pytorch-lightning

Convert PyTorch training code into Lightning workflows with distributed strategies.

Updated Mar 16, 2026

Search Tags:#callbacks #pytorch #distributed-training #lightning #fsdp #ddp #deepspeed

Ready to run

npx skills add https://github.com/arsity/scholar-tools --skill pytorch-lightning-arsity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pytorch-lightning
Source: https://github.com/arsity/scholar-tools/tree/main/vendor/ai-research-skills/08-distributed-training/pytorch-lightning
Command: npx skills add https://github.com/arsity/scholar-tools --skill pytorch-lightning-arsity

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lightning, torch, transformers, and includes references (resource) components.

What problem does it solve?

Clean PyTorch training code by abstracting boilerplate and enabling consistent, scalable training across hardware.

Core Features & Use Cases

Unified training loop with built-in distributed strategies (DDP, FSDP, DeepSpeed)
Deterministic training across CPUs/GPUs/TPUs with automatic precision and logging
Reproducible research workflows with LightningModule, Trainer, and callbacks

Quick Start

Install Lightning and replace your PyTorch training loop with a Lightning Trainer to run on single or multi-GPU setups.

Frequently Asked Questions about pytorch-lightning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce boilerplate in my PyTorch training code?▼

To reduce PyTorch boilerplate, map your scripts into a high-level Lightning workflow using LightningModule and Trainer. This structure abstracts the training loop, ensuring consistent, scalable execution across single and multi-GPU setups.

What is the best way to run distributed training across multiple GPUs with PyTorch?▼

The best way to run distributed training is using Lightning's unified Trainer with built-in strategies like DDP, FSDP, and DeepSpeed. This enables effortless multi-GPU scaling and automatic precision management without writing complex distributed logic.

Does Lightning support deterministic training across CPUs, GPUs, and TPUs?▼

Yes, Lightning supports deterministic training across CPUs, GPUs, and TPUs. By defining a LightningModule and utilizing the Trainer, you achieve reproducible research workflows with automatic precision handling and consistent logging across different hardware environments.

Can I use custom callbacks and loggers with the Lightning Trainer?▼

Yes, you can use custom callbacks and loggers with the Lightning Trainer. The Lightning workflow explicitly supports optional callbacks and loggers alongside the core LightningModule, allowing you to monitor and modify training behavior dynamically.

Do I need to install transformers to use the Lightning workflow?▼

Yes, installing transformers is required alongside lightning and torch. These three dependencies form the core environment needed to map your PyTorch training code into the high-level Lightning workflow for distributed and deterministic execution.

How does FSDP integration work in Lightning compared to standard PyTorch?▼

FSDP integration in Lightning works through the unified Trainer API, abstracting the complex boilerplate required in standard PyTorch. You simply select the FSDP distributed strategy to achieve fully sharded data parallel training with effortless reproducibility.