Build ONNX Runtime with Execution Providers

Execution Provider Shared Libraries
CUDA
TensorRT
NVIDIA Jetson TX1/TX2/Nano/Xavier
oneDNN
OpenVINO
DirectML
ARM Compute Library
ArmNN
RKNPU
Vitis-AI
AMD MIGraphX
NNAPI
CoreML

Execution Provider Shared Libraries

The oneDNN, TensorRT, and OpenVINO providers are built as shared libraries vs being statically linked into the main onnxruntime. This enables them to be loaded only when needed, and if the dependent libraries of the provider are not installed onnxruntime will still run fine, it just will not be able to use that provider. For non shared library providers, all dependencies of the provider must exist to load onnxruntime.

Built files

On Windows, shared provider libraries will be named ‘onnxruntime_providers_*.dll’ (for example onnxruntime_providers_openvino.dll). On Unix, they will be named ‘libonnxruntime_providers_*.so’ On Mac, they will be named ‘libonnxruntime_providers_*.dylib’.

There is also a shared library that shared providers depend on called onnxruntime_providers_shared (with the same naming convension applied as above).

Note: It is not recommended to put these libraries in a system location or added to a library search path (like LD_LIBRARY_PATH on Unix). If multiple versions of onnxruntime are installed on the system this can make them find the wrong libraries and lead to undefined behavior.

Loading the shared providers

Shared provider libraries are loaded by the onnxruntime code (do not load or depend on them in your client code). The API for registering shared or non shared providers is identical, the difference is that shared ones will be loaded at runtime when the provider is added to the session options (through a call like OrtSessionOptionsAppendExecutionProvider_OpenVINO or SessionOptionsAppendExecutionProvider_OpenVINO in the C API). If a shared provider library cannot be loaded (if the file doesn’t exist, or its dependencies don’t exist or not in the path) then an error will be returned.

The onnxruntime code will look for the provider shared libraries in the same location as the onnxruntime shared library is (or the executable statically linked to the static library version).

CUDA

Prerequisites

Install CUDA and cuDNN
- The path to the CUDA installation must be provided via the CUDA_PATH environment variable, or the --cuda_home parameter
- The path to the cuDNN installation (include the cuda folder in the path) must be provided via the cuDNN_PATH environment variable, or --cudnn_home parameter. The cuDNN path should contain bin, include and lib directories.
- The path to the cuDNN bin directory must be added to the PATH environment variable so that cudnn64_8.dll is found.

Build Instructions

Windows

.\build.bat --use_cuda --cudnn_home <cudnn home path> --cuda_home <cuda home path>

Linux

./build.sh --use_cuda --cudnn_home <cudnn home path> --cuda_home <cuda home path>

A Dockerfile is available here.

Notes

Depending on compatibility between the CUDA, cuDNN, and Visual Studio 2017 versions you are using, you may need to explicitly install an earlier version of the MSVC toolset.
CUDA 10.0 is known to work with toolsets from 14.11 up to 14.16 (Visual Studio 2017 15.9), and should continue to work with future Visual Studio versions
CUDA 9.2 is known to work with the 14.11 MSVC toolset (Visual Studio 15.3 and 15.4)
- To install the 14.11 MSVC toolset, see this page.
- To use the 14.11 toolset with a later version of Visual Studio 2017 you have two options:
  1. Setup the Visual Studio environment variables to point to the 14.11 toolset by running vcvarsall.bat, prior to running the build script. e.g. if you have VS2017 Enterprise, an x64 build would use the following command "C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\VC\Auxiliary\Build\vcvarsall.bat" amd64 -vcvars_ver=14.11 For convenience, .\build.amd64.1411.bat will do this and can be used in the same way as .\build.bat. e.g. ` .\build.amd64.1411.bat –use_cuda`
1. Alternatively, if you have CMake 3.13 or later you can specify the toolset version via the --msvc_toolset build script parameter. e.g. .\build.bat --msvc_toolset 14.11
If you have multiple versions of CUDA installed on a Windows machine and are building with Visual Studio, CMake will use the build files for the highest version of CUDA it finds in the BuildCustomization folder. e.g. C:\Program Files (x86)\Microsoft Visual Studio\2017\Enterprise\Common7\IDE\VC\VCTargets\BuildCustomizations. If you want to build with an earlier version, you must temporarily remove the ‘CUDA x.y.*’ files for later versions from this directory.

