Summary Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard, MLflow), 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.
Current upstream: lightning 2.6.4 (PyPI, May 2026). Docs: lightning.ai/docs/pytorch/stable . Use import lightning as L (the pytorch-lightning package name still installs the same library).
Installation
uv pip install lightning
Optional extras:
uv pip install lightning[extra] # loggers, strategies, etc.
uv pip install wandb mlflow # specific loggers as needed
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 DataLoaderval_dataloader() - Return validation DataLoadertest_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 modelsEarlyStopping - Stop when metrics plateauLearningRateMonitor - Track LR scheduler changesBatchSizeFinder - 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)
Comet (CometLogger)
CSV (CSVLogger)
Note: NeptuneLogger was removed in lightning 2.6.4. Use W&B, MLflow, or TensorBoard instead.
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 boilerplatetemplate_datamodule.py - Complete LightningDataModule boilerplatequick_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 parametersdata_module.md - LightningDataModule patterns and methodscallbacks.md - Built-in and custom callbackslogging.md - Logger integrations and usagedistributed_training.md - DDP, FSDP, DeepSpeed comparison and setupbest_practices.md - Common patterns, tips, and pitfalls 