In this story, we will show how we can create an interactive gradio demo for Lightning Flash Image Classification in just 5 lines of code. We will then show how to host the demo on Grid GPU compute, enabling distributed inferencing. All code for this demo can be found in the repo below. Lightning Flash is a PyTorch AI Factory built on top of PyTorch Lightning. Built for all experience levels, Flash helps you quickly develop strong baselines on your own data across multiple tasks.

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Just to recap from our last post on Getting Started with PyTorch Lightning, in this tutorial we will be diving deeper into two additional tools you should be using: TorchMetrics and Lightning Flash. TorchMetrics unsurprisingly provides a modular approach to define and track useful metrics across batches and devices, while Lightning Flash offers a suite of functionality facilitating more efficient transfer learning and data handling, and a recipe book of state-of-the-art approaches to typical deep learning problems. We'll start by adding a few useful classification metrics to the MNIST example we started with earlier. We'll also swap out the PyTorch Lightning Trainer object with a Flash Trainer object, which will make it easier to perform transfer learning on a new classification problem. First things first, and that's ensuring that we have all needed packages installed.

In this tutorial, we will package and deploy a simple model that exposes an HTTP API and serves predictions to a device managed by Synpse. Flash is a high-level deep learning framework for fast prototyping, baselining, finetuning and solving deep learning problems. It features a set of tasks for you to use for inference and finetuning out of the box, and an easy to implement API to customize every step of the process for full flexibility. Flash is built for beginners with a simple API that requires very little deep learning background, and for data scientists, Kagglers, applied ML practitioners and deep learning researchers that want a quick way to get a deep learning baseline with advanced features PyTorch Lightning offers. I decided to go with the image_classification as it was important to me to have some kind of service that could differentiate between ants and bees.

Flash is a collection of tasks for fast prototyping, baselining and fine-tuning scalable Deep Learning models, built on PyTorch Lightning. Whether you are new to deep learning, or an experienced researcher, Flash offers a seamless experience from baseline experiments to state-of-the-art research. It allows you to build models without being overwhelmed by all the details, and then seamlessly override and experiment with Lightning for full flexibility. Continue reading to learn how to use Flash tasks to get state-of-the-art results in a flash. Over the past year, PyTorch Lightning has received an enthusiastic response from the community for decoupling research from boilerplate code, enabling seamless distributed training, logging, and reproducibility of deep learning research code.