git clone 01org-clDNN_-_2017-05-24_11-20-05.bundle -b master
Compute Library for Deep Neural Networks (clDNN)
Compute Library for Deep Neural Networks (clDNN)
Compute Library for Deep Neural Networks (clDNN) is an open source performancelibrary for Deep Learning (DL) applications intended for acceleration ofDL Inference on Intel® Processor Graphics – including HD Graphics andIris® Graphics.
clDNN includes highly optimized building blocks for implementation ofconvolutional neural networks (CNN) with C and C++ interfaces. We createdthis project to enable the DL community to innovate on Intel® processors.
Usages supported: Image recognition, image detection, and image segmentation.
Topologies: AlexNet*, VGG*, GoogleNet* and ResNet*.
As with any technical preview, APIs may change in future updates.
clDNN is licensed is licensed underApache License Version 2.0.
clDNN uses 3rd-party components licensed under following licenses:- boost under Boost* Software License - Version 1.0- googletest under Google* License- OpenCL™ ICD and C++ Wrapper under Khronos™ License
The latest clDNN documentation is at GitHub pages.
There is also inline documentation available that can be generated with Doxygen.
Accelerate Deep Learning Inference with Intel® Processor Graphics whitepaper link.
Please report issues and suggestions GitHub issues.
How to Contribute
We welcome community contributions to clDNN. If you have an idea how to improve the library:
- Share your proposal viaGitHub issues
- Ensure you can build the product and run all the examples with your patch
- In the case of a larger feature, create a test
- Submit a pull request
We will review your contribution and, if any additional fixes or modificationsare necessary, may provide feedback to guide you. When accepted, your pullrequest will be merged into our internal and GitHub repositories.
clDNN supports Intel® HD Graphics and Intel® Iris® Graphics and is optimized for- Codename Skylake: * Intel® HD Graphics 510 (GT1, client market) * Intel® HD Graphics 515 (GT2, client market) * Intel® HD Graphics 520 (GT2, client market) * Intel® HD Graphics 530 (GT2, client market) * Intel® Iris® Graphics 540 (GT3e, client market) * Intel® Iris® Graphics 550 (GT3e, client market) * Intel® Iris® Pro Graphics 580 (GT4e, client market) * Intel® HD Graphics P530 (GT2, server market) * Intel® Iris® Pro Graphics P555 (GT3e, server market) * Intel® Iris® Pro Graphics P580 (GT4e, server market)- Codename Apollolake: * Intel® HD Graphics 500 * Intel® HD Graphics 505
clDNN currently uses OpenCL™ with multiple Intel® OpenCL™ extensions and requires Intel® Graphics Driver to run.
clDNN requires CPU with Intel® SSE/Intel® AVX support.
The software dependencies are:- CMake* 3.6 or later
(the project is compatible with CMake 3.1, but, due to issues with boost libraries resolutionin CMake 3.4.3 and with CheckCXXCompilerFlag module in CMake 3.5.2, we strongly recommend 3.6+)- C++ compiler with partiall or full C++14 standard support compatible with: * GNU* Compiler Collection 5.2 or later * clang 3.5 or later * Intel® C++ Compiler 17.0 or later * Visual C++ 2015 (MSVC++ 19.0) or later
Intel® CPU intrinsics header (
) must be available during compilation.
- python™ 2.7 or later (scripts are both compatible with python™ 2.7.x and python™ 3.x)
- (optional) Doxygen* 1.8.13 or later
Needed for manual generation of documentation from inline comments or running
docscustom target which will generate it automatically.
GraphViz* (2.38 or later) is also recommended to generate documentation with all embedded diagrams.
(Make sure that
dotapplication is visible in the
The software was validated on:- CentOS* 7 with GNU* Compiler Collection 5.2 (64-bit only)- Windows® 10 and Windows® Server 2012 R2 with MSVC 14.0
We have validated using Intel® intel-opencl-r4.1 (SRB4.1) Linux driver package.
Download clDNN source codeor clone the repository to your system:
git clone https://github.com/01org/cldnn.git
Satisfy all software dependencies and ensure that the versions are correct before building.
clDNN uses multiple 3rd-party components. They are stored in binary form in
common subdirectory. Currently they are prepared for MSVC++ and GCC*. They will be cloned with repository.
clDNN uses a CMake-based build system. You can use CMake command-line tool or CMake GUI (
cmake-gui) to generate required solution.
For Windows system, you can call in
shellscript @REM Generate 32-bit solution (solution contains multiple build configurations)... cmake -E make_directory build && cd build && cmake -G "Visual Studio 14 2015" .. @REM Generate 64-bit solution (solution contains multiple build configurations)... cmake -E make_directory build && cd build && cmake -G "Visual Studio 14 2015 Win64" ..
Created solution can be opened in Visual Studio 2015 or built using appropriate
msbuild tool(you can also use
cmake --build . to select build tool automatically).
