onnxruntime-web

What is onnxruntime-web?

onnxruntime-web is a JavaScript library that allows you to run ONNX (Open Neural Network Exchange) models directly in the browser or in Node.js environments. It leverages WebAssembly (WASM) and WebGL for efficient execution of machine learning models, making it possible to perform inference tasks on the client side without needing a server.

What are onnxruntime-web's main functionalities?

Load and Run ONNX Model

This feature allows you to load an ONNX model and run inference on it. The code sample demonstrates how to create an inference session, prepare input data, and run the model to get the results.

const ort = require('onnxruntime-web');

async function runModel() {
  const session = await ort.InferenceSession.create('model.onnx');
  const input = new ort.Tensor('float32', new Float32Array([1.0, 2.0, 3.0, 4.0]), [1, 4]);
  const feeds = { input: input };
  const results = await session.run(feeds);
  console.log(results);
}

runModel();

Use WebGL Backend

This feature allows you to leverage the WebGL backend for running ONNX models, which can provide better performance for certain types of models. The code sample shows how to specify the WebGL execution provider when creating the inference session.

const ort = require('onnxruntime-web');

async function runModelWithWebGL() {
  const session = await ort.InferenceSession.create('model.onnx', { executionProviders: ['webgl'] });
  const input = new ort.Tensor('float32', new Float32Array([1.0, 2.0, 3.0, 4.0]), [1, 4]);
  const feeds = { input: input };
  const results = await session.run(feeds);
  console.log(results);
}

runModelWithWebGL();

Run Model in Node.js

This feature demonstrates how to run ONNX models in a Node.js environment. The code sample is similar to the browser example but is intended to be run in a Node.js context.

const ort = require('onnxruntime-web');

async function runModelInNode() {
  const session = await ort.InferenceSession.create('model.onnx');
  const input = new ort.Tensor('float32', new Float32Array([1.0, 2.0, 3.0, 4.0]), [1, 4]);
  const feeds = { input: input };
  const results = await session.run(feeds);
  console.log(results);
}

runModelInNode();

Other packages similar to onnxruntime-web

brain.js

Brain.js is a JavaScript library for neural networks, which can be used in the browser or with Node.js. It is simpler and more lightweight compared to onnxruntime-web, and is suitable for basic neural network tasks. However, it does not support the ONNX model format.

synaptic

Synaptic is a JavaScript neural network library for node.js and the browser. It provides a flexible and powerful API for creating and training neural networks. Unlike onnxruntime-web, Synaptic does not support running pre-trained ONNX models and is more focused on building and training models from scratch.

README

ONNX Runtime Web

ONNX Runtime Web is a Javascript library for running ONNX models on browsers and on Node.js.

ONNX Runtime Web has adopted WebAssembly and WebGL technologies for providing an optimized ONNX model inference runtime for both CPUs and GPUs.

Why ONNX models

The Open Neural Network Exchange (ONNX) is an open standard for representing machine learning models. The biggest advantage of ONNX is that it allows interoperability across different open source AI frameworks, which itself offers more flexibility for AI frameworks adoption.

Why ONNX Runtime Web

With ONNX Runtime Web, web developers can score models directly on browsers with various benefits including reducing server-client communication and protecting user privacy, as well as offering install-free and cross-platform in-browser ML experience.

ONNX Runtime Web can run on both CPU and GPU. On CPU side, WebAssembly is adopted to execute the model at near-native speed. ONNX Runtime Web compiles the native ONNX Runtime CPU engine into WebAssembly backend by using Emscripten, so it supports most functionalities native ONNX Runtime offers, including full ONNX operator coverage, multi-threading, ONNX Runtime Quantization as well as ONNX Runtime Mobile. For performance acceleration with GPUs, ONNX Runtime Web leverages WebGL, a popular standard for accessing GPU capabilities. We are keeping improving op coverage and optimizing performance in WebGL backend.

See Compatibility and Operators Supported for a list of platforms and operators ONNX Runtime Web currently supports.

Usage

• See Get started as a landing page for ONNX Runtime Web documentation.

• Refer to ONNX Runtime JavaScript examples for samples and tutorials.

• See also ONNX Runtime Web API reference for detailed API documentation.

Documents

Development

Refer to the following links for development information:

• Development
• Build
• Test
• Debugging
• Generating Document

Compatibility

EPs/Browsers	Chrome/Edge (Windows)	Chrome/Edge (Android)	Chrome/Edge (MacOS)	Chrome/Edge (iOS)	Safari (MacOS)	Safari (iOS)	Firefox (Windows)	Node.js
WebAssembly (CPU)	✔️	✔️	✔️	✔️	✔️	✔️	✔️	✔️[1]
WebGPU	✔️[2]	✔️[3]	✔️	❌	❌	❌	❌	❌
WebGL	✔️[4]	✔️[4]	✔️[4]	✔️[4]	✔️[4]	✔️[4]	✔️[4]	❌
WebNN	✔️[5]	❌	❌	❌	❌	❌	❌	❌

• [1]: Node.js only support single-threaded wasm EP.
• [2]: WebGPU requires Chromium v113 or later on Windows. Float16 support requires Chrome v121 or later, and Edge v122 or later.
• [3]: WebGPU requires Chromium v121 or later on Windows.
• [4]: WebGL support is in maintenance mode. It is recommended to use WebGPU for better performance.
• [5]: Requires to launch browser with commandline flag --enable-features=WebMachineLearningNeuralNetwork.

Operators

WebAssembly backend

ONNX Runtime Web currently support all operators in ai.onnx and ai.onnx.ml.

WebGL backend

ONNX Runtime Web currently supports a subset of operators in ai.onnx operator set. See webgl-operators.md for a complete, detailed list of which ONNX operators are supported by WebGL backend.

WebGPU backend

WebGPU backend is still an experimental feature. See webgpu-operators.md for a detailed list of which ONNX operators are supported by WebGPU backend.

WebNN backend

WebNN backend is still an experimental feature. See webnn-operators.md for a detailed list of which ONNX operators are supported by WebNN backend.

License

License information can be found here.