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.

Reviewed release: Lightning 2.6.6, including its checkpoint-loading security fixes. CPU tests use Python 3.11, Torch 2.14.1 and Lightning 2.6.6. Use import lightning as L with the lightning distribution. The alternative pytorch-lightning distribution exposes import pytorch_lightning as pl; do not mix class namespaces within one application. GPU, TPU, multi-node and online logger examples are source-verified illustrations, not executed training validation.

Installation

uv pip install "lightning==2.6.6"

Optional extras:

uv pip install "lightning[extra]==2.6.6"    # 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:

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/cache on local rank zero per node by default; use global rank zero with shared storage when configured
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:

• CSV locally; TensorBoard is the default only when its optional package is installed
• Weights & Biases (WandbLogger)
• MLflow (MLFlowLogger)
• Comet (CometLogger)
• CSV (CSVLogger)

NeptuneLogger was removed in Lightning 2.6.4; the Neptune service shut down March 5, 2026. The bundled helpers use local CSV logging; cloud tracking and model artifact uploads require an intentional choice.

Log metrics with self.log("metric_name", value) in supported LightningModule hooks; specify epoch/step reduction and batch size when they matter.

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 - When each device can hold the full model, optimizer state and activations
• FSDP - When sharding training state addresses measured memory pressure
• DeepSpeed - When its ZeRO/offload features fit the measured workload and CUDA stack

Choose from peak memory and throughput measurements, not a fixed parameter-count threshold.

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 - For scalar tensors, cross-device reduction requires self.log(..., sync_dist=True); the default is False. Have all ranks participate in synchronized calls. For non-additive metrics such as AUROC, use a stateful TorchMetrics object with its own distributed synchronization instead of averaging per-rank AUROCs. See the Lightning logging contract and TorchMetrics integration.
• 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

Copy the three scripts into a working directory, then run this CPU smoke there:

import lightning as L
from template_datamodule import TemplateDataModule
from template_lightning_module import TemplateLightningModule
from quick_trainer_setup import production_single_gpu_trainer

L.seed_everything(42, workers=True)
model = TemplateLightningModule(hidden_dim=16)
dm = TemplateDataModule(num_samples=64, batch_size=8, num_workers=0)
trainer = production_single_gpu_trainer(
    max_epochs=1, accelerator="cpu", precision="32-true",
    log_dir="logs", checkpoint_dir="checkpoints",
)
trainer.fit(model, datamodule=dm)
trainer.test(model, datamodule=dm, ckpt_path="best", weights_only=True)

The synthetic vectors match the model input and use separate validation transforms and held-out test generation. Random labels test mechanics only. All helpers monitor val/loss. Use a fresh model/Trainer plus fit(..., ckpt_path=path, weights_only=True) to resume full training state; load_from_checkpoint alone loads a model. A normal short fit checks callbacks and checkpoints; fast_dev_run disables much of that behavior.

For real science, split independent subjects/groups/time periods before fitting preprocessing, preserve split IDs, and reserve test data from model selection. Final DDP evaluation can duplicate padded samples: use one device for exact sample coverage or validate a nonduplicating distributed evaluator.

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