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pytorch-lightning skill

by K-Dense-AI·K-Dense-AI/scientific-agent-skills·47k stars·MIT

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.

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Install the pytorch-lightning skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/scientific-agent-skills
mkdir -p ~/.claude/skills
cp -r /tmp/scientific-agent-skills/skills/pytorch-lightning ~/.claude/skills/pytorch-lightning
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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:

  1. Initialization - init() and setup()
  2. Training Loop - trainingstep(batch, batchidx)
  3. Validation Loop - validationstep(batch, batchidx)
  4. Test Loop - teststep(batch, batchidx)
  5. Prediction - predictstep(batch, batchidx)
  6. Optimizer Configuration - configure_optimizers()

Quick template reference: See scripts/templatelightningmodule.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/quicktrainersetup.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.savehyperparameters() in init__()
  • Metric logging - Use self.log() for automatic aggregation across devices
  • Reproducibility - Use seed_everything() and Trainer(deterministic=True)
  • Debugging - Use Trainer(fastdevrun=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())
  1. 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)
  1. 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:

  • templatelightningmodule.py - Complete LightningModule boilerplate
  • template_datamodule.py - Complete LightningDataModule boilerplate
  • quicktrainersetup.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

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