pytorch-lightning

Enables seamless scaling from CPU to multi-node GPUs or TPUs without code changes, as shown in examples for training on 256 GPUs with a single line adjustment in the Trainer.

Eliminates repetitive code for backpropagation, mixed precision, and distributed training, reducing errors and saving development time while maintaining PyTorch flexibility.

Provides expert-level control over training loops for complex models like LLMs and diffusion models, allowing custom strategies without sacrificing hardware abstraction.

Includes built-in support for exporting to TorchScript and ONNX formats, with code snippets in the README for easy model deployment in production environments.

Offers dozens of integrations with tools like TensorBoard, WandB, and MLFlow, plus advanced distributed strategies such as FSDP and DeepSpeed for scalable training.