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* Update tools/ci_build/upload_python_package_to_azure_storage.py to not use the azure blob storage python package (#11114) * Fix the rocm packaging pipeline package upload problem (#11174) In #11114 , I changed the script to use azcopy instead of azure blob storage's python APIs. However, it doesn't work for the AMD rocm pipeline, because: 1. The machines do not have azcopy installed 2. The machines are not in Azure, so they don't have Azure managed identity. So they still need to use SAS. Therefore in this PR I get the old python file back, but only use it in the AMD pipeline. * Scoped GIL release in run_with_iobinding (#11248) * [js/web] disable test_tan temorarily (#11048) * [js/web] fix output type mapping (#11049) Co-authored-by: Changming Sun <chasun@microsoft.com> Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com> Co-authored-by: Yulong Wang <7679871+fs-eire@users.noreply.github.com> |
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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.