pytorch-lightning

PyTorch Lightning provides an organized structure for PyTorch code, abstracting away boilerplate code related to training loops, distributed training, logging, checkpointing, and hardware acceleration (GPUs/TPUs). It enforces best practices, promotes modularity, and reduces the amount of code needed to train complex deep learning models. Instead of manually writing all the training logic, users define modules that represent their model and data loading processes, while Lightning handles the rest. This allows researchers and developers to focus on the core aspects of their models and experiments rather than getting bogged down in infrastructure details. It supports various features like automatic mixed precision (AMP), early stopping, hyperparameter optimization, and distributed training across multiple GPUs or machines, making it suitable for both small-scale projects and large-scale research endeavors. Lightning’s modular design also facilitates reproducibility and collaboration.