TensorRT

See more information on the TensorRT Execution Provider here.

Prerequisites

Install CUDA and cuDNN
- The TensorRT execution provider for ONNX Runtime is built and tested with CUDA 10.2/11.0/11.1/11.4 and cuDNN 8.0/cuDNN 8.2.
- The path to the CUDA installation must be provided via the CUDA_PATH environment variable, or the --cuda_home parameter. The CUDA path should contain bin, include and lib directories.
- The path to the CUDA bin directory must be added to the PATH environment variable so that nvcc is found.
- The path to the cuDNN installation (path to folder that contains libcudnn.so) must be provided via the cuDNN_PATH environment variable, or --cudnn_home parameter.
Install TensorRT
- The TensorRT execution provider for ONNX Runtime is built and tested with TensorRT 8.2.3.0.
- To use different versions of TensorRT, prior to building, change the onnx-tensorrt submodule to a branch corresponding to the TensorRT version. e.g. To use TensorRT 7.2.x,
  - cd cmake/external/onnx-tensorrt
  - git remote update
  - git checkout 7.2.1
  - build as usual (but add the –skip_submodule_sync command so it doesn’t update the submodule)
- The path to TensorRT installation must be provided via the --tensorrt_home parameter.

Build Instructions

Windows

.\build.bat --cudnn_home <path to cuDNN home> --cuda_home <path to CUDA home> --use_tensorrt --tensorrt_home <path to TensorRT home>

Linux

# to build with the latest supported TensorRT version
./build.sh --cudnn_home <path to cuDNN e.g. /usr/lib/x86_64-linux-gnu/> --cuda_home <path to folder for CUDA e.g. /usr/local/cuda> --use_tensorrt --tensorrt_home <path to TensorRT home>
# to build with different version. e.g. TensorRT 7.2.1
cd cmake/external/onnx-tensorrt
git remote update
git checkout 7.2.1
./build.sh  --cudnn_home <path to cuDNN e.g. /usr/lib/x86_64-linux-gnu/> --cuda_home <path to folder for CUDA e.g. /usr/local/cuda> --use_tensorrt --tensorrt_home <path to TensorRT home> --skip_submodule_sync

Dockerfile instructions are available here

NVIDIA Jetson TX1/TX2/Nano/Xavier

Build Instructions

These instructions are for JetPack SDK 4.6.1.

Clone the ONNX Runtime repo on the Jetson host

 git clone --recursive https://github.com/microsoft/onnxruntime

Specify the CUDA compiler, or add its location to the PATH.

Cmake can’t automatically find the correct nvcc if it’s not in the PATH.
```
 export CUDACXX="/usr/local/cuda/bin/nvcc"
```
or:
```
 export PATH="/usr/local/cuda/bin:${PATH}"
```

Install the ONNX Runtime build dependencies on the Jetpack 4.6.1 host:

 sudo apt install -y --no-install-recommends \
   build-essential software-properties-common libopenblas-dev \
   libpython3.6-dev python3-pip python3-dev python3-setuptools python3-wheel

Cmake is needed to build ONNX Runtime. Because the minimum required version is 3.18, it is necessary to build CMake from source. Download Unix/Linux sources from https://cmake.org/download/ and follow https://cmake.org/install/ to build from source. Version 3.23.0 has been tested on Jetson.

Build the ONNX Runtime Python wheel:

 ./build.sh --config Release --update --build --parallel --build_wheel \
 --use_cuda --cuda_home /usr/local/cuda --cudnn_home /usr/lib/aarch64-linux-gnu

Note: You may optionally build with TensorRT support.

 ./build.sh --config Release --update --build --parallel --build_wheel \
 --use_tensorrt --cuda_home /usr/local/cuda --cudnn_home /usr/lib/aarch64-linux-gnu \
 --tensorrt_home /usr/lib/aarch64-linux-gnu

oneDNN

See more information on ondDNN (formerly DNNL) here.

Build Instructions

The DNNL execution provider can be built for Intel CPU or GPU. To build for Intel GPU, install Intel SDK for OpenCL Applications. Install the latest GPU driver - Windows graphics driver, Linux graphics compute runtime and OpenCL driver.

