53c771f215
### Description "onnxruntime-common" starts to get more and more complicated, so it's a good idea to add unit tests for it. Includes the following changes: - move `mocha` from each subfolder (js/web/, js/node/) to root (js/), so that it will be installed once and all subfolder can use. - add folder `test` in js/common/ as root folder for ort-common tests. - add sub folder `type-tests`. this folder contains a few typescript source code, which are excluded from the tsconfig.json. they are not compiled by default. instead, file `type-tests.ts` calls typescript compiler (tsc) to check for the files under this folder whether the compilation result is as expected. If tsc compiles a file successfully when a failure is expected, this is considered an failed test. - add sub folder `unit-tests`. files under this folder will be compiled by default. we use default mode of mocha (using `describe()` and `it()`) to setup test groups and cases. - update eslint rules accordingly. |
||
|---|---|---|
| .. | ||
| docs | ||
| lib | ||
| script | ||
| test | ||
| .gitignore | ||
| .npmignore | ||
| karma.conf.js | ||
| package-lock.json | ||
| package.json | ||
| README.md | ||
| tsconfig.json | ||
| types.d.ts | ||
| webpack.config.js | ||
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, webgl | wasm |
| Ubuntu LTS 18.04 | wasm, webgl | wasm, webgl | - | wasm, webgl | wasm |
| iOS | wasm, webgl | wasm, webgl | wasm, webgl | - | - |
| Android | wasm, webgl | wasm, webgl | - | - | - |
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.
License
License information can be found here.