pytorch-lightning

PyTorch training scales across multiple GPUs by using a Lightning Trainer to orchestrate models and training loops, automatically enabling distributed strategies like DDP, FSDP, and DeepSpeed with minimal code changes.

Converting a vanilla PyTorch model to Lightning involves defining a LightningModule, preparing your data, and running a Trainer to scale training from a laptop to a cluster with minimal boilerplate.

PyTorch Lightning supports mixed-precision workflows and TPU acceleration by providing device-agnostic code orchestration, enabling seamless scaling from single-GPU prototyping to production-scale experiments.

Callbacks and checkpointing in PyTorch Lightning are built-in features that track progress, log metrics, and save model states automatically during training, ensuring reproducible training loops without manual intervention.

DeepSpeed and FSDP strategies are automatically applied by configuring a single PyTorch Lightning Trainer, allowing you to orchestrate distributed training across multi-node setups without altering the core research code.

PyTorch Lightning is needed when you require deterministic training orchestration, clean separation of research and engineering code, and automatic distribution across GPUs or TPUs for reproducible, production-scale experiments.