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Lightning-AI/pytorch-lightning

Streamlines complex deep learning engineering, enabling scalable AI model training and finetuning across diverse hardware with minimal code changes.

Core Features

Automates repetitive engineering code (e.g., backpropagation, mixed precision, distributed training).

Scales seamlessly from CPU to multi-node GPUs without altering core model logic.

Provides a structured way to organize PyTorch code, separating research from engineering.

Offers granular control over abstraction levels with PyTorch Lightning and Lightning Fabric.

Quick Start

pip install lightning

Detailed Introduction

PyTorch Lightning addresses the challenge of repetitive and error-prone engineering code in plain PyTorch, particularly for tasks like backpropagation, mixed precision, and distributed training. It provides a structured framework that automates this infrastructure, allowing researchers and engineers to focus on model logic ('the science'). The project ensures seamless scalability from single CPUs to multi-node GPUs without requiring changes to the core model code, making large-scale AI model development more efficient and accessible. It acts as an abstraction layer, akin to ReactJS for JavaScript, simplifying complex deep learning workflows.