ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Pranav Sharma b935524e22
Revert reverse setup of allocators + create/register allocator in CPU EP only when needed. (#12954)
* Revert reverse setup of allocators + create/register allocator in CPU EP only when needed.
2022-09-15 17:54:32 -07:00
.config A new pipeline to replace the existing WindowsAI packaging pipeline (#10646) 2022-03-03 08:56:49 -08:00
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.gdn Update compliance tasks in python packaging pipeline and fix some compile warnings (#8471) 2021-07-30 17:16:37 -07:00
.github [Issue labeler] Separate out C# api as separate label (#12951) 2022-09-15 17:36:57 -07:00
.pipelines Publish WinML Nuget package to ORT-Nightly ADO feed (#12904) 2022-09-15 12:10:27 -07:00
.vscode cpplint & Eager mode: refactor and add comments to empty_* functions, general lint cleanup in ort_aten (#12238) 2022-07-20 11:47:57 -04:00
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onnxruntime Revert reverse setup of allocators + create/register allocator in CPU EP only when needed. (#12954) 2022-09-15 17:54:32 -07:00
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requirements-doc.txt Add auto doc gen for ORTModule API during CI build (#7046) 2021-03-22 10:20:33 -07:00
requirements-training.txt pin protobuf version to be compatible with onnx (#12132) 2022-07-08 15:01:27 -07:00
requirements.txt.in Add additional python requirements (#11522) 2022-05-20 16:16:18 -07:00
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VERSION_NUMBER Bump ort version number (#11948) 2022-07-22 12:55:53 -07:00

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

General Information: onnxruntime.ai

Usage documention and tutorials: onnxruntime.ai/docs

Companion sample repositories:

Build Pipeline Status

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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.