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The Inference Engine can infer models in different formats with various input and output formats.

The open source version of Inference Engine includes the following plugins:

PLUGIN DEVICE TYPES
CPU plugin Intel® Xeon® with Intel® AVX2 and AVX512, Intel® Core™ Processors with Intel® AVX2, Intel® Atom® Processors with Intel® SSE
GPU plugin Intel® Processor Graphics, including Intel® HD Graphics and Intel® Iris® Graphics
GNA plugin Intel® Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel® Pentium® Silver processor J5005, Intel® Celeron® processor J4005, Intel® Core™ i3-8121U processor
Heterogeneous plugin Heterogeneous plugin enables computing for inference on one network on several Intel® devices.

Inference Engine plugins for Intel® FPGA and Intel® Movidius™ Neural Compute Stick are distributed only in a binary form as a part of Intel® Distribution of OpenVINO™.

Build on Linux* Systems

The software was validated on:

Software Requirements

  • CMake* 3.9 or higher
  • GCC* 4.8 or higher to build the Inference Engine
  • Python 2.7 or higher for Inference Engine Python API wrapper

Build Steps

  1. Clone submodules: 
    cd dldt/inference-engine
git submodule init
git submodule update --recursive
  2. Install build dependencies using the install_dependencies.sh script in the project root folder.
  3. Create a build folder:
  mkdir build
  1. 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 -j16

You can use the following additional build options:

  • Internal JIT GEMM implementation is used by default.

  • To switch to OpenBLAS* implementation, use GEMM=OPENBLAS option and BLAS_INCLUDE_DIRS and BLAS_LIBRARIES cmake options to specify path to OpenBLAS headers and library, for example use 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 here

  • 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 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.

  • To build the Python API wrapper, 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 on/off the CPU and GPU plugins, use cmake options -DENABLE_MKL_DNN=ON/OFF and -DENABLE_CLDNN=ON/OFF.

  1. Adding to your project

    For CMake projects, set an environment variable InferenceEngine_DIR:

    export InferenceEngine_DIR=/path/to/dldt/inference-engine/build/

    Then you can find Inference Engine by find_package:

    find_package(InferenceEngine)

include_directories(${InferenceEngine_INCLUDE_DIRS})

target_link_libraries(${PROJECT_NAME} ${InferenceEngine_LIBRARIES} dl)

Build on Windows* Systems:

The software was validated on:

Software Requirements

Build Steps

  1. Clone submodules: 
    git submodule init
git submodule update --recursive
  2. Download and install Intel® C++ Compiler 18.0
  3. Install OpenBLAS:
    1. Download OpenBLAS*
    2. Unzip the downloaded package to a directory on your machine. In this document, this directory is referred to as <OPENBLAS_DIR>.
  4. Create build directory: 
    mkdir build
  5. In the build directory, run cmake to fetch project dependencies and generate a Visual Studio solution:
cd build
cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
    -DCMAKE_BUILD_TYPE=Release ^
    -DICCLIB="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\compiler\lib" ..

  • Internal JIT GEMM implementation is used by default.

  • To switch to OpenBLAS GEMM implementation, use -DGEMM=OPENBLAS cmake option and specify path to OpenBLAS using -DBLAS_INCLUDE_DIRS=<OPENBLAS_DIR>\include and -DBLAS_LIBRARIES=<OPENBLAS_DIR>\lib\libopenblas.dll.a options. Prebuilt OpenBLAS* package can be downloaded here, mingw64* runtime dependencies here

  • 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 here

  • 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 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.

  • 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"

  1. Build generated solution in Visual Studio 2017 or run cmake --build . --config Release to build from the command line.

  2. Before running the samples, add paths to 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 <dldt_repo>/inference-engine/temp/tbb/lib folder, OpenCV binaries - to the <dldt_repo>/inference-engine/temp/opencv_4.1.0/bin folder.

Building Inference Engine with Ninja

call "C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\bin\ipsxe-comp-vars.bat" intel64 vs2017
set CXX=icl
set CC=icl
:: clean TBBROOT value set by ipsxe-comp-vars.bat, required TBB package will be downloaded by dldt cmake script
set TBBROOT=
cmake -G Ninja -Wno-dev -DCMAKE_BUILD_TYPE=Release ..
cmake --build . --config Release

Build on macOS* Systems

The software was validated on:

  • macOS* 10.14, 64-bit

Software Requirements

  • CMake* 3.9 or higher
  • Clang* compiler from Xcode* 10.1
  • Python* 3.4 or higher for the Inference Engine Python API wrapper

Build Steps

  1. Clone submodules: 
    cd dldt/inference-engine
git submodule init
git submodule update --recursive
  2. Install build dependencies using the install_dependencies.sh script in the project root folder.
  3. Create a build folder:
  mkdir build
  1. 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 -j16

You can use the following additional build options:

  • Internal JIT GEMM implementation is used by default.

  • 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 here

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

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

  -DPYTHON_EXECUTABLE=/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 \
  -DPYTHON_LIBRARY=/Library/Frameworks/Python.framework/Versions/3.7/lib/libpython3.7m.dylib \
  -DPYTHON_INCLUDE_DIR=/Library/Frameworks/Python.framework/Versions/3.7/include/python3.7m

* Other names and brands may be claimed as the property of others.