pytorch-lightning

Reducing PyTorch boilerplate involves using high-level abstractions like LightningModule to organize code and eliminate repetitive training loops. This streamlines model development by separating engineering concerns from scientific logic.

Setting up distributed training across multiple GPUs is best achieved using a high-level API like the Trainer class to handle GPU switching automatically. This enables multi-GPU training with minimal code changes and supports mixed precision.

You need PyTorch and the lightning library installed to use PyTorch Lightning. The transformers library is listed as an additional dependency, indicating this Skill integrates with it for complex model development workflows.

Converting a PyTorch model to a LightningModule involves using a conversion guide to restructure the model code into high-level abstractions. This process streamlines the model for automatic handling of distributed training and hardware management.

Switching between GPUs and TPUs without altering core training code is possible using the Trainer class. It handles hardware switching and distributed training automatically, allowing you to target different accelerators with minimal modifications.