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Building ONNX Runtime

Dockerfiles are available here to help you get started.

Pre-built packages are available at the locations indicated here.

Getting Started: Build the baseline CPU version of ONNX Runtime from source

Pre-Requisites

  • Checkout the source tree: 
    git clone --recursive https://github.com/Microsoft/onnxruntime
cd onnxruntime
  • Install cmake-3.13 or higher from https://cmake.org/download/.

Build Instructions

Windows

Open Developer Command Prompt for Visual Studio version you are going to use. This will properly setup the environment including paths to your compiler, linker, utilities and header files.

.\build.bat --config RelWithDebInfo --build_shared_lib --parallel

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 --build_shared_lib --parallel

Notes

  • Please note that these instructions build the debug build, which may have performance tradeoffs
  • To build the version from each release (which include Windows, Linux, and Mac variants), see these .yml files for reference: CPU, GPU
  • The build script runs all unit tests by default (for native builds and skips tests by default for cross-compiled builds).
  • If you need to install protobuf 3.6.1 from source code (cmake/external/protobuf), please note:
    • CMake flag protobuf_BUILD_SHARED_LIBS must be turned OFF. After the installation, you should have the 'protoc' executable in your PATH. It is recommended to run ldconfig to make sure protobuf libraries are found.
    • If you installed your protobuf in a non standard location it would be helpful to set the following env var:export CMAKE_ARGS="-DONNX_CUSTOM_PROTOC_EXECUTABLE=full path to protoc" so the ONNX build can find it. Also run ldconfig <protobuf lib folder path> so the linker can find protobuf libraries.
  • If you'd like to install onnx from source code (cmake/external/onnx), use: 
    export ONNX_ML=1
python3 setup.py bdist_wheel
pip3 install --upgrade dist/*.whl

Supported architectures and build environments

Architectures

x86_32 x86_64 ARM32v7 ARM64
Windows YES YES YES YES
Linux YES YES YES YES
Mac OS X NO YES NO NO

Environments

OS Supports CPU Supports GPU Notes
Windows 10 YES YES VS2019 through the latest VS2015 are supported
Windows 10
Subsystem for Linux		 YES NO
Ubuntu 16.x YES YES Also supported on ARM32v7 (experimental)
  • GCC 4.x and below are not supported.

OS/Compiler Matrix:

OS/Compiler Supports VC Supports GCC
Windows 10 YES Not tested
Linux NO YES(gcc>=4.8)

System Requirements

For other system requirements and other dependencies, please see this section.

Common Build Instructions

Description Command Additional description
Basic build build.bat (Windows)
./build.sh (Linux)
Debug build --config RelWithDebInfo Debug build
Use OpenMP --use_openmp OpenMP will parallelize some of the code for potential performance improvements. This is not recommended for running on single threads.
Build using parallel processing --parallel This is strongly recommended to speed up the build.
Build Shared Library --build_shared_lib
Build Python wheel --build_wheel
Build C# and C packages --build_csharp
Build WindowsML --use_winml
--use_dml
--build_shared_lib		 WindowsML depends on DirectML and the OnnxRuntime shared library.
Build Java package --build_java Creates an onnxruntime4j.jar in the build directory, implies --build_shared_lib

Additional Build Instructions

The complete list of build options can be found by running ./build.sh (or .\build.bat) --help

Execution Providers

Options

Architectures

Execution Providers

CUDA

Pre-Requisites

  • Install CUDA and cuDNN
    • ONNX Runtime is built and tested with CUDA 10.0 and cuDNN 7.6 using the Visual Studio 2017 14.11 toolset (i.e. Visual Studio 2017 v15.3). CUDA versions from 9.1 up to 10.1, and cuDNN versions from 7.1 up to 7.4 should also work with Visual Studio 2017.
    • 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_7.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

    2. 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.

Pre-Requisites

  • Install CUDA and cuDNN
    • The TensorRT execution provider for ONNX Runtime is built and tested with CUDA 10.2 and cuDNN 7.6.5.
    • 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 on TensorRT 7.x and is tested with TensorRT 7.0.0.11.
    • 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
./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>

Dockerfile instructions are available here

Jetson (ARM64 Builds)

See instructions for additional information and tips related to building Onnxruntime with TensorRT Execution Provider on Jetson platforms (TX1/TX2, Nano)

DNNL and MKLML

See more information on DNNL and MKL-ML here.

Build Instructions

Linux
./build.sh --use_dnnl

nGraph

See more information on the nGraph Execution Provider here.

Build Instructions

Windows

.\build.bat --use_ngraph
Linux
./build.sh --use_ngraph

OpenVINO

See more information on the OpenVINO Execution Provider here.

