PyTorch Lightning

Overview

PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining full flexibility. Automate training workflows, multi-device orchestration, and implement best practices for neural network training and scaling across multiple GPUs/TPUs.

When to Use This Skill

This skill should be used when:

• Building, training, or deploying neural networks using PyTorch Lightning

• Organizing PyTorch code into LightningModules

• Configuring Trainers for multi-GPU/TPU training

• Implementing data pipelines with LightningDataModules

• Working with callbacks, logging, and distributed training strategies (DDP, FSDP, DeepSpeed)

• Structuring deep learning projects professionally

Core Capabilities

1. LightningModule - Model Definition

Organize PyTorch models into six logical sections:

• Initialization - init() and setup()

• Training Loop - training_step(batch, batch_idx)

• Validation Loop - validation_step(batch, batch_idx)

• Test Loop - test_step(batch, batch_idx)

• Prediction - predict_step(batch, batch_idx)

• Optimizer Configuration - configure_optimizers()

Quick template reference: See scripts/template_lightning_module.py for a complete boilerplate.

Detailed documentation: Read references/lightning_module.md for comprehensive method documentation, hooks, properties, and best practices.

2. Trainer - Training Automation

The Trainer automates the training loop, device management, gradient operations, and callbacks. Key features:

• Multi-GPU/TPU support with strategy selection (DDP, FSDP, DeepSpeed)

• Automatic mixed precision training

• Gradient accumulation and clipping

• Checkpointing and early stopping

• Progress bars and logging

Quick setup reference: See scripts/quick_trainer_setup.py for common Trainer configurations.

Detailed documentation: Read references/trainer.md for all parameters, methods, and configuration options.

3. LightningDataModule - Data Pipeline Organization

Encapsulate all data processing steps in a reusable class:

• prepare_data()

• Download and process data (single-process)

• setup()

• Create datasets and apply transforms (per-GPU)

• train_dataloader()

• Return training DataLoader

• val_dataloader()

• Return validation DataLoader

• test_dataloader()

• Return test DataLoader

Quick template reference: See scripts/template_datamodule.py for a complete boilerplate.

Detailed documentation: Read references/data_module.md for method details and usage patterns.

4. Callbacks - Extensible Training Logic

Add custom functionality at specific training hooks without modifying your LightningModule. Built-in callbacks include:

• ModelCheckpoint - Save best/latest models

• EarlyStopping - Stop when metrics plateau

• LearningRateMonitor - Track LR scheduler changes

• BatchSizeFinder - Auto-determine optimal batch size

Detailed documentation: Read references/callbacks.md for built-in callbacks and custom callback creation.

5. Logging - Experiment Tracking

Integrate with multiple logging platforms:

• TensorBoard (default)

• Weights & Biases (WandbLogger)

• MLflow (MLFlowLogger)

• Neptune (NeptuneLogger)

• Comet (CometLogger)

• CSV (CSVLogger)

Log metrics using self.log("metric_name", value) in any LightningModule method.

Detailed documentation: Read references/logging.md for logger setup and configuration.

6. Distributed Training - Scale to Multiple Devices

Choose the right strategy based on model size:

• DDP - For models <500M parameters (ResNet, smaller transformers)

• FSDP - For models 500M+ parameters (large transformers, recommended for Lightning users)

• DeepSpeed - For cutting-edge features and fine-grained control

Configure with: Trainer(strategy="ddp", accelerator="gpu", devices=4)

Detailed documentation: Read references/distributed_training.md for strategy comparison and configuration.

7. Best Practices

• Device agnostic code - Use self.device instead of .cuda()

• Hyperparameter saving - Use self.save_hyperparameters() in init()

• Metric logging - Use self.log() for automatic aggregation across devices

• Reproducibility - Use seed_everything() and Trainer(deterministic=True)

• Debugging - Use Trainer(fast_dev_run=True) to test with 1 batch

Detailed documentation: Read references/best_practices.md for common patterns and pitfalls.

Quick Workflow

Define model:

class MyModel(L.LightningModule): def init(self): super().init() self.save_hyperparameters() self.model = YourNetwork()

def training_step(self, batch, batch_idx):
    x, y = batch
    loss = F.cross_entropy(self.model(x), y)
    self.log("train_loss", loss)
    return loss

def configure_optimizers(self):
    return torch.optim.Adam(self.parameters())

- Prepare data:

Option 1: Direct DataLoaders

train_loader = DataLoader(train_dataset, batch_size=32)

Option 2: LightningDataModule (recommended for reusability)

dm = MyDataModule(batch_size=32)

Train:

trainer = L.Trainer(max_epochs=10, accelerator="gpu", devices=2) trainer.fit(model, train_loader) # or trainer.fit(model, datamodule=dm)

Resources

scripts/

Executable Python templates for common PyTorch Lightning patterns:

• template_lightning_module.py

• Complete LightningModule boilerplate

• template_datamodule.py

• Complete LightningDataModule boilerplate

• quick_trainer_setup.py

• Common Trainer configuration examples

references/

Detailed documentation for each PyTorch Lightning component:

• lightning_module.md

• Comprehensive LightningModule guide (methods, hooks, properties)

• trainer.md

• Trainer configuration and parameters

• data_module.md

• LightningDataModule patterns and methods

• callbacks.md

• Built-in and custom callbacks

• logging.md

• Logger integrations and usage

• distributed_training.md

• DDP, FSDP, DeepSpeed comparison and setup

• best_practices.md

• Common patterns, tips, and pitfalls