pytorch-lightning

PyTorch boilerplate is reduced by organizing code into LightningModule and LightningDataModule structures, separating research logic from engineering scaffolding while preserving full flexibility for model definition and training.

Distributed PyTorch training is automated using the Lightning Trainer, which orchestrates execution across single or multi-GPU setups and supports strategies like DDP, FSDP, and DeepSpeed without requiring manual device management.

Yes, you need to install PyTorch separately. The deep learning workflow requires both the PyTorch and Lightning libraries to define models, configure data modules, and execute the trainer.

LightningDataModule creates reusable data handling pipelines for reproducible deep learning. It standardizes data loading and processing into a shareable format, ensuring consistent data delivery across different training runs.

Yes, automated training orchestration includes built-in logging and checkpointing. The Lightning Trainer manages these features natively during the model fitting process, supporting dependencies like WandB for experiment tracking.

You should avoid Lightning if your project requires highly customized training loops that bypass standard orchestration, as the framework enforces a structured separation of research and engineering logic via its core modules.