Windows

.\build.bat --use_dnnl

Linux

./build.sh --use_dnnl

To build for Intel GPU, replace dnnl_opencl_root with the path of the Intel SDK for OpenCL Applications.

Windows

.\build.bat --use_dnnl --dnnl_gpu_runtime ocl --dnnl_opencl_root "c:\program files (x86)\intelswtools\sw_dev_tools\opencl\sdk"

Linux

./build.sh --use_dnnl --dnnl_gpu_runtime ocl --dnnl_opencl_root "/opt/intel/sw_dev_tools/opencl-sdk"

Build Phython Wheel

OneDNN EP build supports building Python wheel for both Windows and linux using flag –build_wheel

.\build.bat --config RelWithDebInfo --parallel --build_shared_lib --cmake_generator "Visual Studio 16 2019" --build_wheel --use_dnnl --dnnl_gpu_runtime ocl --dnnl_opencl_root "C:\Program Files (x86)\IntelSWTools\system_studio_2020\OpenCL\sdk"

OpenVINO

See more information on the OpenVINO Execution Provider here.

Prerequisites

Install the Intel^® Distribution of OpenVINO^TM Toolkit Release 2022.1 for the appropriate OS and target hardware:
- Linux - CPU, GPU, VPU, VAD-M
- Windows - CPU, GPU, VPU, VAD-M.
Follow documentation for detailed instructions.

2022.1 is the recommended OpenVINO version. OpenVINO 2021.3 is minimal OpenVINO version requirement. The minimum ubuntu version to support 2022.1 is 18.04.

Configure the target hardware with specific follow on instructions:
- To configure Intel^® Processor Graphics(GPU) please follow these instructions: Windows, Linux
- To configure Intel^® Movidius^TM USB, please follow this getting started guide: Linux
- To configure Intel^® Vision Accelerator Design based on 8 Movidius^TM MyriadX VPUs, please follow this configuration guide: Windows, Linux. Follow steps 3 and 4 to complete the configuration.
Initialize the OpenVINO environment by running the setupvars script as shown below:
- For Linux run till OpenVINO 2021.4 version:
```
 $ source <openvino_install_directory>/bin/setupvars.sh
```
- For Linux run from OpenVINO 2022.1 version:
```
 $ source <openvino_install_directory>/setupvars.sh
```
- For Windows run till OpenVINO 2021.4 version:
```
 C:\ <openvino_install_directory>\bin\setupvars.bat
```
- For Windows run from OpenVINO 2022.1 version:
```
 C:\ <openvino_install_directory>\setupvars.bat
```
  Note: If you are using a dockerfile to use OpenVINO-EP, sourcing OpenVINO won’t be possible within the dockerfile. You would have to explicitly set the LD_LIBRARY_PATH to point to OpenVINO libraries location. Refer our [dockerfile].(https://github.com/microsoft/onnxruntime/blob/master/dockerfiles/Dockerfile.openvino)
Extra configuration step for Intel^® Vision Accelerator Design based on 8 Movidius^TM MyriadX VPUs:
- After setting the environment using setupvars script, follow these steps to change the default scheduler of VAD-M to Bypass:
  - Edit the hddl_service.config file from $HDDL_INSTALL_DIR/config/hddl_service.config and change the field “bypass_device_number” to 8.
  - Restart the hddl daemon for the changes to take effect.
  - Note that if OpenVINO was installed with root permissions, this file has to be changed with the same permissions.

Build Instructions

Windows

.\build.bat --config RelWithDebInfo --use_openvino <hardware_option> --build_shared_lib

Note: The default Windows CMake Generator is Visual Studio 2017, but you can also use the newer Visual Studio 2019 by passing --cmake_generator "Visual Studio 16 2019" to .\build.bat

Linux

./build.sh --config RelWithDebInfo --use_openvino <hardware_option> --build_shared_lib

--use_openvino builds the OpenVINO Execution Provider in ONNX Runtime.
<hardware_option>: Specifies the default hardware target for building OpenVINO Execution Provider. This can be overriden dynamically at runtime with another option (refer to OpenVINO-ExecutionProvider for more details on dynamic device selection). Below are the options for different Intel target devices.

