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update TRT-RTX EP docs
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_config.yml

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- jekyll-redirect-from
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kramdown:
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parse_block_html: true
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toc_levels: '2'
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toc_levels: [2, 3, 4]
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logo: '/images/ONNX-Runtime-logo.svg'
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aux_links:
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'ONNX Runtime':

docs/build/eps.md

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---
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## NVIDIA TensorRT RTX
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See more information on the TensorRT RTX Execution Provider [here](../execution-providers/TensorRTRTX-ExecutionProvider.md).
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### Minimum requirements
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| ONNX Runtime | TensorRT-RTX | CUDA Toolkit |
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| :----------- | :----------- | :------------- |
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| main branch | 1.1 | 12.9 |
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| 1.23 | 1.1 | 12.9 |
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| 1.22 | 1.0 | 12.8 |
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### Pre-requisites
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* Install git, cmake, Python 3.12
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* Install latest [NVIDIA driver](https://www.nvidia.com/en-us/drivers/)
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* Install [CUDA toolkit 12.9](https://developer.nvidia.com/cuda-12-9-1-download-archive)
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* Install [TensorRT RTX](https://docs.nvidia.com/deeplearning/tensorrt-rtx/latest/installing-tensorrt-rtx/installing.html)
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* For Windows only, install [Visual Studio](https://visualstudio.microsoft.com/downloads/)
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* Set TensorRT-RTX dlls in `PATH` or put it in same folder as application exe
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```sh
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git clone https://github.com/microsoft/onnxruntime.git
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cd onnxruntime
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```
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### Windows
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```powershell
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.\build.bat --config Release --build_dir build --parallel --use_nv_tensorrt_rtx --tensorrt_rtx_home "path\to\tensorrt-rtx" --cuda_home "path\to\cuda\home" --cmake_generator "Visual Studio 17 2022" --build_shared_lib --skip_tests --build --update --use_vcpkg
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```
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### Linux
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```sh
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./build.sh --config Release --build_dir build --parallel --use_nv_tensorrt_rtx --tensorrt_rtx_home "path/to/tensorrt-rtx" --cuda_home "path/to/cuda/home" --build_shared_lib --skip_tests --build --update
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```
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### Run unit test
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```powershell
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.\build\Release\Release\onnxruntime_test_all.exe --gtest_filter=*NvExecutionProviderTest.*
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```
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### Python wheel
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```powershell
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# build the python wheel
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.\build.bat --config Release --build_dir build --parallel --use_nv_tensorrt_rtx --tensorrt_rtx_home "path\to\tensorrt-rtx" --cuda_home "path\to\cuda\home" --cmake_generator "Visual Studio 17 2022" --build_shared_lib --skip_tests --build_wheel
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# install
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pip install "build\Release\Release\dist\onnxruntime-1.23.0-cp312-cp312-win_amd64.whl"
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```
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> NOTE: TensorRT-RTX .dll or .so are in `PATH` or in the same folder as the application
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---
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## NVIDIA Jetson TX1/TX2/Nano/Xavier/Orin
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### Build Instructions
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* For a portion of Jetson devices like the Xavier series, higher power mode involves more cores (up to 6) to compute but it consumes more resource when building ONNX Runtime. Set `--parallel 1` in the build command if OOM happens and system is hanging.
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## TensorRT-RTX
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See more information on the NV TensorRT RTX Execution Provider [here](../execution-providers/TensorRTRTX-ExecutionProvider.md).
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### Prerequisites
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{: .no_toc }
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* Follow [instructions for CUDA execution provider](#cuda) to install CUDA and setup environment variables.
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* Intall TensorRT for RTX from nvidia.com (TODO: add link when available)
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### Build Instructions
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{: .no_toc }
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`build.bat --config Release --parallel 32 --build_dir _build --build_shared_lib --use_nv_tensorrt_rtx --tensorrt_home "C:\dev\TensorRT-RTX-1.1.0.3" --cuda_home "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.9" --cmake_generator "Visual Studio 17 2022" --use_vcpkg`
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Replace the --tensorrt_home and --cuda_home with correct paths to CUDA and TensorRT-RTX installations.
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## oneDNN
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```bash
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./build.sh --config <Release|Debug|RelWithDebInfo> --build_shared_lib --build_wheel --use_azure
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```
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```

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