Skip to content
/ clDNN Public
forked from intel/clDNN

Compute Library for Deep Neural Networks (clDNN)

01.org/cldnn
0 stars 115 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

jjasoliya/clDNN

BranchesTags
 
 

Repository files navigation

Compute Library for Deep Neural Networks (clDNN)

Apache License Version 2.0 v1.0

Compute Library for Deep Neural Networks (clDNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL Inference on Intel® Processor Graphics – including HD Graphics and Iris® Graphics.
clDNN includes highly optimized building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. We created this project to enable the DL community to innovate on Intel® processors.

Usages supported: Image recognition, image detection, and image segmentation.

Validated Topologies: AlexNet*, VGG(16,19)*, GoogleNet(v1,v2,v3)*, ResNet(50,101,152)* Faster R-CNN*, Squeezenet*, SSD_googlenet*, SSD_VGG*, PVANET*, PVANET_REID*, age_gender*, FCN* and yolo*.

As with any technical preview, APIs may change in future updates.

License

clDNN is licensed is licensed under Apache License Version 2.0.

Attached licenses

clDNN uses 3rd-party components licensed under following licenses:

Documentation

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.

Changelog

Drop 4.0

New features:
- OOOQ execution model implementation
- depthwise separable convolution implementation
- kernel auto-tuner implementation
Bug fixes:
- dump hidden layer fix
- run single layer fix
- reshape fix
UX:
- enable RTTI
- better error handling/reporting
Performance:
- lrn optimization
- dynamic pruning for sparse fc layers
- reorder optimization
- concatenation optimization
- eltwise optimization
- activation fusing

Drop 3.0

Added:
- kernel selector
- custom layer
Changed:
- performance improvments
- bug fixes (deconvolution, softmax, reshape)
- apply fixes from community reported issues

Drop 2.0

Added:
- step by step tutorial
Changed:
- perfomance optimization for: softmax, fully connected, eltwise, reshape
- bug fixes (conformance)

Drop 1.0

- initial drop of clDNN

Support

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 via GitHub 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 modifications are necessary, may provide feedback to guide you. When accepted, your pull request will be merged into our internal and GitHub repositories.

System Requirements

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
  • Codename Kabylake:
    • Intel® HD Graphics 610 (GT1, client market)
    • Intel® HD Graphics 615 (GT2, client market)
    • Intel® HD Graphics 620 (GT2, client market)
    • Intel® HD Graphics 630 (GT2, client market)
    • Intel® Iris® Graphics 640 (GT3e, client market)
    • Intel® Iris® Graphics 650 (GT3e, client market)
    • Intel® HD Graphics P630 (GT2, server market)
    • Intel® Iris® Pro Graphics 630 (GT2, server market)

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 resolution in 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 (<immintrin.h>) 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 docs custom target which will generate it automatically.

GraphViz* (2.38 or later) is also recommended to generate documentation with all embedded diagrams.
(Make sure that dot application is visible in the PATH environment variable.)

We recommend to use SRB5.0 for Linux and 15.46 driver for Windows.

Installation

Building

Download clDNN source code or 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 cmd (or powershell):

    @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:

    @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 (they will generate solutions/makefiles in build subdirectory and binary outputs will be written to build/out subdirectory):

  • create_msvc_mscc.bat (Windows*, Visual Studio* 2015)
  • create_unixmake_gcc.sh [Y|N] [<devtoolset-version>] (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 devtoolset version.

CMake solution offers multiple options which you can specify using normal CMake syntax (-D<option-name>=<value>):

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: Debug (default), 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: Windows32, Windows64, 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/<arch-target>/<build-type> 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.
CLDNN__COMPILE_LINK_ALLOW_UNSAFE_SIZE_OPT BOOL Allow unsafe optimizations during linking (like aggressive dead code elimination, etc.). Default: ON
CLDNN__COMPILE_LINK_USE_STATIC_RUNTIME BOOL Link with static C++ runtime. Default: OFF (shared C++ runtime is used)
CLDNN__INCLUDE_CORE BOOL Include core clDNN library project in generated makefiles/solutions. Default: ON
CLDNN__INCLUDE_TESTS BOOL Include tests application project (based on googletest framework) in generated makefiles/solutions . Default: ON
CLDNN__RUN_TESTS BOOL Run tests after building tests project. This option requires CLDNN__INCLUDE_TESTS option to be ON. Default: OFF
CLDNN__CMAKE_DEBUG BOOL Enable extended debug messages in CMake. Default: OFF

clDNN includes unit tests implemented using the googletest framework. To validate your build, run tests target, e.g.:

    make tests

(Make sure that both CLDNN__INCLUDE_TESTS and CLDNN__RUN_TESTS were set to ON when invoking CMake.)

Generating documentation

Documentation is provided inline and can be generated in HTML format with Doxygen. We recommend to use latest Doxygen* and GraphViz*.

Documentation templates and configuration files are stored in docs subdirectory. You can simply call:

    cd docs && doxygen

to generate HTML documentation in docs/html subdirectory.

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 docs

in order to create it.

Deployment

Special install target will place the API header files and libraries in /usr/local (C:/Program Files/clDNN or C:/Program Files (x86)/clDNN on Windows). To change the installation path, use the option -DCMAKE_INSTALL_PREFIX=<prefix> when invoking CMake.

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

Copyright © 2017, Intel® Corporation

About

Compute Library for Deep Neural Networks (clDNN)

01.org/cldnn

Resources

Readme
Activity

Stars

0 stars

Watchers

2 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • C++ 94.5%
  • C 4.3%
  • CMake 1.1%
  • Python 0.1%
  • Objective-C 0.0%
  • Shell 0.0%