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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Changming Sun cc0c5e5612
Fix an error in test/shared_lib/test_inference.cc (#16090) 
### Description
Fix an error in test/shared_lib/test_inference.cc. It should use
ASSERT_NEAR to test float values.

### Motivation and Context
Our OpenVino pipeline is failing because of this.
2023-05-24 22:59:28 -07:00
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cmake [DML EP] Fix issue with --dml_path build option (#15972) 2023-05-24 19:20:40 -05:00
csharp [Bug Fix] Incorrect comparison for FromBuffer in TrainingSession.cs (#16022) 2023-05-22 21:21:54 -07:00
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docs [DML EP] Register pad18 (#15985) 2023-05-23 18:25:36 -07:00
include/onnxruntime/core [QNN EP] Add session option to disable fallback to default CPU EP (#16016) 2023-05-23 17:56:32 -07:00
java
js [js/webgpu] generate operator table for webgpu (#15954) 2023-05-20 12:20:41 -07:00
objectivec Add iOS Swift Package Manager support (#15297) 2023-04-20 16:18:35 +10:00
onnxruntime Fix an error in test/shared_lib/test_inference.cc (#16090) 2023-05-24 22:59:28 -07:00
orttraining Type hint for ORTModule (#15938) 2023-05-25 09:28:20 +08:00
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swift/OnnxRuntimeBindingsTests
tools [Fix] Some pipelines have to be using VS2019 (#16034) 2023-05-25 09:55:35 +08:00
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setup.py Fix python pipeline for AzureEP without using root (#16023) 2023-05-22 16:38:47 -07:00
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README.md

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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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.