PyTorch Lightning is an open-source deep learning library released by PyTorch that makes it easier to develop and optimize deep learning models. It is designed to reduce the complexity of deep learning code and automate away the “boilerplate”. The library provides a way of structuring complex deep learning projects that helps researchers and developers set up experiments, debug code, and build reusable components that have a better chance of working across multiple projects.
PyTorch Lightning consists of lightweight modules which can be used independently or combined together as layers, making it easier to share, debug, and reuse code. It provides integration with popular debugging tools including TensorBoard, Visdom, and Weights & Biases, allows for easy save and resume points, and makes it simple to scale experiments across multiple GPUs.
PyTorch Lightning provides both high-level abstractions and an environment where users can customize pre-existing components from the community. It also exposes the underlying architecture so that customizations can be made and experiments further optimized.
PyTorch Lightning is being used in a variety of areas including natural language processing, computer vision, and reinforcement learning. It is quickly becoming a popular tool for both academics and researchers.
