2021-05-03, 10:00–10:25, 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/