05-03, 10:00–10:25 (Asia/Jerusalem), PyData Track 1
This talk introduces PyTorch Lightning, outline its core design philosophy, and provides inline examples of how this philosophy enables more reproducible and production-capable deep learning code.
PyTorch Lightning reduces the engineering boilerplate and resources required to implement state-of-the-art AI. Organizing PyTorch code with Lightning, enables seamless training on multiple-GPUs, TPUs, CPUs as well as the use of difficult to implement best practices such as model sharding, 16-bit precision and more, without any code changes. This talk introduces PyTorch Lightning, outline its core design philosophy, and provides inline examples of how this philosophy enables more reproducible and production-capable deep learning code based on work the following post https://opendatascience.com/pytorch-lightning-from-research-to-production-minus-the-boilerplate/
English
Target audience –Developers, Data Scientists, R&D
Aaron (Ari) Bornstein is an AI researcher with a passion for history, engaging with new technologies and computational medicine. As Head of Developer Advocacy at Grid.ai, he collaborates with the Machine Learning Community to solve real-world problems with game-changing technologies that are then documented, open-sourced, and shared with the rest of the world.