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* Fix bug in Transpose CUDA kernel (#7329) * Fix permission error for ORTModule lock file (#7814) * fix topo sort in quant tool (#7833) * fix topo sort in quant tool * add unit test and make the topo sort stable * Relax tol for Conv1D fp16 test (#7844) * Relax tol for Conv1D fp16 test Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> * Resolve issue with wrapped ORTModule load_state_dict (#7847) * Encapsulate children modules inside a ModuleAccessor object to prevent erroneuos iteration over children while loading the state dictionary * Add named_models, models, apply methods, change ModuleAccessor to ModuleMetadata and modify unit tests * Change ModuleMetadata module getter logic, raise NotImplementedError for add_modules * Add comment explaining why overriding _load_from_state_dict method is needed * fixed bugs in packed mode and enable pack mode tests in ci (#7848) * fixed bugs in packed mode and enable pack mode tests in ci * removed unnecessary space * pr comments * pr comments * disable an average pool test * try disabling another avg pool * disable more avg pool tests * disable maxpool tests * add environment variable to control default training package's local version (#7849) * [js] update documents (#7852) * [js] update documents * escape double quotes * update operators.md * resolve comments * Support bool type for Pad CPU (#7856) * Initial commit * update * nit * Include ORT C/C++ API headers in the ORT Mobile AAR package (#7858) * Add header files of ort c/c++ api to aar package * Move header file selection to cmake based on EP choice * fix duplicated node name (#7865) * Clean up CPU kernel definition for opset 13 Pad (#7867) Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> Co-authored-by: Yufeng Li <liyufeng1987@gmail.com> Co-authored-by: Sherlock <baihan.huang@gmail.com> Co-authored-by: Sherlock Huang <bahuang@OrtTrainingDev3.af05slrtruoetgaxwwjv5nsq5e.px.internal.cloudapp.net> Co-authored-by: baijumeswani <bmeswani@microsoft.com> Co-authored-by: Tixxx <tix@microsoft.com> Co-authored-by: liqunfu <liqfu@microsoft.com> Co-authored-by: Yulong Wang <yulongw@microsoft.com> Co-authored-by: Guoyu Wang <62914304+gwang-msft@users.noreply.github.com> Co-authored-by: Tianlei Wu <tlwu@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 pre-trained ONNX models directly on browsers with various benefits of 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. For running on CPU, WebAssembly is adopted to execute the model at near-native speed. Furthermore, ONNX Runtime Web utilizes Web Workers to provide a "multi-threaded" environment to parallelize data processing. Empirical evaluation shows very promising performance gains on CPU by taking full advantage of WebAssembly and Web Workers. For running on GPUs, a popular standard for accessing GPU capabilities - WebGL is adopted. ONNX Runtime Web has further adopted several novel optimization techniques for reducing data transfer between CPU and GPU, as well as some techniques to reduce GPU processing cycles to further push the performance to the maximum.
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 | - | wasm | wasm | 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.