ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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Rachel Guo 04dbdc96bf
[js/rn] Fix React Native CI pipeline E2E test (#16447)
### Description
<!-- Describe your changes. -->

Based on this kindly provided quick fix:
https://github.com/microsoft/onnxruntime/pull/16411

See more description in the above linked pr about bumping AGP version,
etc.

Also fixed import header file path in detox e2e test.

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

Good build:

https://dev.azure.com/onnxruntime/onnxruntime/_build/results?buildId=1041757&view=logs&j=de302ec2-2305-57e0-e8c6-cd89c569f2a3&t=9894c870-b8ce-548d-51ff-8f44d21a4117&l=18
2023-06-22 14:33:49 -07:00
.config
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.gdn Update win-ci-pipeline.yml: enable xnnpack tests (#16244) 2023-06-14 19:12:42 -07:00
.github Bump actions/setup-python from 3 to 4 (#16404) 2023-06-22 18:12:11 +00:00
.pipelines
.vscode
cgmanifests Clean AzureEP logics (#16367) 2023-06-21 09:38:52 -07:00
cmake Minimal Build for On-Device Training (#16326) 2023-06-22 12:27:23 -07:00
csharp Enable Microsoft.AI.MachineLearning NuGet with WinUI projects (#16415) 2023-06-20 13:10:19 -07:00
dockerfiles Enable model subgraph execution in OVEP and setting the OpenVINO dll's to the path from the OpenVINO pypi packge in OVEP and fix OVEP windows io buffer sample (#16147) 2023-06-16 19:47:09 -07:00
docs Embedding sparsity optimization (#16141) 2023-06-19 20:34:53 +08:00
include/onnxruntime/core Allow saving of large models after optimization (github issue 12882) (#16440) 2023-06-21 22:46:26 -07:00
java Minimal Build for On-Device Training (#16326) 2023-06-22 12:27:23 -07:00
js [js/rn] Fix React Native CI pipeline E2E test (#16447) 2023-06-22 14:33:49 -07:00
objectivec Treat Objective-C static analysis warnings as errors (#16293) 2023-06-09 08:51:49 -07:00
onnxruntime Minimal Build for On-Device Training (#16326) 2023-06-22 12:27:23 -07:00
orttraining Minimal Build for On-Device Training (#16326) 2023-06-22 12:27:23 -07:00
rust
samples
swift/OnnxRuntimeBindingsTests
tools [js/rn] Fix React Native CI pipeline E2E test (#16447) 2023-06-22 14:33:49 -07:00
winml Use M_PI to replace 3.14 constants (#16421) 2023-06-20 15:09:10 -07:00
.clang-format
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.dockerignore
.gitattributes
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.gitmodules
.lintrunner.toml Minimal Build for On-Device Training (#16326) 2023-06-22 12:27:23 -07:00
build.amd64.1411.bat
build.bat
build.sh
CITATION.cff
CODEOWNERS
CONTRIBUTING.md
lgtm.yml
LICENSE
NuGet.config
ort.wprp
ORT_icon_for_light_bg.png
Package.swift
packages.config
pyproject.toml
README.md add third-party pipeline status to README.md (#16155) 2023-05-31 22:14:39 -07:00
requirements-dev.txt
requirements-doc.txt
requirements-lintrunner.txt
requirements-training.txt
requirements.txt.in
SECURITY.md
setup.py Clean AzureEP logics (#16367) 2023-06-21 09:38:52 -07:00
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
Windows Build Status
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Linux Build Status
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Android Build Status
iOS Build Status
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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.