onnxruntime/js/web
shalvamist 851b0ce936
[js/web][Fix] - updating the C API to catch non-tensor data (#12811)
Added a check for tensor validation on the input - this change fixes the
quiet abort WASM takes when processing a non tensor data in
"OrtGetTensorData"

**Motivation and Context**
At the current status when we try to process non-tensor data through
OrtGetTensorData and exception is thrown which results in a quiet abort
from WASM (assuming WASM was built without exception handling).

I added a check in the C API to catch this case and output a meaningful
message to the user

[example_error_github_12622.zip](https://github.com/microsoft/onnxruntime/files/9464328/example_error_github_12622.zip)
2022-09-21 13:59:17 -07:00
..
docs replace 'master' branch ref to 'main' for onnx repo (#12678) 2022-08-30 13:41:42 -07:00
lib [js/web][Fix] - updating the C API to catch non-tensor data (#12811) 2022-09-21 13:59:17 -07:00
script replace 'master' branch ref to 'main' for onnx repo (#12678) 2022-08-30 13:41:42 -07:00
test [js] upgrade minimist@1.2.6 (#12689) 2022-08-25 01:40:42 -07:00
.gitignore prepare test folder from GitHub (#12220) 2022-07-20 22:01:08 -07:00
.npmignore [js/web] optimize bundle file size (#9817) 2021-11-22 13:56:55 -08:00
karma.conf.js [js/web] use windowed Chrome for perf mode (#12157) 2022-07-18 14:04:27 -07:00
package-lock.json [js] upgrade minimist@1.2.6 (#12689) 2022-08-25 01:40:42 -07:00
package.json [js] upgrade minimist@1.2.6 (#12689) 2022-08-25 01:40:42 -07:00
README.md replace 'master' branch ref to 'main' for onnx repo (#12678) 2022-08-30 13:41:42 -07:00
tsconfig.json [js/web] optimize bundle file size (#9817) 2021-11-22 13:56:55 -08:00
webpack.config.js [js/web] do not use nodejs type 'Buffer' in web (#9839) 2021-11-24 14:14:42 -08:00

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

License

License information can be found here.