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### Description The patch release will fix the following issues: 1. A coding problem in test/shared_lib/test_inference.cc that it should use ASSERT_NEAR to test float values instead of ASSERT_EQ. Without this change, some DNNL/OpenVino tests would fail on some AMD CPUs. 2. A misaligned error in cublasGemmBatchedHelper function. The error only occurs when the GPU's CUDA Compute capability >=80. (In other words: with TensorFloat-32 support) 3. A build issue that build with onnxruntime_ENABLE_MEMORY_PROFILE was broken in 1.15.0 release. 4. Native onnxruntime library not loading in Azure App Service. It is because in 1.15.0 we introduced a Windows API call to SetThreadDescription. Though the API is available in all Windows 10 versions, some sandbox environments block using the API. 5. An alignment problem for xnnpack EP on Intel/AMD CPUs on PC platforms. 6. Some training header files were missing in the 1.15.0 training NuGet package. 7. Some fields in OrtCUDAProviderOptionsV2 struct are not initialized. --------- Co-authored-by: cao lei <jslhcl@gmail.com> Co-authored-by: Lei Cao <leca@microsoft.com> Co-authored-by: Scott McKay <skottmckay@gmail.com> Co-authored-by: Baiju Meswani <bmeswani@microsoft.com> Co-authored-by: JiCheng <wejoncy@163.com> Co-authored-by: Yuriy Chernyshov <thegeorg@yandex-team.com> Co-authored-by: Artur <artur@vaadin.com> Co-authored-by: Dale Phurrough <dale@hidale.com> Co-authored-by: Yi Zhang <zhanyi@microsoft.com> |
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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 | Inference | Training |
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| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.