-## Repository components
-
+# Build Inference Engine
+
+## Contents
+
+- [Introduction](#introduction)
+- [Build on Linux* Systems](#build-on-linux-systems)
+ - [Software Requirements](#software-requirements)
+ - [Build Steps](#build-steps)
+ - [Additional Build Options](#additional-build-options)
+- [Build for Raspbian* Stretch OS](#build-for-raspbian-stretch-os)
+ - [Hardware Requirements](#hardware-requirements)
+ - [Native Compilation](#native-compilation)
+ - [Cross Compilation Using Docker*](#cross-compilation-using-docker)
+ - [Additional Build Options](#additional-build-options-1)
+- [Build on Windows* Systems](#build-on-windows-systems)
+ - [Software Requirements](#software-requirements-1)
+ - [Build Steps](#build-steps-1)
+ - [Additional Build Options](#additional-build-options-2)
+ - [Building Inference Engine with Ninja* Build System](#building-inference-engine-with-ninja-build-system)
+- [Build on macOS* Systems](#build-on-macos-systems)
+ - [Software Requirements](#software-requirements-2)
+ - [Build Steps](#build-steps-2)
+ - [Additional Build Options](#additional-build-options-3)
+- [Use Custom OpenCV Builds for Inference Engine](#use-custom-opencv-builds-for-inference-engine)
+- [(Optional) Additional Installation Steps for the Intel® Movidius™ Neural Compute Stick and Neural Compute Stick 2](#optional-additional-installation-steps-for-the-intel-movidius-neural-compute-stick-and-neural-compute-stick-2)
+ - [For Linux, Raspbian Stretch* OS](#for-linux-raspbian-stretch-os)
+ - [For Windows](#for-windows-1)
+- [Next Steps](#next-steps)
+- [Additional Resources](#additional-resources)
+
+## Introduction
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:
| 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 |
+| MYRIAD plugin | Intel® Movidius™ Neural Compute Stick powered by the Intel® Movidius™ Myriad™ 2, Intel® Neural Compute Stick 2 powered by the Intel® Movidius™ Myriad™ X |
| 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™](https://software.intel.com/en-us/openvino-toolkit).
+Inference Engine plugin for Intel® FPGA is distributed only in a binary form as a part of [Intel® Distribution of OpenVINO™](https://software.intel.com/en-us/openvino-toolkit).
-## Build on Linux\* Systems
+## Build on Linux* Systems
The software was validated on:
- Ubuntu\* 16.04 (64-bit) with default GCC\* 5.4.0
- CentOS\* 7.4 (64-bit) with default GCC\* 4.8.5
-- [Intel® Graphics Compute Runtime for OpenCL™ Driver package 18.28.11080](https://github.com/intel/compute-runtime/releases/tag/18.28.11080).
### Software Requirements
-- [CMake\*](https://cmake.org/download/) 3.9 or higher
+- [CMake\*](https://cmake.org/download/) 3.5 or higher
- GCC\* 4.8 or higher to build the Inference Engine
- Python 2.7 or higher for Inference Engine Python API wrapper
+- (Optional) [Install Intel® Graphics Compute Runtime for OpenCL™ Driver package 19.04.12237](https://github.com/intel/compute-runtime/releases/tag/19.04.12237).
### Build Steps
1. Clone submodules:
git submodule update --recursive
```
2. Install build dependencies using the `install_dependencies.sh` script in the project root folder.
-3. Create a build folder:
+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 19.04.12237](https://github.com/intel/compute-runtime/releases/tag/19.04.12237) before running the build. If you don't want to use the GPU plugin, use the `-DENABLE_CLDNN=ON` CMake build option and skip the installation of the Intel® Graphics Compute Runtime for OpenCL™ Driver.
+4. Create a build folder:
```sh
- mkdir build
+ mkdir build && cd build
```
-4. 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:
+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:
```sh
cmake -DCMAKE_BUILD_TYPE=Release ..
- make -j16
+ make --jobs=$(nproc --all)
```
+
+### Additional Build Options
+
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](https://github.com/intel/mkl-dnn/releases/download/v0.17/mklml_lnx_2019.0.1.20180928.tgz)
+- To switch to OpenBLAS\* implementation, use the `GEMM=OPENBLAS` option and `BLAS_INCLUDE_DIRS` and `BLAS_LIBRARIES` CMake options to specify path to the 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 from the [MKL-DNN repository](https://github.com/intel/mkl-dnn/releases/download/v0.17/mklml_lnx_2019.0.1.20180928.tgz).
