/pytorch-lightning

name: pytorch-lightning
description: "Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training."

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:**

   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())

2. **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)

3. **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 Lightn