TL;DR: Use PyTorch Lightning with Ray to enable multi-node training and automatic cluster configuration with minimal code changes. PyTorch Lightning abstracts away engineering code, making deep learning experiments easier to reproduce and improving developer productivity. However, parallelizing training across multiple GPUs requires significant expertise and infrastructure setup. Ray Lightning simplifies this process by providing a simple plugin for PyTorch Lightning that can scale out training with minimal code changes, works with Jupyter Notebooks, seamlessly creates multi-node clusters on AWS/Azure/GCP, integrates with Ray Tune, and is fully open source and free to use. With Ray Lightning, scaling up PyTorch Lightning training becomes much easier and more flexible, allowing users to run their training jobs programmatically and automatically scale instances up and down as they train.