For Unix and Linux systems:
shellscript @REM Create GNU makefile for release clDNN and build it... cmake -E make_directory build && cd build && cmake -DCMAKE_BUILD_TYPE=Release .. && make @REM Create Ninja makefile for debug clDNN and build it... cmake -E make_directory build && cd build && cmake -G Ninja -DCMAKE_BUILD_TYPE=Debug .. && ninja -k 20
You can call also scripts in main directory of project which will create solutions/makefiles for clDNN (theywill generate solutions/makefiles in
build subdirectory and binary outputs will be written to
create_msvc_mscc.bat (Windows*, Visual Studio* 2015)-
create_unixmake_gcc.sh [Y|N]  (Linux*, GNU* or Ninja* makefiles, optional devtoolset support) * If you specify the first parameter as
Y, the Ninja makefiles will be generated. * If you specify second parameter (number), the CMake will be called via
scl with selected
CMake solution offers multiple options which you can specify using normal CMake syntax (
| CMake option | Type | Description ||:------------------------------------------|:---------|:-----------------------------------------------------------------------------|| CMAKE_BUILD_TYPE | STRING | Build configuration that will be used by generated makefiles (it does not affect multi-configuration generators like generators for Visual Studio solutions). Currently supported:
Release || CMAKE_INSTALL_PREFIX | PATH | Install directory prefix. || CLDNN__ARCHITECTURE_TARGET | STRING | Architecture of target system (where binary output will be deployed). CMake will try to detect it automatically (based on selected generator type, host OS and compiler properties). Specify this option only if CMake has problem with detection. Currently supported:
Linux64 || CLDNN__OUTPUT_DIR (CLDNN__OUTPUT_BIN_DIR, CLDNN__OUTPUT_LIB_DIR) | PATH | Location where built artifacts will be written to. It is set automatically to roughly
build/out// subdirectory. For more control use:
CLDNN__OUTPUT_LIB_DIR (specifies output path for static libraries) or
CLDNN__OUTPUT_BIN_DIR (for shared libs and executables). || | | || CMake advanced option | Type | Description || PYTHON_EXECUTABLE | FILEPATH | Path to Python interpreter. CMake will try to detect Python. Specify this option only if CMake has problem with locating Python. || CLDNN__BOOST_VERSION | STRING | Version of boost prebuilded binaries to use (from
common subdirectory). It is automatically setected by CMake (highest version). Specify, if you have multiple versions and want to use different than automatically selected. || CLDNN__IOCL_ICD_USE_EXTERNAL | BOOL | Use this option to enable use of external Intel® OpenCL™ SDK as a source for ICD binaries and headers (based on
INTELOCLSDKROOT environment variable). Default:
OFF || CLDNN__IOCL_ICD_VERSION | STRING | Version of Intel® OpenCL™ ICD binaries and headers to use (from
common subdirectory). It is automatically setected by CMake (highest version). Specify, if you have multiple versions and want to use different than automatically selected. || | | || CLDNNCOMPILELINKALLOWUNSAFESIZEOPT | BOOL | Allow unsafe optimizations during linking (like aggressive dead code elimination, etc.). Default:
ON || CLDNNCOMPILELINKUSESTATICRUNTIME | BOOL | Link with static C++ runtime. Default:
OFF (shared C++ runtime is used) || | | || CLDNNINCLUDECORE | BOOL | Include core clDNN library project in generated makefiles/solutions. Default:
ON || CLDNNINCLUDETESTS | BOOL | Include tests application project (based on googletest framework) in generated makefiles/solutions . Default:
ON || | | || CLDNNRUNTESTS | BOOL | Run tests after building
tests project. This option requires
CLDNN__INCLUDE_TESTS option to be
OFF || | | || CLDNNCMAKE_DEBUG | BOOL | Enable extended debug messages in CMake. Default:
clDNN includes unit tests implemented using the googletest framework. To validate your build, run
tests target, e.g.:
(Make sure that both
CLDNN__RUN_TESTS were set to
ON when invoking CMake.)
Documentation templates and configuration files are stored in
docs subdirectory. You can simply call:
shellscript cd docs && doxygento generate HTML documentation in
There is also custom CMake target named
docs which will generate documentation in
CLDNN__OUTPUT_BIN_DIR/html directory. For example, when using Unix makefiles, you can run:
make docsin order to create it.
install target will place the API header files and libraries in
C:/Program Files/clDNN or
C:/Program Files (x86)/clDNN on Windows). To changethe installation path, use the option
-DCMAKE_INSTALL_PREFIX= when invoking CMake.
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Intel® technologies may require enabled hardware, specific software, or services activation. Check with your system manufacturer or retailer.
The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request.
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Copyright © 2017, Intel® Corporation
Upload date: 2017-05-24
- 2017-05-25 18:02:48
- 2017-05-24 11:20:05
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