BuildingForLinux

Build on Linux* Systems

The software was validated on:

• Ubuntu* 18.04 (64-bit) with default GCC* 7.5.0
• Ubuntu* 20.04 (64-bit) with default GCC* 9.3.0
• CentOS* 7.6 (64-bit) with default GCC* 4.8.5

Table of content:

• Software Requirements
• Build Steps
• Additional Build Options

Software Requirements

• [CMake]* 3.13 or higher
• GCC* 4.8 or higher to build the Inference Engine
• Python 3.6 or higher for Inference Engine Python API wrapper
• (Optional) [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441].

NOTE: Building samples and demos from the Intel® Distribution of OpenVINO™ toolkit package requires CMake* 3.10 or higher.

Build Steps

1. Clone submodules:

   cd openvino
   git submodule update --init --recursive

2. Install build dependencies using the install_build_dependencies.sh script in the project root folder.

   chmod +x install_build_dependencies.sh

   ./install_build_dependencies.sh

3. By default, the build enables the Inference Engine GPU plugin to infer models on your Intel® Processor Graphics. This requires you to [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.41.14441] 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 Compute Runtime for OpenCL™ Driver.
4. Create a build folder:

  mkdir build && cd build

5. Inference Engine uses a CMake-based build system. In the created build directory, run cmake to fetch project dependencies and create Unix makefiles, then run make to build the project:

  cmake -DCMAKE_BUILD_TYPE=Release ..
  make --jobs=$(nproc --all)

Additional Build Options

You can use the following additional build options:

• The default build uses an internal JIT GEMM implementation.

• To switch to an OpenBLAS* implementation, use the GEMM=OPENBLAS option with BLAS_INCLUDE_DIRS and BLAS_LIBRARIES CMake options to specify a path to the OpenBLAS headers and library. For example, the following options on CentOS*: -DGEMM=OPENBLAS -DBLAS_INCLUDE_DIRS=/usr/include/openblas -DBLAS_LIBRARIES=/usr/lib64/libopenblas.so.0.

• To switch to the optimized MKL-ML* GEMM implementation, use -DGEMM=MKL and -DMKLROOT=<path_to_MKL> CMake options to specify a path to unpacked MKL-ML with the include and lib folders. MKL-ML* package can be downloaded from the Intel® [MKL-DNN repository].

• 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 will not 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 build the Python API wrapper:

  1. Install all additional packages listed in the /inference-engine/ie_bridges/python/requirements.txt file:

     pip install -r requirements.txt

  2. Use the -DENABLE_PYTHON=ON option. To specify an exact Python version, use the following options:

     -DPYTHON_EXECUTABLE=`which python3.7` \
     -DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.7m.so \
     -DPYTHON_INCLUDE_DIR=/usr/include/python3.7
     

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

• 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.