining(Callback):
    def __init__(self, stage_epochs=[10, 20, 30]):
        self.stage_epochs = stage_epochs
        self.current_stage = 0

    def on_train_epoch_start(self, trainer, pl_module):
        epoch = trainer.current_epoch

        if epoch in self.stage_epochs:
            self.current_stage += 1
            print(f"Entering stage {self.current_stage}")

            # Adjust learning rate for new stage
            for optimizer in trainer.optimizers:
                for param_group in optimizer.param_groups:
                    param_group['lr'] *= 0.1