Hardware Option	Target Device
`CPU_FP32`	Intel^® CPUs
`GPU_FP32`	Intel^® Integrated Graphics
`GPU_FP16`	Intel^® Integrated Graphics with FP16 quantization of models
`MYRIAD_FP16`	Intel^® Movidius^TM USB sticks
`VAD-M_FP16`	Intel^® Vision Accelerator Design based on 8 Movidius^TM MyriadX VPUs
`VAD-F_FP32`	Intel^® Vision Accelerator Design with an Intel^® Arria^® 10 FPGA
`HETERO:DEVICE_TYPE_1,DEVICE_TYPE_2,DEVICE_TYPE_3...`	All Intel^® silicons mentioned above
`MULTI:DEVICE_TYPE_1,DEVICE_TYPE_2,DEVICE_TYPE_3...`	All Intel^® silicons mentioned above
`AUTO:DEVICE_TYPE_1,DEVICE_TYPE_2,DEVICE_TYPE_3...`	All Intel^® silicons mentioned above

Specifying Hardware Target for HETERO or Multi or AUTO device Build:

HETERO:DEVICE_TYPE_1,DEVICE_TYPE_2,DEVICE_TYPE_3… The DEVICE_TYPE can be any of these devices from this list [‘CPU’,’GPU’,’MYRIAD’,’FPGA’,’HDDL’]

A minimum of two device’s should be specified for a valid HETERO or MULTI or AUTO device build.

Example's: HETERO:MYRIAD,CPU or AUTO:GPU,CPU or MULTI:MYRIAD,GPU,CPU

Disable subgraph partition Feature

Builds the OpenVINO Execution Provider in ONNX Runtime with sub graph partitioning disabled.
With this option enabled. Fully supported models run on OpenVINO Execution Provider else they completely fall back to default CPU EP.
To enable this feature during build time. Use --use_openvino <hardware_option>_NO_PARTITION

Usage: --use_openvino CPU_FP32_NO_PARTITION or --use_openvino GPU_FP32_NO_PARTITION or
       --use_openvino GPU_FP16_NO_PARTITION or --use_openvino MYRIAD_FP16_NO_PARTITION or
       --use_openvino VAD-F_FP32_NO_PARTITION or --use_openvino VAD-M_FP16_NO_PARTITION

For more information on OpenVINO Execution Provider's ONNX Layer support, Topology support, and Intel hardware enabled, please refer to the document OpenVINO-ExecutionProvider

Prerequisites

The Nuphar execution provider for ONNX Runtime is built and tested with LLVM 9.0.0. Because of TVM’s requirement when building with LLVM, you need to build LLVM from source. To build the debug flavor of ONNX Runtime, you need the debug build of LLVM.

Windows (Visual Studio 2017):

 REM download llvm source code 9.0.0 and unzip to \llvm\source\path, then install to \llvm\install\path
 cd \llvm\source\path
 mkdir build
 cd build
 cmake .. -G "Visual Studio 15 2017 Win64" -DLLVM_TARGETS_TO_BUILD=X86 -DLLVM_ENABLE_DIA_SDK=OFF
 msbuild llvm.sln /maxcpucount /p:Configuration=Release /p:Platform=x64
 cmake -DCMAKE_INSTALL_PREFIX=\llvm\install\path -DBUILD_TYPE=Release -P cmake_install.cmake

Note that following LLVM cmake patch is necessary to make the build work on Windows, Linux does not need to apply the patch. The patch is to fix the linking warning LNK4199 caused by this LLVM commit

diff --git "a/lib\\Support\\CMakeLists.txt" "b/lib\\Support\\CMakeLists.txt"
index 7dfa97c..6d99e71 100644
--- "a/lib\\Support\\CMakeLists.txt"
+++ "b/lib\\Support\\CMakeLists.txt"
@@ -38,12 +38,6 @@ elseif( CMAKE_HOST_UNIX )
   endif()
 endif( MSVC OR MINGW )

-# Delay load shell32.dll if possible to speed up process startup.
-set (delayload_flags)
-if (MSVC)
-  set (delayload_flags delayimp -delayload:shell32.dll -delayload:ole32.dll)
-endif()
-
 # Link Z3 if the user wants to build it.
 if(LLVM_WITH_Z3)
   set(Z3_LINK_FILES ${Z3_LIBRARIES})
@@ -187,7 +181,7 @@ add_llvm_library(LLVMSupport
   ${LLVM_MAIN_INCLUDE_DIR}/llvm/ADT
   ${LLVM_MAIN_INCLUDE_DIR}/llvm/Support
   ${Backtrace_INCLUDE_DIRS}
-  LINK_LIBS ${system_libs} ${delayload_flags} ${Z3_LINK_FILES}
+  LINK_LIBS ${system_libs} ${Z3_LINK_FILES}
   )

 set_property(TARGET LLVMSupport PROPERTY LLVM_SYSTEM_LIBS "${system_libs}")