- 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.
+- 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.
-- To build the Python API wrapper, use the `-DENABLE_PYTHON=ON` option. To specify an exact Python version, use the following options:
-```sh
- -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`.
+- 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](#use-custom-opencv-builds-for-inference-engine) section for details.
+- To build the Python API wrapper, use the `-DENABLE_PYTHON=ON` option. To specify an exact Python version, use the following options:
+ ```sh
+ -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 off/on the CPU and GPU plugins, use the `cmake` options `-DENABLE_MKL_DNN=ON/OFF` and `-DENABLE_CLDNN=ON/OFF` respectively.
+
5. Adding to your project
For CMake projects, set an environment variable `InferenceEngine_DIR`:
target_link_libraries(${PROJECT_NAME} ${InferenceEngine_LIBRARIES} dl)
```
-## Build on Windows\* Systems:
+## Build for Raspbian Stretch* OS
+
+> **NOTE**: Only the MYRIAD plugin is supported.
+
+### Hardware Requirements
+* Raspberry Pi\* 2 or 3 with Raspbian\* Stretch OS (32-bit). Check that it's CPU supports ARMv7 instruction set (`uname -m` command returns `armv7l`).
+
+ > **NOTE**: Despite the Raspberry Pi\* CPU is ARMv8, 32-bit OS detects ARMv7 CPU instruction set. The default `gcc` compiler applies ARMv6 architecture flag for compatibility with lower versions of boards. For more information, run the `gcc -Q --help=target` command and refer to the description of the `-march=` option.
+
+You can compile the Inference Engine for Raspberry Pi\* in one of the two ways:
+* [Native Compilation](#native-compilation), which is the simplest way, but time-consuming
+* [Cross Compilation Using Docker*](#cross-compilation-using-docker), which is the recommended way
+
+### Native Compilation
+Native compilation of the Inference Engine is the most straightforward solution. However, it might take at least one hour to complete on Raspberry Pi\* 3.
+
+1. Install dependencies:
+
+ ```bash
+ sudo apt-get update
+ sudo apt-get install -y git cmake libusb-1.0-0-dev
+ ```
+
+2. Go to the `inference-engine` directory of the cloned `dldt` repository:
+
+ ```bash
+ cd dldt/inference-engine
+ ```
+
+3. Initialize submodules:
+
+ ```bash
+ git submodule init
+ git submodule update --recursive
+ ```
+
+4. Create a build folder:
+
+ ```bash
+ mkdir build && cd build
+ ```
+
+5. Build the Inference Engine:
+
+ ```bash
+ cmake -DCMAKE_BUILD_TYPE=Release \
+ -DENABLE_SSE42=OFF \
+ -DTHREADING=SEQ \
+ -DENABLE_GNA=OFF .. && make -j2
+ ```
+
+### Cross Compilation Using Docker*
+
+ This compilation was tested on the following configuration:
+
+ * Host: Ubuntu\* 16.04 (64-bit, Intel® Core™ i7-6700K CPU @ 4.00GHz × 8)
+ * Target: Raspbian\* Stretch (32-bit, ARMv7, Raspberry Pi\* 3)
+
+1. Install Docker\*:
+
+ ```bash
+ sudo apt-get install -y docker.io
+ ```
+
+2. Add a current user to `docker` group:
+
+ ```bash
+ sudo usermod -a -G docker $USER
+ ```
+
+ Log out and log in for this to take effect.
+
+3. Create a directory named `ie_cross_armhf` and add a text file named `Dockerfile`
+with the following content:
+
+ ```docker
+ FROM debian:stretch
+
+ USER root
+
+ RUN dpkg --add-architecture armhf && \
+ apt-get update && \
+ apt-get install -y --no-install-recommends \
+ build-essential \
+ crossbuild-essential-armhf \
+ git \
+ wget \
+ libusb-1.0-0-dev:armhf \
+ libgtk-3-dev:armhf \
+ libavcodec-dev:armhf \
+ libavformat-dev:armhf \
+ libswscale-dev:armhf \
+ libgstreamer1.0-dev:armhf \
+ libgstreamer-plugins-base1.0-dev:armhf \
+ libpython3-dev:armhf \
+ python3-pip
+
+ RUN wget https://www.cmake.org/files/v3.14/cmake-3.14.3.tar.gz && \
+ tar xf cmake-3.14.3.tar.gz && \
+ (cd cmake-3.14.3 && ./bootstrap --parallel=$(nproc --all) && make --jobs=$(nproc --all) && make install) && \
+ rm -rf cmake-3.14.3 cmake-3.14.3.tar.gz
+
+ ```
+
+ It uses the Debian\* Stretch (Debian 9) OS for compilation because it is a base of the Raspbian\* Stretch.
