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Cassie Breviu for Microsoft Azure

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Inference machine learning models in the browser with JavaScript and ONNX Runtime Web

In this video tutorial we will go over how to do client side inferencing in the browser with ONNX Runtime web. Below is a video on how to understand and use a QuickStart template to start building out a static web app with an open source computer vision model. Additionally, you can find a written step-by-step tutorial in the docs here. Let's learn a bit more about the library, ONNX Runtime (ORT), which allows us to inference in many different languages.

What is ORT and ORT-Web?

ONNX Runtime (ORT) is a library to optimize and accelerate machine learning inferencing. It has cross-platform support so you can train a model in Python and deploy with C#, Java, JavaScript, Python and more. Check out all the support platforms, architectures, and APIs here.

ONNX Runtime Web (ORT-Web) enables JavaScript developers to run and deploy machine learning models client-side. With ORT-Web you have the option to use a backend of either WebGL for GPU processing or WebAssembly WASM for CPU processing. If you want to do JavaScript server side inferencing with node checkout the onnxruntime-node library.

Video tutorial:

Written tutorial:
Check out the written tutorial here: ONNX Runtime Web Docs tutorial


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