f633caa0b1
### Changes
1. CMake option `onnxruntime_USE_VCPKG`. It will be used in the vcpkg
port
* Unit test may fail because this option leads to a mixture of
unexpected external library versions.
Especially ONNX, Protobuf, and Flatbuffers version can be different
2. Overhaul of `onnxruntime_external_deps.cmake`
* Make `FetchContent_Declare` to try `find_package`.
See
https://cmake.org/cmake/help/latest/guide/using-dependencies/index.html
* Relocated `FetchContent_Declare` and `FetchContent_MakeAvailable`(or
`onnxruntime_fetchcontent_makeavailable`) to closer lines.
It was too hard to navigate the entire file to search related
sections...
* Alias `IMPORTED` targets like build targets (e.g. `ONNX::onnx` -->
`onnx`)
```cmake
# The script uses `find_package` with the changes.
# In this case, use vcpkg to search dependencies
# See https://cmake.org/cmake/help/latest/guide/using-dependencies/index.html
include(external/onnxruntime_external_deps.cmake)
```
3. Create CMakePresets.json and presets to [run vcpkg in manifest
mode](https://learn.microsoft.com/en-us/vcpkg/concepts/manifest-mode)
* Currently, it's NOT for training build
* Main triplets are `x64-windows` and `x64-osx`
```pwsh
Push-Location "cmake"
cmake --preset "x64-windows-vcpkg"
cmake --build --preset "x64-windows-vcpkg-debug"
Pop-Location
```
```bash
pushd "cmake"
cmake --preset "x64-osx-vcpkg"
cmake --build --preset "x64-osx-vcpkg-debug"
popd
```
4. Updated tools/ci_build/build.py
* `--use_vcpkg` option: it needs `CMAKE_TOOLCHAIN_FILE` with
[vcpkg.cmake toolchain
script](https://github.com/microsoft/vcpkg/blob/master/scripts/buildsystems/vcpkg.cmake)
* `--compile_no_warning_as_error` is recommended because library version
differences will cause unexpected compiler warnings
```bash
python ./tools/ci_build/build.py \
--compile_no_warning_as_error \
--use_vcpkg \
--cmake_extra_defines "CMAKE_TOOLCHAIN_FILE:FILEPATH=${VCPKG_ROOT}/scripts/buildsystems/vcpkg.cmake" \
--cmake_extra_defines "VCPKG_TARGET_TRIPLET=..."
```
5. Created Job `Vcpkg` for Windows and macOS
* Show how to setup and use vcpkg.
Similar to the CMakePresets.json usage
### Motivation and Context
* Help #7150
* Help https://github.com/microsoft/vcpkg/pull/36850
* https://github.com/luncliff/vcpkg-registry/pull/212
* https://github.com/microsoft/vcpkg/pull/39881
* https://github.com/luncliff/vcpkg-registry/pull/215
* https://github.com/luncliff/vcpkg-registry/pull/216
* https://github.com/luncliff/vcpkg-registry/pull/227
*
https://cmake.org/cmake/help/latest/guide/using-dependencies/index.html
*
https://github.com/microsoft/vcpkg/blob/master/scripts/buildsystems/vcpkg.cmake
### Future Works?
More feature coverage with the vcpkg supported libraries
* CUDA feature support
* Training feature support
|
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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
-
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.