+
+4. Build a Docker\* image:
+
+ ```bash
+ docker image build -t ie_cross_armhf ie_cross_armhf
+ ```
+
+5. Run Docker\* container with mounted source code folder from host:
+
+ ```bash
+ docker run -it -v /absolute/path/to/dldt:/dldt ie_cross_armhf /bin/bash
+ ```
+
+6. While in the container:
+
+ 1. Go to the `inference-engine` directory of the cloned `dldt` repository:
+
+ ```bash
+ cd dldt/inference-engine
+ ```
+
+ 2. Create a build folder:
+
+ ```bash
+ mkdir build && cd build
+ ```
+
+ 3. Build the Inference Engine:
+
+ ```bash
+ cmake -DCMAKE_BUILD_TYPE=Release \
+ -DCMAKE_TOOLCHAIN_FILE="../cmake/arm.toolchain.cmake" \
+ -DTHREADS_PTHREAD_ARG="-pthread" \
+ -DENABLE_SSE42=OFF \
+ -DTHREADING=SEQ \
+ -DENABLE_GNA=OFF .. && make --jobs=$(nproc --all)
+ ```
+
+7. Press "Ctrl"+"D" to exit from Docker\*. You can find the resulting binaries in the `dldt/inference-engine/bin/armv7l/` directory and the OpenCV* installation in the `dldt/inference-engine/temp`.
+
+>**NOTE**: Native applications that link to cross-compiled Inference Engine library require an extra compilation flag `-march=armv7-a`.
+
+### Additional Build Options
+
+You can use the following additional build options:
+
+- Required versions of OpenCV packages are downloaded automatically by the CMake-based script. If you want to use the automatically downloaded packages but you already have installed OpenCV packages configured in your environment, you may need to clean the `OpenCV_DIR` environment variable 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](#use-custom-opencv-builds-for-inference-engine) section for details.
+
+- To build Python API wrapper, install `libpython3-dev:armhf` and `python3-pip` packages using `apt-get`, then install `numpy` and `cython` python modules using `pip3` command and add the following cmake options:
+```sh
+ -DENABLE_PYTHON=ON \
+ -DPYTHON_EXECUTABLE=/usr/bin/python3.5 \
+ -DPYTHON_LIBRARY=/usr/lib/arm-linux-gnueabihf/libpython3.5m.so \
+ -DPYTHON_INCLUDE_DIR=/usr/include/python3.5
+```
+
+## Build on Windows* Systems
The software was validated on:
- Microsoft\* Windows\* 10 (64-bit) with Visual Studio 2017 and Intel® C++ Compiler 2018 Update 3
-- [Intel® Graphics Driver for Windows* [24.20] driver package](https://downloadcenter.intel.com/download/27803/Graphics-Intel-Graphics-Driver-for-Windows-10?v=t).
### Software Requirements
-- [CMake\*](https://cmake.org/download/) 3.9 or higher
+- [CMake\*](https://cmake.org/download/) 3.5 or higher
- [OpenBLAS\*](https://sourceforge.net/projects/openblas/files/v0.2.14/OpenBLAS-v0.2.14-Win64-int64.zip/download) and [mingw64\* runtime dependencies](https://sourceforge.net/projects/openblas/files/v0.2.14/mingw64_dll.zip/download).
- [Intel® C++ Compiler](https://software.intel.com/en-us/intel-parallel-studio-xe) 18.0 to build the Inference Engine on Windows.
+- (Optional) [Intel® Graphics Driver for Windows* [25.20] driver package](https://downloadcenter.intel.com/download/28646/Intel-Graphics-Windows-10-DCH-Drivers?product=80939).
