onnxruntime/js/web
Ashwini Khade 96eb9810ba
Update onnx (#8458)
* updates for picking pnnx commit

* add tests filter to c# tests

* plus test fixes

* fix versioning for contrib ops

* fix tests

* test filter for optional ops

* more versioning related updates

* fix test

* fix layernorm spec

* more updates

* update docs

* add more test filters

* more filters

* update binary size threshold

* update docs

* plus more fixes

* updates per review

* update to release commit

* add filters for optional type tests

* plus updates
2021-08-05 09:21:44 -07:00
..
docs Update onnx (#8458) 2021-08-05 09:21:44 -07:00
lib [js/web] adding webgl pointwise conv kernel (#8418) 2021-08-04 20:46:08 -07:00
script revise terms according to guideline 2021-07-23 13:26:15 -07:00
test [js/web] adding webgl pointwise conv kernel (#8418) 2021-08-04 20:46:08 -07:00
.gitignore [js/web] fix bundle for multi-thread, add e2e test and support nodejs (#7688) 2021-05-14 18:15:38 -07:00
.npmignore [js/web] fix pacakge metadata of onnxruntime-web (#7543) 2021-05-02 13:26:07 -07:00
karma.conf.js [js/web] Add wasm SIMD backend to onnxruntime-web (#7896) 2021-06-07 23:24:27 -07:00
package-lock.json bumping onnxruntime version to 1.8.1 (#8429) 2021-07-19 16:48:56 -07:00
package.json bumping onnxruntime version to 1.8.1 (#8429) 2021-07-19 16:48:56 -07:00
README.md [JS/Web]Adding support for WebGL v1 (#7906) 2021-06-03 21:30:42 -07:00
tsconfig.json
webpack.config.js [js/web] fix webpack config for onnxruntime-web (#7785) 2021-05-21 19:18:22 -07:00

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 complies 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

Refer to ONNX Runtime JavaScript examples for samples and tutorials.

Documents

Developement

Refer to the following links for development information:

Compatibility

OS/Browser Chrome Edge Safari Electron Node.js
Windows 10 wasm, webgl wasm, webgl - wasm, webgl wasm
macOS wasm, webgl - wasm, webgl wasm, webgl wasm
Ubuntu LTS 18.04 wasm - - wasm wasm
iOS wasm wasm wasm - -
Android wasm - - - -

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 operators.md for a complete, detailed list of which ONNX operators are supported by WebGL backend.

License

License information can be found here.