pytorch-lightning

Scale PyTorch training by defining a LightningModule and Trainer to automate distributed strategies like DDP and FSDP. This separates research logic from engineering boilerplate, enabling clean training loops and multi-GPU scalability with minimal code changes.

Structure PyTorch projects using LightningModule to isolate research code and Trainer to manage automatic mixed precision and checkpointing. This organized structure streamlines scalable training across hardware and ensures experiments are reproducible.

Distributed training with PyTorch Lightning supports DeepSpeed, DDP, and FSDP strategies across single-GPU through multi-node clusters. It requires PyTorch, PyTorch Lightning, and optional dependencies for hardware acceleration and distributed training integration.

Convert existing PyTorch projects to Lightning to reduce boilerplate and streamline scalable training across hardware. Lightning automates end-to-end workflows, making it easier to reproduce experiments, iterate quickly, and run advanced training workflows with built-in logging.

Use PyTorch Lightning for multi-GPU and TPU training to implement advanced training workflows. It provides built-in logging and checkpointing, automating the training loop while supporting distributed strategies and automatic mixed precision for performance scalability.