K-Dense-AI/pytorch-lightning

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 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