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### Description This change adds only necessary code to enable ort-web works with any Float16Array polyfill. Unlike #19302, in this PR, ort-web does not include any specific polyfill; instead, it's user's choice for how to use a polyfill. ORT-web uses Float16Array if it's available; otherwise, fallback to use Uint16Array. ```js // case 1: user does not use polyfill: import * as ort from 'onnxruntime-web'; const myF16Data = new Uint16Array(...); // need to use Uint16Array const myF16tensor = new ort.Tensor('float16', myF16Data, dims); ``` ```js // case 2: user use polyfill: import * as ort from 'onnxruntime-web'; import { Float16Array, isFloat16Array, isTypedArray, getFloat16, setFloat16, f16round, } from "@petamoriken/float16"; globalThis.Float16Array = Float16Array; // ort-web will pick the global Float16Array const myF16Data = new Float16Array(...); // Use the polyfilled Float16Array type const myF16tensor = new ort.Tensor('float16', myF16Data, dims); ``` |
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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.