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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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[//]: # (dependabot-start) ⚠️ **Dependabot is rebasing this PR** ⚠️ Rebasing might not happen immediately, so don't worry if this takes some time. Note: if you make any changes to this PR yourself, they will take precedence over the rebase. --- [//]: # (dependabot-end) Bumps [Newtonsoft.Json](https://github.com/JamesNK/Newtonsoft.Json) from 13.0.1 to 13.0.2. <details> <summary>Release notes</summary> <p><em>Sourced from <a href="https://github.com/JamesNK/Newtonsoft.Json/releases">Newtonsoft.Json's releases</a>.</em></p> <blockquote> <h2>13.0.2</h2> <ul> <li>New feature - Add support for DateOnly and TimeOnly</li> <li>New feature - Add UnixDateTimeConverter.AllowPreEpoch property</li> <li>New feature - Add copy constructor to JsonSerializerSettings</li> <li>New feature - Add JsonCloneSettings with property to disable copying annotations</li> <li>Change - Add nullable annotation to JToken.ToObject(Type, JsonSerializer)</li> <li>Change - Reduced allocations by reusing boxed values</li> <li>Fix - Fixed MaxDepth when used with ToObject inside of a JsonConverter</li> <li>Fix - Fixed deserializing mismatched JToken types in properties</li> <li>Fix - Fixed merging enumerable content and validate content</li> <li>Fix - Fixed using $type with arrays of more than two dimensions</li> <li>Fix - Fixed rare race condition in name table when deserializing on device with ARM processors</li> <li>Fix - Fixed deserializing via constructor with ignored base type properties</li> <li>Fix - Fixed MaxDepth not being used with ISerializable deserialization</li> </ul> </blockquote> </details> <details> <summary>Commits</summary> <ul> <li><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 documention 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
Build Pipeline Status
| System | CPU | GPU | EPs |
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| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
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| WebAssembly |
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.