ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Jian Chen 1a73d61829
Update eigen to 3.4 and remove the eigen from git submodule (#15875)
### Description
Update eigen to 3.4 and remove the eigen from git submodule

### Motivation and Context
We need to have eigen 3.4 for c++20
2023-05-11 11:56:59 -07:00
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cgmanifests update onnx release 1.14 for docker files (#15680) 2023-05-10 13:15:56 -07:00
cmake Update eigen to 3.4 and remove the eigen from git submodule (#15875) 2023-05-11 11:56:59 -07:00
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include/onnxruntime/core Support WebNN EP (#15698) 2023-05-08 21:25:10 -07:00
java Creating Nuget and Android packages for Training (#15712) 2023-05-01 12:59:56 -07:00
js update onnx release 1.14 for docker files (#15680) 2023-05-10 13:15:56 -07:00
objectivec Add iOS Swift Package Manager support (#15297) 2023-04-20 16:18:35 +10:00
onnxruntime Ryanunderhill/beamsearch simplify (#15883) 2023-05-11 09:50:14 -07:00
orttraining Refine a bit (on device training) (#15803) 2023-05-10 20:36:13 -07:00
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swift/OnnxRuntimeBindingsTests Add iOS Swift Package Manager support (#15297) 2023-04-20 16:18:35 +10:00
tools Update eigen to 3.4 and remove the eigen from git submodule (#15875) 2023-05-11 11:56:59 -07:00
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.gitignore remove 'lib/' from .gitignore (#15613) 2023-04-24 18:43:32 -07:00
.gitmodules Update eigen to 3.4 and remove the eigen from git submodule (#15875) 2023-05-11 11:56:59 -07:00
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Package.swift Add iOS Swift Package Manager support (#15297) 2023-04-20 16:18:35 +10:00
packages.config [DML EP] Update DirectML version to 1.11.0 (#15858) 2023-05-09 12:48:15 -07:00
pyproject.toml Bump ruff in CI (#15533) 2023-04-17 10:11:44 -07:00
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requirements-lintrunner.txt Enable RUFF as a formatter (#15699) 2023-04-26 14:04:07 -07:00
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setup.py Fix bug when adding Whisper to wheel (#15708) 2023-04-28 16:03:55 -07:00
ThirdPartyNotices.txt [js/web] WebGPU backend via JSEP (#14579) 2023-04-24 15:21:18 -07:00
VERSION_NUMBER Update VERSION_NUMBER (#15773) 2023-05-03 15:07:34 -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 & 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.