Pytorch Lightning

If you're training neural networks with PyTorch and tired of writing the same device management and training loop boilerplate, this organizes your code into LightningModules with clear sections for training, validation, and optimizer config. The Trainer handles multi-GPU orchestration automatically, so switching from single GPU to DDP or FSDP is just a parameter change. The LightningDataModule pattern is genuinely helpful for keeping data pipelines clean and reusable. Built-in callbacks for checkpointing and early stopping save you from reimplementing common patterns. The real win is going from prototype to distributed training without rewriting your model code, though you'll still need to understand the underlying PyTorch when things break.

skills.sh installs: 645
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill pytorch-lightning --agent claude-code

Installs into .claude/skills of the current project.

Files

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.

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