PyTorch Lightning
A PyTorch wrapper that automates engineering boilerplate for scalable AI model training and deployment.
What is PyTorch Lightning?
PyTorch Lightning is a framework that wraps PyTorch to automate the engineering boilerplate required for training and deploying AI models at scale. It handles infrastructure complexities like distributed training, mixed precision, and hardware management, allowing developers to focus solely on model architecture and research logic. The framework scales seamlessly from a single CPU to multi-node GPU clusters without requiring code changes.
AI researchers, machine learning engineers, and data scientists who use PyTorch and need to scale their model training, simplify experiment management, or reduce repetitive engineering code.
Developers choose PyTorch Lightning because it eliminates the need to manually write error-prone distributed training code, provides out-of-the-box support for advanced features like 16-bit precision and multiple accelerators, and maintains full PyTorch flexibility while dramatically reducing boilerplate.
Overview
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Use Cases
Best For
- Training large language models (LLMs) on multiple GPUs
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