Linux Download llvm source code 9.0.0 and unzip to /llvm/source/path, then install to /llvm/install/path

   cd /llvm/source/path
   mkdir build
   cd build
   cmake .. -DLLVM_TARGETS_TO_BUILD=X86 -DCMAKE_BUILD_TYPE=Release
   make -j$(nproc)
   cmake -DCMAKE_INSTALL_PREFIX=/llvm/install/path -DBUILD_TYPE=Release -P cmake_install.cmake

Build Instructions

Windows

.\build.bat --llvm_path=\llvm\install\path\lib\cmake\llvm --use_mklml --use_nuphar --build_shared_lib --build_csharp --enable_pybind --config=Release

These instructions build the release flavor. The Debug build of LLVM would be needed to build with the Debug flavor of ONNX Runtime.

Linux:

./build.sh --llvm_path=/llvm/install/path/lib/cmake/llvm --use_mklml --use_nuphar --build_shared_lib --build_csharp --enable_pybind --config=Release

Dockerfile instructions are available here.

DirectML

See more information on the DirectML execution provider here.

Windows

.\build.bat --use_dml

Notes

The DirectML execution provider supports building for both x64 and x86 architectures. DirectML is only supported on Windows.

ARM Compute Library

See more information on the ACL Execution Provider here.

Prerequisites

Supported backend: i.MX8QM Armv8 CPUs
Supported BSP: i.MX8QM BSP
- Install i.MX8QM BSP: source fsl-imx-xwayland-glibc-x86_64-fsl-image-qt5-aarch64-toolchain-4*.sh

Set up the build environment

source /opt/fsl-imx-xwayland/4.*/environment-setup-aarch64-poky-linux
alias cmake="/usr/bin/cmake -DCMAKE_TOOLCHAIN_FILE=$OECORE_NATIVE_SYSROOT/usr/share/cmake/OEToolchainConfig.cmake"

See Build ARM below for information on building for ARM devices

Build Instructions

Configure ONNX Runtime with ACL support:

cmake ../onnxruntime-arm-upstream/cmake -DONNX_CUSTOM_PROTOC_EXECUTABLE=/usr/bin/protoc -Donnxruntime_RUN_ONNX_TESTS=OFF -Donnxruntime_GENERATE_TEST_REPORTS=ON -Donnxruntime_DEV_MODE=ON -DPYTHON_EXECUTABLE=/usr/bin/python3 -Donnxruntime_USE_CUDA=OFF -Donnxruntime_USE_NSYNC=OFF -Donnxruntime_CUDNN_HOME= -Donnxruntime_USE_JEMALLOC=OFF -Donnxruntime_ENABLE_PYTHON=OFF -Donnxruntime_BUILD_CSHARP=OFF -Donnxruntime_BUILD_SHARED_LIB=ON -Donnxruntime_USE_EIGEN_FOR_BLAS=ON -Donnxruntime_USE_OPENBLAS=OFF -Donnxruntime_USE_ACL=ON -Donnxruntime_USE_DNNL=OFF -Donnxruntime_USE_MKLML=OFF -Donnxruntime_USE_OPENMP=ON -Donnxruntime_USE_TVM=OFF -Donnxruntime_USE_LLVM=OFF -Donnxruntime_ENABLE_MICROSOFT_INTERNAL=OFF -Donnxruntime_USE_BRAINSLICE=OFF -Donnxruntime_USE_NUPHAR=OFF -Donnxruntime_USE_EIGEN_THREADPOOL=OFF -Donnxruntime_BUILD_UNIT_TESTS=ON -DCMAKE_BUILD_TYPE=RelWithDebInfo

The -Donnxruntime_USE_ACL=ON option will use, by default, the 19.05 version of the Arm Compute Library. To set the right version you can use: -Donnxruntime_USE_ACL_1902=ON, -Donnxruntime_USE_ACL_1905=ON, -Donnxruntime_USE_ACL_1908=ON or -Donnxruntime_USE_ACL_2002=ON;

To use a library outside the normal environment you can set a custom path by using -Donnxruntime_ACL_HOME and -Donnxruntime_ACL_LIBS tags that defines the path to the ComputeLibrary directory and the build directory respectively.

