Over the years, I've used a lot of frameworks to build machine learning models. However, it was only until recently that I tried out PyTorch. After going through the intro tutorial, Deep Learning with PyTorch: A 60 Minute Blitz, I started to get the hang of it. With PyTorch support built into Google Cloud, including notebooks and pre-configured VM images, I was able to get started easily.

There was one thing that held me back. All of the wonderful flexibility also meant that there were so many ways to do things. How should I load my training and test data? How should I train my model, calculating the loss and logging along the way? I got everything working properly, but I kept wondering if my approach could be improved. I was hoping for a higher level of abstraction that would take care of how to do things, allowing me to focus on solving the problem.

I was delighted to discover PyTorch Lightning! Lightning is a lightweight PyTorch wrapper that helps you organize your code and provides utilities for common functions. With Lightning, you can produce standard PyTorch models easily on CPUs, GPUs, and TPUs! Let's take a closer look at how it works, and how to get started.

To introduce PyTorch Lightning, let's look at some sample code in this blog post from my notebook, Training and Prediction with PyTorch Lightning. The dataset used, from the UCI Machine Learning Repository, consists of measurements returned from underwater sonar signals to metal cylinders and rocks. The model aims to classify which item was found based on the returned signal. Acoustic data has a wide variety of applications, including medical imaging and seismic surveys, and machine learning can help detect patterns in this data.