Pre-Requisites

  • Install the OpenVINO release along with its dependencies: [Windows](https://software.intel.com/en-us/openvino-toolkit, Linux.
    • For Linux, currently supports and is validated on OpenVINO 2019 R3.1
    • For Windows, download the 2019 R3.1 Windows Installer.
  • Install the model optimizer prerequisites for ONNX by running:
    • Windows: <openvino_install_dir>/deployment_tools/model_optimizer/install_prerequisites/install_prerequisites_onnx.bat
    • Linux: <openvino_install_dir>/deployment_tools/model_optimizer/install_prerequisites/install_prerequisites_onnx.sh
  • Initialize the OpenVINO environment by running the setupvars in \<openvino\_install\_directory\>\/bin using setupvars.bat (Windows) or source setupvars.sh (Linux)
    • To configure Intel® Processor Graphics(GPU) please follow these instructions: Windows, Linux
    • To configure Intel® MovidiusTM USB, please follow this getting started guide: Windows, Linux
    • To configure Intel® Vision Accelerator Design based on 8 MovidiusTM MyriadX VPUs, please follow this configuration guide: Windows, Linux
    • To configure Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA, please follow this configuration guide: Linux

Build Instructions

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

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>

--use_openvino: Builds the OpenVINO Execution Provider in ONNX Runtime.

--build_server: Using this flag in addition to --use_openvino builds the OpenVINO Execution Provider with ONNX Runtime Server.

  • <hardware_option>: Specifies the hardware target for building OpenVINO Execution Provider. 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® MovidiusTM USB sticks
 VAD-M_FP16  Intel® Vision Accelerator Design based on 8 MovidiusTM MyriadX VPUs
 VAD-F_FP32  Intel® Vision Accelerator Design with an Intel® Arria® 10 FPGA

For more information on OpenVINO Execution Provider's ONNX Layer support, Topology support, and Intel hardware enabled, please refer to the document OpenVINO-ExecutionProvider.md in $onnxruntime_root/docs/execution_providers

Android NNAPI

See more information on the NNAPI Execution Provider here.

Pre-Requisites

To build ONNX Runtime with the NN API EP, first install Android NDK (see Android Build instructions)

Build Instructions

The basic build commands are below. There are also some other parameters for building the Android version. See Android Build instructions for more details.

Cross compiling on Windows
./build.bat --android --android_sdk_path <android sdk path> --android_ndk_path <android ndk path> --dnnlibrary
Cross compiling on Linux
./build.sh --android --android_sdk_path <android sdk path> --android_ndk_path <android ndk path> --dnnlibrary

NUPHAR

See more information on the Nuphar Execution Provider here.

Pre-Requisites

  • 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 --use_tvm --use_llvm --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 --use_tvm --use_llvm --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

  1. 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 or -Donnxruntime_USE_ACL_1908=ON;

  1. Build ONNX Runtime library, test and performance application:
make -j 6
  1. Deploy ONNX runtime on the i.MX 8QM board
libonnxruntime.so.0.5.0
onnxruntime_perf_test
onnxruntime_test_all

Build Instructions(Jetson Nano)

  1. Build ACL Library (skip if already built)
cd ~
git clone https://github.com/Arm-software/ComputeLibrary.git
cd ComputeLibrary
sudo apt install scons
sudo apt install g++-arm-linux-gnueabihf
scons -j8 arch=arm64-v8a  Werror=1 debug=0 asserts=0 neon=1 opencl=1 examples=1 build=native
  1. Set environment variables to set include directory and shared object library path.
export CPATH=~/ComputeLibrary/include/:~/ComputeLibrary/
export LD_LIBRARY_PATH=~/ComputeLibrary/build/
  1. Build onnxruntime with --use_acl flag
./build.sh --use_acl

Options

OpenMP

Build Instructions

Windows
.\build.bat --use_openmp
Linux
./build.sh --use_openmp

OpenBLAS

Pre-Requisites

  • OpenBLAS
    • Windows: See build instructions here
    • Linux: Install the libopenblas-dev package sudo apt-get install libopenblas-dev

Build Instructions

Windows
.\build.bat --use_openblas
Linux
./build.sh --use_openblas

DebugNodeInputsOutputs

OnnxRuntime supports build options for enabling debugging of intermediate tensor shapes and data.

Build Instructions

Set onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=1

Dump tensor input/output shapes for all nodes to stdout.

# Linux
./build.sh --cmake_extra_defines onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=1
# Windows
.\build.bat --cmake_extra_defines onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=1
Set onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=2

Dump tensor input/output shapes and output data for all nodes to stdout.

# Linux
./build.sh --cmake_extra_defines onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=2
# Windows
.\build.bat --cmake_extra_defines onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=2
Set onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=0

To disable this functionality after previously enabling, set onnxruntime_DEBUG_NODE_INPUTS_OUTPUTS=0 or delete CMakeCache.txt.

