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### How to run it locally
1. conda install ninja
2. "C:\Program Files\Microsoft Visual
Studio\2022\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" x64
3. python.exe {ort_repo}\tools\ci_build\build.py --config RelWithDebInfo
--build_dir {ort_repo}\build_cuda --skip_submodule_sync --build_csharp
--update --parallel --cmake_generator "Ninja" --build_shared_lib
--enable_onnx_tests --enable_pybind --build_java --build_nodejs
--use_cuda "--cuda_home=C:\Program Files\NVIDIA GPU Computing
Toolkit\CUDA\v11.8" --enable_cuda_profiling --cmake_extra_defines
CMAKE_CUDA_ARCHITECTURES=60
4. cd build_cuda\RelWithDebInfo
5. cmake --build . j16
### Motivation and Context
In packaging pipelines, we often come across a random issue that the
building with CUDA on Windows takes too much time.
Although it has been reduced much by moving the building to the CPU
machine.
We're planning to build with Ninja instead of msbuild in Packaging
pipelines, thus, nvcc can run parallelly.
It's the first step to support it locally.
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| test | ||
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| .npmignore | ||
| CMakeLists.txt | ||
| package-lock.json | ||
| package.json | ||
| README.md | ||
| tsconfig.json | ||
ONNX Runtime Node.js Binding
ONNX Runtime Node.js binding enables Node.js applications to run ONNX model inference.
Usage
Install the latest stable version:
npm install onnxruntime-node
Refer to ONNX Runtime JavaScript examples for samples and tutorials.
Requirements
ONNXRuntime works on Node.js v12.x+ or Electron v5.x+.
Following platforms are supported with pre-built binaries:
- Windows x64 CPU NAPI_v3
- Linux x64 CPU NAPI_v3
- MacOS x64 CPU NAPI_v3
To use on platforms without pre-built binaries, you can build Node.js binding from source and consume it by npm install <onnxruntime_repo_root>/js/node/. See also instructions for building ONNX Runtime Node.js binding locally.
GPU Support
Right now, the Windows version supports only the DML provider. Linux x64 can use CUDA and TensorRT.
License
License information can be found here.