- Python 3.4 or higher for Inference Engine Python API wrapper
### Build Steps
3. Install OpenBLAS:
1. Download [OpenBLAS\*](https://sourceforge.net/projects/openblas/files/v0.2.14/OpenBLAS-v0.2.14-Win64-int64.zip/download)
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:
+4. 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* [25.20] driver package](https://downloadcenter.intel.com/download/28646/Intel-Graphics-Windows-10-DCH-Drivers?product=80939) before running the build. If you don't want to use the GPU plugin, use the `-DENABLE_CLDNN=ON` CMake build option and skip the installation of the Intel® Graphics Driver.
+5. Create build directory:
```sh
mkdir build
```
-5. In the `build` directory, run `cmake` to fetch project dependencies and generate a Visual Studio solution:
+6. In the `build` directory, run `cmake` to fetch project dependencies and generate a Visual Studio solution:
```sh
cd build
cmake -G "Visual Studio 15 2017 Win64" -T "Intel C++ Compiler 18.0" ^
-DICCLIB="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\compiler\lib" ..
```
+7. Build generated solution in Visual Studio 2017 or run `cmake --build . --config Release` to build from the command line.
+
+8. 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.
+
+### Additional Build Options
+
- 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](https://sourceforge.net/projects/openblas/files/v0.2.14/OpenBLAS-v0.2.14-Win64-int64.zip/download), mingw64* runtime dependencies [here](https://sourceforge.net/projects/openblas/files/v0.2.14/mingw64_dll.zip/download)
-- 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](https://github.com/intel/mkl-dnn/releases/download/v0.17/mklml_win_2019.0.1.20180928.zip)
+- To switch to OpenBLAS GEMM implementation, use the `-DGEMM=OPENBLAS` CMake option and specify path to OpenBLAS using the `-DBLAS_INCLUDE_DIRS=<OPENBLAS_DIR>\include` and `-DBLAS_LIBRARIES=<OPENBLAS_DIR>\lib\libopenblas.dll.a` options. Prebuilt OpenBLAS\* package can be downloaded [here](https://sourceforge.net/projects/openblas/files/v0.2.14/OpenBLAS-v0.2.14-Win64-int64.zip/download). mingw64* runtime dependencies can be downloaded [here](https://sourceforge.net/projects/openblas/files/v0.2.14/mingw64_dll.zip/download).
+- To switch to the optimized MKL-ML\* GEMM implementation, use the `-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 [MKL-DNN repository](https://github.com/intel/mkl-dnn/releases/download/v0.17/mklml_win_2019.0.1.20180928.zip).
- 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.
+- 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.
-- To build the Python API wrapper, use the `-DENABLE_PYTHON=ON` option. To specify an exact Python version, use the following options:
-```sh
- -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"
-```
+- 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](#use-custom-opencv-builds-for-inference-engine) section for details.
-6. Build generated solution in Visual Studio 2017 or run `cmake --build . --config Release` to build from the command line.
+- To switch off/on the CPU and GPU plugins, use the `cmake` options `-DENABLE_MKL_DNN=ON/OFF` and `-DENABLE_CLDNN=ON/OFF` respectively.
-7. 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.
+- To build the Python API wrapper, use the `-DENABLE_PYTHON=ON` option. To specify an exact Python version, use the following options:
+ ```sh
+ -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"
+ ```
-### Building Inference Engine with Ninja
+### Building Inference Engine with Ninja* Build System
```sh
call "C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\bin\ipsxe-comp-vars.bat" intel64 vs2017
cmake --build . --config Release
```
-## Build on macOS\* Systems
+## Build on macOS* Systems
+
+> **NOTE**: The current version of the OpenVINO™ toolkit for macOS* supports inference on Intel CPUs only.
The software was validated on:
- macOS\* 10.14, 64-bit
### Software Requirements
-- [CMake\*](https://cmake.org/download/) 3.9 or higher
+- [CMake\*](https://cmake.org/download/) 3.5 or higher
- Clang\* compiler from Xcode\* 10.1
- Python\* 3.4 or higher for the Inference Engine Python API wrapper
4. 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:
```sh
cmake -DCMAKE_BUILD_TYPE=Release ..
- make -j16
+ make --jobs=$(nproc --all)
```
+### Additional Build Options
+
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](https://github.com/intel/mkl-dnn/releases/download/v0.17.1/mklml_mac_2019.0.1.20180928.tgz)
- 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](#use-custom-opencv-builds-for-inference-engine) section for details.
