High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

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: 1. Initialization init () and setup() 2. Training Loop training step(batch, batch idx) 3. Validation Loop validation step(batch, batch idx) 4. Test Loop test step(batch, batch idx) 5. Prediction predict step(batch, batch idx) 6. 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: 1. prepare data() Download and process data (single process) 2. setup() Create datasets and apply transforms (per GPU) 3. train dataloader() Return training DataLoader 4. val dataloader() Return validation DataLoader 5. 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 1. Define model: 2. Prepare data: 3. Train: 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

pytorch-lightning