/pytorch-lightning

name: pytorch-lightning
description: 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.
allowed-tools: Read Write Edit Bash
license: Apache-2.0 license
compatibility: Requires Python 3.10+ with lightning 2.6.6 and compatible PyTorch. CPU examples need no network after installation. CUDA/DDP/FSDP/DeepSpeed need suitable hardware; optional loggers require separate packages and online services need credentials/network.
metadata:
  version: "1.4"
  last-reviewed: "2026-10-01"
  skill-author: K-Dense Inc.

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](https://github.com/Lightning-AI/pytorch-lightning/releases/tag/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:** Rea