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### Description The patch release will fix the following issues: 1. A coding problem in test/shared_lib/test_inference.cc that it should use ASSERT_NEAR to test float values instead of ASSERT_EQ. Without this change, some DNNL/OpenVino tests would fail on some AMD CPUs. 2. A misaligned error in cublasGemmBatchedHelper function. The error only occurs when the GPU's CUDA Compute capability >=80. (In other words: with TensorFloat-32 support) 3. A build issue that build with onnxruntime_ENABLE_MEMORY_PROFILE was broken in 1.15.0 release. 4. Native onnxruntime library not loading in Azure App Service. It is because in 1.15.0 we introduced a Windows API call to SetThreadDescription. Though the API is available in all Windows 10 versions, some sandbox environments block using the API. 5. An alignment problem for xnnpack EP on Intel/AMD CPUs on PC platforms. 6. Some training header files were missing in the 1.15.0 training NuGet package. 7. Some fields in OrtCUDAProviderOptionsV2 struct are not initialized. --------- Co-authored-by: cao lei <jslhcl@gmail.com> Co-authored-by: Lei Cao <leca@microsoft.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Baiju Meswani <bmeswani@microsoft.com> Co-authored-by: JiCheng <wejoncy@163.com> Co-authored-by: Yuriy Chernyshov <thegeorg@yandex-team.com> Co-authored-by: Artur <artur@vaadin.com> Co-authored-by: Dale Phurrough <dale@hidale.com> Co-authored-by: Yi Zhang <zhanyi@microsoft.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.