PyTorch Lightning

PyTorch Lightning is an open source deep learning library for large-scale machine learning and deep learning experiments and products created by William Falcon. The library is designed to address the hardships of scale associated with deep learning models, which typically have to be managed manually. The PyTorch Lightning library helps simplify their management.

PyTorch Lightning has three core principles, Computational efficiency, Experimental Flexibility, and Code Simplicity. more specifically, it allows developers to design and manage code sets in a declarative way, with a simple and efficient API, consistent performance, and scalable multi-GPU training. It also includes high-level abstractions such as optimizers, learning rates, checkpointing, callbacks, logging, and more to manage deep learning code sets.

The library is built on top of the PyTorch Library, allowing developers to take advantage of both PyTorch’s deep learning and PyTorch’s Lightning Deep Learning features. Additionally, developers have the ability to integrate add-on plug-ins from other libraries, such as scikit-learn, Catalyst, Hydra, and Ignite.

PyTorch Lightning is currently widely used by research teams around the world, and is their primary choice for training deep learning models. It is also used in the development of applications such as self-driving cars, healthcare, natural language processing, computer vision, internet-of-things, recommendation systems, and more.

PyTorch Lightning makes it easier for developers to build machine learning solutions using deep learning. Its features enable developers to quickly and efficiently design and manage code sets allowing for fast development and experimentation cycles. It is used mostly by research teams around the world to develop deep learning models, and is also increasingly used to develop applications across a wide variety of industries.