Lightning-AI/pytorch-lightning

The deep learning framework to pretrain and finetune AI models. Serving models? Use LitServe to build custom inference servers in pure Python. Examples • PyTorch Lightning • Fabric • Lightning Cloud • Community • Docs [](https://pypi.org/project/pytorch-lightning/) [](https://badge.fury.io/py/pytorch-lightning) [](https://pepy.tech/project/pytorch-lightning) [](https://anaconda.org/conda-forge/lightning) [](https://codecov.io/gh/Lightning-AI/pytorch-lightning) [](https://discord.gg/VptPCZkGNa) [](https://github.com/Lightning-AI/pytorch-lightning/blob/master/LICENSE)     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. Lightning Cloud is the easiest way to run PyTorch Lightning without managing infrastructure. Start training with one command…