pytorch-lightning

Organize PyTorch code for scalable distributed training by structuring models into LightningModules and using LightningDataModules to separate research logic from engineering boilerplate. This enables clean, reproducible workflows across multi-GPU and TPU environments.

Reduce PyTorch training boilerplate by adopting LightningModule and Trainer templates that automate standard training loops, logging, and callbacks. This approach removes repetitive setup code while maintaining reproducible experimentation.

PyTorch Lightning supports multi-GPU and TPU distributed training pipelines using DDP, FSDP, and DeepSpeed. The Trainer component manages these configurations natively, enabling scalable deep learning without rewriting core model logic.

Integrate Weights and Biases logging into a Lightning training pipeline using built-in callback and logging templates. The Skill provides references for connecting wandb to the Trainer, streamlining experiment tracking and metric visualization.

Use LightningDataModule to structure PyTorch data pipelines by encapsulating data loading, transformation, and splitting logic into a reusable class. This template ensures consistent data handling across different training runs and distributed environments.

Avoid PyTorch Lightning for model training if your project requires highly customized training loops that conflict with the Trainer abstraction, or if the dependency overhead of lightning and torch exceeds the boilerplate reduction benefits.