## Build on Linux\* Systems
The software was validated on:
-- Ubuntu\* 16.04 with default GCC\* 5.4.0
-- CentOS\* 7.4 with default GCC\* 4.8.5
+- 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
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 optimized MKL-ML\* GEMM implementation, use `GEMM=MKL` and `MKLROOT` cmake options to specify path to unpacked MKL-ML with `include` and `lib` folders, for example use the following options: `-DGEMM=MKL -DMKLROOT=<path_to_MKL>`. MKL-ML\* package can be downloaded [here](https://github.com/intel/mkl-dnn/releases/download/v0.17/mklml_lnx_2019.0.1.20180928.tgz)
-- OpenMP threading is used by default. To build Inference Engine with TBB threading, set `-DTHREADING=TBB` option.
+- 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 build Python API wrapper, use -DENABLE_PYTHON=ON option. To specify exact Python version, use the following options: `-DPYTHON_EXECUTABLE=`which python3.6` -DPYTHON_LIBRARY=/usr/lib/x86_64-linux-gnu/libpython3.6m.so -DPYTHON_INCLUDE_DIR=/usr/include/python3.6`
+- 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:
+```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`.
## Build on Windows\* Systems:
The software was validated on:
-- Microsoft\* Windows\* 10 with Visual Studio 2017 and Intel® C++ Compiler 2018 Update 3
+- 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
- 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 optimized MKL-ML GEMM implementation, use `GEMM=MKL` and `MKLROOT` cmake options to specify path to unpacked MKL-ML with `include` and `lib` folders, for example use the following options: `-DGEMM=MKL -DMKLROOT=<path_to_MKL>`. 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 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)
+
+- Threading Building Blocks (TBB) is used by default. To build the Inference Engine with OpenMP* threading, set the `-DTHREADING=OMP` option.
-- OpenMP threading is used by default. To build Inference Engine with TBB threading, set `-DTHREADING=TBB` 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 Python API wrapper, use -DENABLE_PYTHON=ON option. To specify exact Python version, use the following options: `-DPYTHON_EXECUTABLE="C:\Program Files\Python36\python.exe" -DPYTHON_INCLUDE_DIR="C:\Program Files\Python36\include" -DPYTHON_LIBRARY="C:\Program Files\Python36\libs\python36.lib"`.
+- 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"
+```
6. Build generated solution in Visual Studio 2017 or run `cmake --build . --config Release` to build from the command line.
+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.
+
### Building Inference Engine with Ninja
```sh
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
```
-Before running the samples on Microsoft\* Windows\*, please add path to OpenMP library (<dldt_repo>/inference-engine/temp/omp/lib) and OpenCV libraries (<dldt_repo>/inference-engine/temp/opencv_4.0.0/bin) to the %PATH% environment variable.
+## Build on macOS\* Systems
+
+The software was validated on:
+- macOS\* 10.14, 64-bit
+
+### Software Requirements
+- [CMake\*](https://cmake.org/download/) 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:
+ ```sh
+ 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:
+```sh
+ mkdir 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:
+```sh
+ 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](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.
+
+- 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_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.
projects / platform / upstream / dldt.git / blobdiff