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### Description This PR is an API-only change to address the requirements being discussed in #20729. There are multiple ways that users may create an ORT session by specifying the session options differently. All the code snippet below will use the variable `webnnOptions` as this: ```js const myWebnnSession = await ort.InferenceSession.create('./model.onnx', { executionProviders: [ webnnOptions ] }); ``` ### The old way (backward-compatibility) ```js // all-default, name only const webnnOptions_0 = 'webnn'; // all-default, properties omitted const webnnOptions_1 = { name: 'webnn' }; // partial const webnnOptions_2 = { name: 'webnn', deviceType: 'cpu' }; // full const webnnOptions_3 = { name: 'webnn', deviceType: 'gpu', numThreads: 1, powerPreference: 'high-performance' }; ``` ### The new way (specify with MLContext) ```js // options to create MLcontext const options = { deviceType: 'gpu', powerPreference: 'high-performance' }; const myMlContext = await navigator.ml.createContext(options); // options for session options const webnnOptions = { name: 'webnn', context: myMlContext, ...options }; ``` This should throw (because no deviceType is specified): ```js const myMlContext = await navigator.ml.createContext({ ... }); const webnnOptions = { name: 'webnn', context: myMlContext }; ``` ### Interop with WebGPU ```js // get WebGPU device const adaptor = await navigator.gpu.requestAdapter({ ... }); const device = await adaptor.requestDevice({ ... }); // set WebGPU adaptor and device ort.env.webgpu.adaptor = adaptor; ort.env.webgpu.device = device; const myMlContext = await navigator.ml.createContext(device); const webnnOptions = { name: 'webnn', context: myMlContext, gpuDevice: device }; ``` This should throw (because cannot specify both gpu device and MLContext option at the same time): ```js const webnnOptions = { name: 'webnn', context: myMlContext, gpuDevice: device, deviceType: 'gpu' }; ``` |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documentation and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.