# Pytorch Lightning

> Framework de deep learning (PyTorch Lightning): organiza código en LightningModules, configura Trainers multi-GPU/TPU, pipelines de datos, callbacks, logging (W&B, TensorBoard, MLflow) y entrenamiento distribuido (DDP, FSDP, DeepSpeed).

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/pytorch-lightning
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/pytorch-lightning.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: Apache-2.0 license
Actualizado: el mes pasado
Coste de contexto: 76 tok instalada, 1.9k tok al activarse, 35k tok con todos los archivos del bundle
Bundle: 11 archivos, 137 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pytorch-lightning --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pytorch-lightning --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pytorch-lightning --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pytorch-lightning --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pytorch-lightning --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill pytorch-lightning --agent cline
```

## Qué hace

- Organiza código PyTorch en LightningModules eliminando boilerplate sin perder flexibilidad
- Configura el Trainer para entrenamiento multi-GPU/TPU, precisión mixta, checkpointing y early stopping
- Estructura pipelines de datos reutilizables con LightningDataModule (prepare_data, setup, dataloaders)
- Integra logging con TensorBoard, W&B, MLflow o Comet, y añade callbacks personalizados en hooks de entrenamiento
- Guía la elección de estrategia distribuida (DDP, FSDP, DeepSpeed) según el tamaño del modelo

## Cuándo usarla

- El usuario construye, entrena o despliega redes neuronales con PyTorch Lightning
- Necesita organizar código PyTorch en LightningModules o LightningDataModules
- Configura Trainers para entrenamiento multi-GPU/TPU
- Trabaja con callbacks, logging o estrategias de entrenamiento distribuido (DDP, FSDP, DeepSpeed)

## Qué la activa

- "Convierte este modelo PyTorch en un LightningModule"
- "Configura el Trainer para entrenar en 4 GPUs con FSDP"
- "Añade early stopping y checkpointing a mi entrenamiento"
- "Crea un LightningDataModule para este dataset"

## Antes de instalar

- Requiere Python 3.10+ y lightning 2.6+ (uv pip install lightning); entrenamiento en GPU necesita PyTorch con CUDA, y loggers como wandb o mlflow se instalan aparte.

## Archivos

- SKILL.md — 7 KB
- references/best_practices.md — 16 KB
- references/callbacks.md — 15 KB
- references/data_module.md — 14 KB
- references/distributed_training.md — 14 KB
- references/lightning_module.md — 13 KB
- references/logging.md — 14 KB
- references/trainer.md — 14 KB
- scripts/quick_trainer_setup.py — 14 KB
- scripts/template_datamodule.py — 9 KB
- scripts/template_lightning_module.py — 6 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo Apache-2.0 license. Esta sección es el documento original y está en inglés.

# 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](https://lightning.ai/docs/pytorch/stable/). Use `import lightning as L` (the `pytorch-lightning` package name still installs the same library).

## Installation

```bash
uv pip install lightning
```

Optional extras:

```bash
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:

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)
- 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

1. **Define model:**
   ```python
   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:**
   ```python
   # 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:**
   ```python
   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

## Dónde encaja

- Categoría: [Datos y analítica](https://skillsagentes.com/categorias/datos-analitica.md) — Consulta, limpia y visualiza datos sin salir del agente.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

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