pytorch-lightning

You can scale PyTorch training across multiple GPUs by converting your module into a LightningModule and using the high-level Trainer class. It orchestrates distributed training backends like DDP, FSDP, and DeepSpeed with minimal code changes.

The best way to reduce PyTorch training loop boilerplate is using a high-level interface that organizes code into portable training loops. Converting plain PyTorch modules into LightningModules separates research logic from engineering overhead, increasing reliability.

Yes, you can run distributed PyTorch training on a laptop and scale to multi-node clusters. The Trainer class handles orchestration across devices and configurations, enabling seamless scaling from local experimentation to multi-node clusters.

Yes, PyTorch Lightning supports automatic mixed precision and built-in callbacks. The Trainer class automates precision handling and provides logging integrations, allowing you to focus on model logic rather than training infrastructure.

To set up a PyTorch Lightning training run, install the Lightning package, convert your PyTorch model into a LightningModule, instantiate a Trainer, and call fit with your data. The Trainer handles the end-to-end training orchestration automatically.

You should choose FSDP or DeepSpeed based on your specific distributed training needs. The Trainer class supports both backends, allowing you to switch between them for multi-GPU and multi-node training with minimal code adjustments.