BuildingForWindows

Build OpenVINO™ Inference Engine for Windows* systems

The software was validated on:

• Microsoft* Windows* 10 (64-bit) with Visual Studio 2019

Table of content:

• Software Requirements
• Build Steps
• Additional Build Options
• Building Inference Engine with Ninja* Build System

Software Requirements

• CMake*3.13 or higher
• Microsoft* Visual Studio 2019, version 16.8 or later
• (Optional) Intel® Graphics Driver for Windows* (26.20) driver package.
• Python 3.6 or higher for Inference Engine Python API wrapper

Build Steps

1. Clone submodules:

   git submodule update --init --recursive

2. By default, the build enables the Inference Engine GPU plugin to infer models on your Intel® Processor Graphics. This requires you to download and install the Intel® Graphics Driver for Windows (26.20) driver package before running the build. If you don't want to use the GPU plugin, use the -DENABLE_CLDNN=OFF CMake build option and skip the installation of the Intel® Graphics Driver.

3. Create build directory:

   mkdir build

4. In the build directory, run cmake to fetch project dependencies and generate a Visual Studio solution.

   For Microsoft* Visual Studio 2019 x64 architecture:

cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_BUILD_TYPE=Release ..

For Microsoft* Visual Studio 2019 ARM architecture:

cmake -G "Visual Studio 16 2019" -A ARM -DCMAKE_BUILD_TYPE=Release ..

For Microsoft* Visual Studio 2019 ARM64 architecture:

cmake -G "Visual Studio 16 2019" -A ARM64 -DCMAKE_BUILD_TYPE=Release ..

5. Build generated solution in Visual Studio or run cmake --build . --config Release --verbose -j8 to build from the command line.

6. Before running the samples, add paths to the TBB and OpenCV binaries used for the build to the %PATH% environment variable. By default, TBB binaries are downloaded by the CMake-based script to the <openvino_repo>/inference-engine/temp/tbb/bin folder, OpenCV binaries to the <openvino_repo>/inference-engine/temp/opencv_4.5.0/opencv/bin folder.

Additional Build Options

• Internal JIT GEMM implementation is used by default.

• Threading Building Blocks (TBB) is used by default. To build the Inference Engine with OpenMP* threading, set the -DTHREADING=OMP option.

• Required versions of TBB and OpenCV packages are downloaded automatically by the CMake-based script. If you want to use the automatically-downloaded packages but you already have installed TBB or OpenCV packages configured in your environment, you may need to clean the TBBROOT and OpenCV_DIR environment variables before running the cmake command; otherwise they won't be downloaded and the build may fail if incompatible versions were installed.

• If the CMake-based build script can not find and download the OpenCV package that is supported on your platform, or if you want to use a custom build of the OpenCV library, refer to the Use Custom OpenCV Builds section for details.

• To switch off/on the CPU and GPU plugins, use the cmake options -DENABLE_MKL_DNN=ON/OFF and -DENABLE_CLDNN=ON/OFF respectively.

• To build the Python API wrapper, use the -DENABLE_PYTHON=ON option. To specify an exact Python version, use the following options:

  -DPYTHON_EXECUTABLE="C:\Program Files\Python37\python.exe" ^
  -DPYTHON_LIBRARY="C:\Program Files\Python37\libs\python37.lib" ^
  -DPYTHON_INCLUDE_DIR="C:\Program Files\Python37\include"

• nGraph-specific compilation options: -DNGRAPH_ONNX_IMPORT_ENABLE=ON enables the building of the nGraph ONNX importer. -DNGRAPH_DEBUG_ENABLE=ON enables additional debug prints.

Building Inference Engine with Ninja* Build System

call "C:\Program Files (x86)\Microsoft Visual Studio\2019\Professional\VC\Auxiliary\Build\vcvars64.bat"
cmake -G Ninja -Wno-dev -DCMAKE_BUILD_TYPE=Release ..
cmake --build . --config Release