02e3a430af
### Description
Enabling python binding and gcc support for AIX.
### Motivation and Context
Code changes in this PR contains:
1. python binding enablement
2. gcc building support
Below are list of files and the description.
1. cmake/CMakeLists.txt
[gcc building support] -no-unused-function compiler flag addition for
IBMClang
2. cmake/external/eigen.cmake
[gcc building support] AIX check for applying the AIX patch
3. cmake/onnxruntime_python.cmake
[python binding ] putting NOT AIX check for -Xlinker
4. cmake/onnxruntime_unittests.cmake
[gcc building support] Fix for gtest behavior. Check the comment .
[python binding ] using -Wl,-brtl for linking
onnxruntime_providers_shared in test_execution_provider
5. cmake/patches/eigen/eigen-aix.patch
[gcc building support] In AIX gcc, we are hitting
__builtin_cpu_supports("mma") which is not supported yet. So patching
code for this method . Patched code will check for P10 Processor at
run-time and based on that routine will be set.
6. onnxruntime/python/onnxruntime_validation.py
[python binding ] Adding AIX check in check_distro_info()
7. onnxruntime/test/providers/cpu/generator/random_test.cc
[gcc building support] updating previous check for AIX , along with
clang. So in case of gcc, else block will hit.
8. onnxruntime/test/python/onnxruntime_test_python.py
[python binding ] powerpc check on platform.processor()
9. setup.py
[python binding ] Adding AIX check for list of libs.
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| .config | ||
| .devcontainer | ||
| .gdn | ||
| .github | ||
| .pipelines | ||
| .vscode | ||
| cgmanifests | ||
| cmake | ||
| csharp | ||
| dockerfiles | ||
| docs | ||
| include/onnxruntime/core | ||
| java | ||
| js | ||
| objectivec | ||
| onnxruntime | ||
| orttraining | ||
| rust | ||
| samples | ||
| tools | ||
| winml | ||
| .clang-format | ||
| .clang-tidy | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| .lintrunner.toml | ||
| build.bat | ||
| build.sh | ||
| build_arm64x.bat | ||
| CITATION.cff | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| lgtm.yml | ||
| LICENSE | ||
| NuGet.config | ||
| ort.wprp | ||
| ORT_icon_for_light_bg.png | ||
| packages.config | ||
| pyproject.toml | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-doc.txt | ||
| requirements-lintrunner.txt | ||
| requirements-training.txt | ||
| requirements.txt | ||
| SECURITY.md | ||
| setup.py | ||
| 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
-
General Information: onnxruntime.ai
-
Usage documentation and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
-
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
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.