pytorch-lightning

PyTorch Lightning simplifies distributed training by organizing model logic into a LightningModule and running it via the Trainer abstraction, which automatically handles device management and gradient synchronization.

Yes, PyTorch Lightning supports FSDP and DeepSpeed for large model sharding. The Trainer abstraction applies Lightning strategies to scale distributed training across these frameworks with minimal code changes.

To configure automatic checkpointing and mixed precision, use the Trainer abstraction within PyTorch Lightning. It provides built-in engineering essentials that manage these requirements automatically during the training loop.

Yes, you can use transformers with Lightning for distributed training. The Skill includes transformers as a dependency, allowing you to integrate transformer models into a LightningModule for multi-GPU scaling.

The best way to handle validation and logging in distributed PyTorch is using PyTorch Lightning. It moves core logic into validation_step and automates logging and progress reporting via the Trainer abstraction.

You need PyTorch Lightning for single-GPU training to remove the repetitive engineering burden of writing training loops. It keeps your code clean by automating device transfers, validation, and checkpointing.