Lightning-AI/pytorch-lightning

AI 总结

• 项目定位为深度学习训练框架，强调“你写模型逻辑，Lightning 处理工程细节”。
• 核心包含 PyTorch Lightning 与 Lightning Fabric 两个包，分别覆盖高层训练与专家级控制。
• 支持多GPU、多节点、TPU、16-bit 精度、早停、检查点、实验管理等 40+ 高级能力。
• 提供丰富示例，覆盖分类、分割、检测、文本摘要、音频生成、LLM 微调、扩散模型等任务。
• 与 Lightning Cloud 和 LitServe 生态联动，可用于云端训练与 Python 推理服务部署。

内容摘录

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**The deep learning framework to pretrain and finetune AI models.**

**Serving models?** Use LitServe to build custom inference servers in pure Python.

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 <a href="#quick-start" style="margin: 0 10px;">Quick start</a> •
 <a href="#examples">Examples</a> •
 <a href="#why-pytorch-lightning">PyTorch Lightning</a> •
 <a href="#lightning-fabric-expert-control">Fabric</a> •
 <a href="https://lightning.ai/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme">Lightning Cloud</a> • 
 <a href="#community">Community</a> •
 <a href="https://pytorch-lightning.readthedocs.io/en/stable/">Docs</a>
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<a target="_blank" href="https://lightning.ai/docs/pytorch/latest/starter/introduction.html?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme#define-a-lightningmodule">
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<a id="why-pytorch-lightning"></a>
Why PyTorch Lightning? 
Training models in plain PyTorch requires writing and maintaining a lot of repetitive engineering code. Handling backpropagation, mixed precision, multi-GPU, and distributed training is error-prone and often reimplemented for every project. PyTorch Lightning organizes PyTorch code to automate this infrastructure while keeping full control over your model logic. You write the science. Lightning handles the engineering, and scales from CPU to multi-node GPUs without changing your core code. PyTorch experts can still opt into expert-level control. 

Fun analogy: If PyTorch is Javascript, PyTorch Lightning is ReactJS or NextJS.
Looking for GPUs?
Lightning Cloud is the easiest way to run PyTorch Lightning without managing infrastructure. Start training with one command and get GPUs, autoscaling, monitoring, and a free tier. No cloud setup required.

You can also run PyTorch Lightning on your own hardware or cloud.
Lightning has 2 core packages

PyTorch Lightning: Train and deploy PyTorch at scale.
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Lightning Fabric: Expert control.

Lightning gives you granular control over how much abstraction you want to add over PyTorch.

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Quick start
Install Lightning:

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Install with optional dependencies
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Install future release from the source
Install bleeding-edge

Install nightly from the source (no guarantees)

or from testing PyPI

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PyTorch Lightning example
Define the training workflow. Here's a toy example (explore real examples):

Run the model on your terminal

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Convert from PyTorch to PyTorch Lightning

PyTorch Lightning is just organized PyTorch - Lightning disentangles PyTorch code to decouple the science from the engineering.

!PT to PL

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Examples
Explore various types of training possible with PyTorch Lightning. Pretrain and finetune ANY kind of model to perform ANY task like classification, segmentation, summarization and more: 

| Task | Description | Run |
|------|--------------|-----|
| Hello world | Pretrain - Hello world example | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/pytorch-lightning-hello-world?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
| Image classification | Finetune - ResNet-34 model to classify images of cars | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/image-classification-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
| Image segmentation | Finetune - ResNet-50 model to segment images | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/image-segmentation-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
| Object detection | Finetune - Faster R-CNN model to detect objects | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/object-detection-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
| Text classification | Finetune - text classifier (BERT model) | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/text-classification-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme"><img src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/studio-badge.svg" alt="Open In Studio"/></a> |
| Text summarization | Finetune - text summarization (Hugging Face transformer model) | <a target="_blank" href="https://lightning.ai/lightning-ai/studios/text-summarization-with-pytorch-lightning?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme"><img src="https://pl-bolts-doc-images.s3.us-east-…