-Donnxruntime_ACL_HOME=/path/to/ComputeLibrary, -Donnxruntime_ACL_LIBS=/path/to/build

Build ONNX Runtime library, test and performance application:
```
make -j 6
```

Deploy ONNX runtime on the i.MX 8QM board

libonnxruntime.so.0.5.0
onnxruntime_perf_test
onnxruntime_test_all

Native Build Instructions

Validated on Jetson Nano and Jetson Xavier

Build ACL Library (skip if already built)

 cd ~
 git clone -b v20.02 https://github.com/Arm-software/ComputeLibrary.git
 cd ComputeLibrary
 sudo apt-get install -y scons g++-arm-linux-gnueabihf
 scons -j8 arch=arm64-v8a  Werror=1 debug=0 asserts=0 neon=1 opencl=1 examples=1 build=native

Cmake is needed to build ONNX Runtime. Because the minimum required version is 3.13, it is necessary to build CMake from source. Download Unix/Linux sources from https://cmake.org/download/ and follow https://cmake.org/install/ to build from source. Version 3.17.5 and 3.18.4 have been tested on Jetson.
Build onnxruntime with –use_acl flag with one of the supported ACL version flags. (ACL_1902 ACL_1905 ACL_1908 ACL_2002)

ArmNN

See more information on the ArmNN Execution Provider here.

Prerequisites

Supported backend: i.MX8QM Armv8 CPUs
Supported BSP: i.MX8QM BSP
- Install i.MX8QM BSP: source fsl-imx-xwayland-glibc-x86_64-fsl-image-qt5-aarch64-toolchain-4*.sh
Set up the build environment

source /opt/fsl-imx-xwayland/4.*/environment-setup-aarch64-poky-linux
alias cmake="/usr/bin/cmake -DCMAKE_TOOLCHAIN_FILE=$OECORE_NATIVE_SYSROOT/usr/share/cmake/OEToolchainConfig.cmake"

See Build ARM below for information on building for ARM devices

Build Instructions

./build.sh --use_armnn

The Relu operator is set by default to use the CPU execution provider for better performance. To use the ArmNN implementation build with –armnn_relu flag

./build.sh --use_armnn --armnn_relu

The Batch Normalization operator is set by default to use the CPU execution provider. To use the ArmNN implementation build with –armnn_bn flag

./build.sh --use_armnn --armnn_bn

To use a library outside the normal environment you can set a custom path by providing the –armnn_home and –armnn_libs parameters to define the path to the ArmNN home directory and build directory respectively. The ARM Compute Library home directory and build directory must also be available, and can be specified if needed using –acl_home and –acl_libs respectively.

./build.sh --use_armnn --armnn_home /path/to/armnn --armnn_libs /path/to/armnn/build  --acl_home /path/to/ComputeLibrary --acl_libs /path/to/acl/build

RKNPU

See more information on the RKNPU Execution Provider here.

Prerequisites

Supported platform: RK1808 Linux
See Build ARM below for information on building for ARM devices
Use gcc-linaro-6.3.1-2017.05-x86_64_aarch64-linux-gnu instead of gcc-linaro-6.3.1-2017.05-x86_64_arm-linux-gnueabihf, and modify CMAKE_CXX_COMPILER & CMAKE_C_COMPILER in tool.cmake:

set(CMAKE_CXX_COMPILER aarch64-linux-gnu-g++)
set(CMAKE_C_COMPILER aarch64-linux-gnu-gcc)

Build Instructions

Linux

Download rknpu_ddk to any directory.

Build ONNX Runtime library and test:

 ./build.sh --arm --use_rknpu --parallel --build_shared_lib --build_dir build_arm --config MinSizeRel --cmake_extra_defines RKNPU_DDK_PATH=<Path To rknpu_ddk> CMAKE_TOOLCHAIN_FILE=<Path To tool.cmake> ONNX_CUSTOM_PROTOC_EXECUTABLE=<Path To protoc>

Deploy ONNX runtime and librknpu_ddk.so on the RK1808 board:

 libonnxruntime.so.1.2.0
 onnxruntime_test_all
 rknpu_ddk/lib64/librknpu_ddk.so

Vitis-AI

See more information on the Xilinx Vitis-AI execution provider here.

