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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
18f76bd25d
Bumps [gradle/wrapper-validation-action](https://github.com/gradle/wrapper-validation-action) from 1 to 2. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/gradle/wrapper-validation-action/releases">gradle/wrapper-validation-action's releases</a>.</em></p> <blockquote> <h2>v2.0.0</h2> <h2>What's Changed</h2> <p>The version of the Node.js runtime was updated to 20, and the majority of dependencies were updated to the latest versions. From now on, the <code>wrapper-validation-action</code> will require a Node.js 20 runtime environment.</p> <p>There are no functional changes in this release. This release is tagged with the <code>v2</code> version label.</p> <ul> <li>[NEW] Update Node.js runtime to version 20 (<a href="https://redirect.github.com/gradle/wrapper-validation-action/issues/170">#170</a>)</li> </ul> <h2>v2.0.0-rc.1</h2> <p>This is a release candidate for <code>v2.0.0</code>. It is also available under the <code>v2</code> version label.</p> <h2>What's Changed</h2> <p>The version of the Node.js runtime was updated to 20, and the majority of dependencies were updated to the latest versions. From now on, the <code>wrapper-validation-action</code> will require a Node.js 20 runtime environment.</p> <p>There are no functional changes in this release.</p> <ul> <li>[NEW] Update Node.js runtime to version 20 (<a href="https://redirect.github.com/gradle/wrapper-validation-action/issues/170">#170</a>)</li> </ul> <h2>v1.1.0</h2> <p>The action now adds the path of the failed wrapper Jar as a <code>failed-wrapper</code> Step output parameter. This makes the value available for reporting in later Steps/Jobs.</p> <h2>v1.0.6</h2> <h1>Gradle Wrapper Validation</h1> <ul> <li>Security vulnerability: <a href=" |
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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.