+
- To build the Python API wrapper, use the `-DENABLE_PYTHON=ON` option. To specify an exact Python version, use the following options:
```sh
-DPYTHON_EXECUTABLE=/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 \
-DPYTHON_INCLUDE_DIR=/Library/Frameworks/Python.framework/Versions/3.7/include/python3.7m
```
+## Use Custom OpenCV Builds for Inference Engine
+
+> **NOTE**: The recommended and tested version of OpenCV is 4.1. The minimum supported version is 3.4.0.
+
+Required versions of OpenCV packages are downloaded automatically during the building Inference Engine library. If the build script can not find and download the OpenCV package that is supported on your platform, you can use one of the following options:
+
+* Download the most suitable version from the list of available pre-build packages from [https://download.01.org/opencv/2019/openvinotoolkit](https://download.01.org/opencv/2019/openvinotoolkit) from the `<release_version>/inference_engine` directory.
+
+* Use a system provided OpenCV package (e.g with running the `apt install libopencv-dev` command). The following modules must be enabled: `imgcodecs`, `videoio`, `highgui`.
+
+* Get the OpenCV package using a package manager: pip, conda, conan etc. The package must have the development components included (header files and CMake scripts).
+
+* Build OpenCV from source using the [build instructions](https://docs.opencv.org/master/df/d65/tutorial_table_of_content_introduction.html) on the OpenCV site.
+
+After you got the built OpenCV library, perform the following preparation steps before running the Inference Engine build:
+
+1. Set the `OpenCV_DIR` environment variable to the directory where the `OpenCVConfig.cmake` file of you custom OpenCV build is located.
+2. Disable the package automatic downloading with using the `-DENABLE_OPENCV=OFF` option for CMake-based build script for Inference Engine.
+
+## (Optional) Additional Installation Steps for the Intel® Movidius™ Neural Compute Stick and Neural Compute Stick 2
+
+> **NOTE**: These steps are only required if you want to perform inference on Intel® Movidius™ Neural Compute Stick or the Intel® Neural Compute Stick 2 using the Inference Engine MYRIAD Plugin. See also [Intel® Neural Compute Stick 2 Get Started](https://software.intel.com/en-us/neural-compute-stick/get-started)
+
+### For Linux, Raspbian\* Stretch OS
+
+1. Add the current Linux user to the `users` group:
+```sh
+sudo usermod -a -G users "$(whoami)"
+```
+Log out and log in for it to take effect.
+
+2. To perform inference on Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2, install the USB rules as follows:
+```sh
+cat <<EOF > 97-myriad-usbboot.rules
+SUBSYSTEM=="usb", ATTRS{idProduct}=="2150", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
+SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
+SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
+EOF
+```
+```sh
+sudo cp 97-myriad-usbboot.rules /etc/udev/rules.d/
+```
+```sh
+sudo udevadm control --reload-rules
+```
+```sh
+sudo udevadm trigger
+```
+```sh
+sudo ldconfig
+```
+```sh
+rm 97-myriad-usbboot.rules
+```
+
+### For Windows
+
+For Intel® Movidius™ Neural Compute Stick and Intel® Neural Compute Stick 2, install the Movidius™ VSC driver:
+1. Go to the `<DLDT_ROOT_DIR>/inference-engine/thirdparty/movidius/MovidiusDriver` directory, where the `DLDT_ROOT_DIR` is the directory to which the DLDT repository was cloned.
+2. Right click on the `Movidius_VSC_Device.inf` file and choose **Install** from the pop up menu.
+
+You have installed the driver for your Intel® Movidius™ Neural Compute Stick or Intel® Neural Compute Stick 2.
+
+## Next Steps
+
+Congratulations, you have built the Inference Engine. To get started with the OpenVINO™ DLDT, proceed to the Get Started guides:
+
+* [Get Started with Deep Learning Deployment Toolkit on Linux*](../get-started-linux.md)
+
+## Additional Resources
+
+* [OpenVINO™ Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
+* [Introduction to Intel® Deep Learning Deployment Toolkit](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Introduction.html)
+* [Inference Engine Samples Overview](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Samples_Overview.html)
+* [Inference Engine Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_IE_DG_Deep_Learning_Inference_Engine_DevGuide.html)
+* [Model Optimizer Developer Guide](https://docs.openvinotoolkit.org/latest/_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)
---
-\* Other names and brands may be claimed as the property of others.
+\* Other names and brands may be claimed as the property of others.
\ No newline at end of file
projects / platform / upstream / dldt.git / blobdiff