For instructions to setup the hardware environment: Hardware setup

Linux

./build.sh --use_vitisai

Notes

The Vitis-AI execution provider is only supported on Linux.

AMD MIGraphX

See more information on the MIGraphX Execution Provider here.

Prerequisites

Install ROCM
- The MIGraphX execution provider for ONNX Runtime is built and tested with ROCM3.3
Install MIGraphX
- The path to MIGraphX installation must be provided via the --migraphx_home parameter.

Build Instructions

Linux

./build.sh --config <Release|Debug|RelWithDebInfo> --use_migraphx --migraphx_home <path to MIGraphX home>

Dockerfile instructions are available here.

NNAPI

Usage of NNAPI on Android platforms is via the NNAPI Execution Provider (EP).

See the NNAPI Execution Provider documentation for more details.

The pre-built ONNX Runtime Mobile package for Android includes the NNAPI EP.

If performing a custom build of ONNX Runtime, support for the NNAPI EP or CoreML EP must be enabled when building.

Create a minimal build with NNAPI EP support

Please see the instructions for setting up the Android environment required to build. The Android build can be cross-compiled on Windows or Linux.

Once you have all the necessary components setup, follow the instructions to create the custom build, with the following changes:

Replace --minimal_build with --minimal_build extended to enable support for execution providers that dynamically create kernels at runtime, which is required by the NNAPI EP.
Add --use_nnapi to include the NNAPI EP in the build

Example build commands with the NNAPI EP enabled

Windows example:

<ONNX Runtime repository root>.\build.bat --config MinSizeRel --android --android_sdk_path D:\Android --android_ndk_path D:\Android\ndk\21.1.6352462\ --android_abi arm64-v8a --android_api 29 --cmake_generator Ninja --minimal_build extended --use_nnapi --disable_ml_ops --disable_exceptions --build_shared_lib --skip_tests --include_ops_by_config <config file from model conversion>

Linux example:

<ONNX Runtime repository root>./build.sh --config MinSizeRel --android --android_sdk_path /Android --android_ndk_path /Android/ndk/21.1.6352462/ --android_abi arm64-v8a --android_api 29 --minimal_build extended --use_nnapi --disable_ml_ops --disable_exceptions --build_shared_lib --skip_tests --include_ops_by_config <config file from model conversion>`

CoreML

Usage of CoreML on iOS and macOS platforms is via the CoreML EP.

See the CoreML Execution Provider documentation for more details.

The pre-built ONNX Runtime Mobile package for iOS includes the CoreML EP.

Create a minimal build with CoreML EP support

Please see the instructions for setting up the iOS environment required to build. The iOS/macOS build must be performed on a mac machine.

Once you have all the necessary components setup, follow the instructions to create the custom build, with the following changes:

Replace --minimal_build with --minimal_build extended to enable support for execution providers that dynamically create kernels at runtime, which is required by the CoreML EP.
Add --use_coreml to include the CoreML EP in the build

Build ONNX Runtime with Execution Providers

Contents

Execution Provider Shared Libraries

Built files

Loading the shared providers

CUDA

Prerequisites

Build Instructions

Windows

Linux

Notes

TensorRT

Prerequisites

Build Instructions

Windows

Linux

NVIDIA Jetson TX1/TX2/Nano/Xavier

Build Instructions

oneDNN

Build Instructions

Windows

Linux

Windows

Linux

Build Phython Wheel

OpenVINO

Prerequisites

Build Instructions

Windows

Linux

Disable subgraph partition Feature

Prerequisites

Build Instructions

Windows

Linux:

DirectML

Windows

Notes

ARM Compute Library

Prerequisites

Build Instructions

Native Build Instructions

ArmNN

Prerequisites

Build Instructions

RKNPU

Prerequisites

Build Instructions

Linux

Vitis-AI

Linux

Notes

AMD MIGraphX

Prerequisites

Build Instructions

Linux

NNAPI

Create a minimal build with NNAPI EP support

Example build commands with the NNAPI EP enabled

CoreML

Create a minimal build with CoreML EP support