pytorch-lightning

Organize PyTorch code for scalable distributed training by structuring models into LightningModule, data into LightningDataModule, and orchestrating the workflow with Trainer to enable multi-GPU strategies like DDP and FSDP.

Reducing PyTorch boilerplate involves encapsulating training, validation, and testing steps inside a LightningModule, which enforces clean engineering practices while preserving research flexibility for your deep learning workflows.

Yes, PyTorch Lightning supports mixed precision and multi-GPU training. It provides built-in Trainer utilities to leverage distributed strategies like DDP, FSDP, and DeepSpeed across CPUs and GPUs for production-grade experiments.

Standardize data loading and preprocessing pipelines in PyTorch by encapsulating dataset handling and transforms inside a LightningDataModule, which standardizes data loading and preprocessing for consistent training workflows.

Yes, you can use DeepSpeed with PyTorch Lightning. The framework provides built-in support for distributed strategies like DDP, FSDP, and DeepSpeed to enable scalable training and multi-device deployment across GPUs.

Before building a PyTorch Lightning training pipeline, you need PyTorch and PyTorch Lightning installed. You must also prepare your datasets and define the model architecture to populate the required LightningModule and LightningDataModule templates.