ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Guenther Schmuelling a756017e9f
[js/webgpu] more fixes for access above 2GB (#19065)
when jsep calls javascript with an index to HEAP8 or HEAP32 the index is
negative when the heap is above 2GB, even if we pass it as uint32_t it
remains negative. So in javascript use >>> 0 to make it unsigned.
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include/onnxruntime/core Make session configuration options available to kernels via OpKernelInfo (#18897) 2024-01-13 10:02:43 +10:00
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js [js/webgpu] more fixes for access above 2GB (#19065) 2024-01-12 17:47:37 -08:00
objectivec Objective-C API updates (#18738) 2023-12-07 16:47:46 -08:00
onnxruntime [js/webgpu] more fixes for access above 2GB (#19065) 2024-01-12 17:47:37 -08:00
orttraining Make session configuration options available to kernels via OpKernelInfo (#18897) 2024-01-13 10:02:43 +10:00
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ort.wprp ORT ETW dynamic logging that improves ORT diagnosability & performance (#18882) 2024-01-11 12:43:27 -08:00
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packages.config Update DML version to 1.13.0 (#18978) 2024-01-03 16:09:55 -08:00
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SECURITY.md
setup.py Adding python3.12 support to ORT (#18814) 2024-01-11 08:34:28 -08:00
ThirdPartyNotices.txt
VERSION_NUMBER

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

Builtin Pipeline Status

System Inference Training
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Third-party Pipeline Status

System Inference Training
Linux Build Status

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.