Architectures

x86

Build Intsructions

Windows
  • add --x86 argument when launching .\build.bat
Linux
  • Must be built on a x86 OS
  • add --x86 argument to build.sh

ARM

We have experimental support for Linux ARM builds. Windows on ARM is well tested.

Cross compiling for ARM with Docker (Linux/Windows - FASTER, RECOMMENDED)

This method allows you to compile using a desktop or cloud VM. This is much faster than compiling natively and avoids out-of-memory issues that may be encountered when on lower-powered ARM devices. The resulting ONNX Runtime Python wheel (.whl) file is then deployed to an ARM device where it can be invoked in Python 3 scripts.

See the instructions for the the Dockerfile here.

Cross compiling on Linux (without Docker)

  1. Get the corresponding toolchain. For example, if your device is Raspberry Pi and the device os is Ubuntu 16.04, you may use gcc-linaro-6.3.1 from https://releases.linaro.org/components/toolchain/binaries

  2. Setup env vars

       export PATH=/opt/gcc-linaro-6.3.1-2017.05-x86_64_arm-linux-gnueabihf/bin:$PATH
   export CC=arm-linux-gnueabihf-gcc
   export CXX=arm-linux-gnueabihf-g++

  3. Get a pre-compiled protoc:

    You may get it from https://github.com/protocolbuffers/protobuf/releases/download/v3.11.2/protoc-3.11.2-linux-x86_64.zip . Please unzip it after downloading.

  4. (optional) Setup sysroot for enabling python extension. (TODO: will add details later)

  5. Save the following content as tool.cmake

    set(CMAKE_SYSTEM_NAME Linux)
set(CMAKE_SYSTEM_PROCESSOR arm)
set(CMAKE_CXX_COMPILER arm-linux-gnueabihf-c++)
set(CMAKE_C_COMPILER arm-linux-gnueabihf-gcc)
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)

  6. Append -DONNX_CUSTOM_PROTOC_EXECUTABLE=/path/to/protoc -DCMAKE_TOOLCHAIN_FILE=path/to/tool.cmake to your cmake args, run cmake and make to build it.

Native compiling on Linux ARM device (SLOWER)

Docker build runs on a Raspberry Pi 3B with Raspbian Stretch Lite OS (Desktop version will run out memory when linking the .so file) will take 8-9 hours in total.

sudo apt-get update
sudo apt-get install -y \
    sudo \
    build-essential \
    curl \
    libcurl4-openssl-dev \
    libssl-dev \
    wget \
    python3 \
    python3-pip \
    python3-dev \
    git \
    tar

pip3 install --upgrade pip
pip3 install --upgrade setuptools
pip3 install --upgrade wheel
pip3 install numpy

# Build the latest cmake
mkdir /code
cd /code
wget https://cmake.org/files/v3.13/cmake-3.13.5.tar.gz;
tar zxf cmake-3.13.5.tar.gz

cd /code/cmake-3.13.5
./configure --system-curl
make
sudo make install

# Prepare onnxruntime Repo
cd /code
git clone --recursive https://github.com/Microsoft/onnxruntime

# Start the basic build
cd /code/onnxruntime
./build.sh --config MinSizeRel --update --build

# Build Shared Library
./build.sh --config MinSizeRel --build_shared_lib

# Build Python Bindings and Wheel
./build.sh --config MinSizeRel --enable_pybind --build_wheel

# Build Output
ls -l /code/onnxruntime/build/Linux/MinSizeRel/*.so
ls -l /code/onnxruntime/build/Linux/MinSizeRel/dist/*.whl

Cross compiling on Windows

Using Visual C++ compilers

  1. Download and install Visual C++ compilers and libraries for ARM(64). If you have Visual Studio installed, please use the Visual Studio Installer (look under the section Individual components after choosing to modify Visual Studio) to download and install the corresponding ARM(64) compilers and libraries.

  2. Use .\build.bat and specify --arm or --arm64 as the build option to start building. Preferably use Developer Command Prompt for VS or make sure all the installed cross-compilers are findable from the command prompt being used to build using the PATH environmant variable.

Android

Pre-Requisites

Install Android NDK in Android Studio or https://developer.android.com/ndk/downloads

Build Instructions

Cross compiling on Windows
./build.bat --android --android_sdk_path <android sdk path> --android_ndk_path <android ndk path> --android_abi <android abi, e.g., arm64-v8a (default) or armeabi-v7a> --android_api <android api level, e.g., 27 (default)>
Cross compiling on Linux
./build.sh --android --android_sdk_path <android sdk path> --android_ndk_path <android ndk path> --android_abi <android abi, e.g., arm64-v8a (default) or armeabi-v7a> --android_api <android api level, e.g., 27 (default)>

Android Archive (AAR) files, which can be imported directly in Android Studio, will be generated in your_build_dir/java/build/outputs/aar.

If you want to use NNAPI Execution Provider on Android, see docs/execution_providers/NNAPI-ExecutionProvider.md.