LightGBM/CMakeLists.txt

269 строки
9.2 KiB
CMake
Исходник Обычный вид История

if(USE_GPU OR APPLE)
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cmake_minimum_required(VERSION 3.2)
else()
cmake_minimum_required(VERSION 2.8)
endif()
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PROJECT(lightgbm)
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OPTION(USE_MPI "Enable MPI-based parallel learning" OFF)
OPTION(USE_OPENMP "Enable OpenMP" ON)
OPTION(USE_GPU "Enable GPU-accelerated training" OFF)
OPTION(USE_SWIG "Enable SWIG to generate Java API" OFF)
OPTION(USE_HDFS "Enable HDFS support (EXPERIMENTAL)" OFF)
OPTION(USE_R35 "Set to ON if your R version is not earlier than 3.5" OFF)
if(APPLE)
OPTION(APPLE_OUTPUT_DYLIB "Output dylib shared library" OFF)
endif(APPLE)
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if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "4.8.2")
message(FATAL_ERROR "Insufficient gcc version")
endif()
elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "3.8")
message(FATAL_ERROR "Insufficient Clang version")
endif()
elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang")
if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS "8.1.0")
message(FATAL_ERROR "Insufficient AppleClang version")
endif()
cmake_minimum_required(VERSION 3.16)
elseif(MSVC)
if(MSVC_VERSION LESS 1900)
message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} doesn't support required C++11 features. Please use a newer MSVC.")
endif()
cmake_minimum_required(VERSION 3.8)
endif()
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if(USE_SWIG)
find_package(SWIG REQUIRED)
find_package(Java REQUIRED)
find_package(JNI REQUIRED)
include(UseJava)
include(UseSWIG)
set(SWIG_CXX_EXTENSION "cxx")
set(SWIG_EXTRA_LIBRARIES "")
set(SWIG_JAVA_EXTRA_FILE_EXTENSIONS ".java" "JNI.java")
set(SWIG_MODULE_JAVA_LANGUAGE "JAVA")
set(SWIG_MODULE_JAVA_SWIG_LANGUAGE_FLAG "java")
set(CMAKE_SWIG_OUTDIR "${CMAKE_CURRENT_BINARY_DIR}/java")
include_directories(Java_INCLUDE_DIRS)
include_directories(JNI_INCLUDE_DIRS)
include_directories($ENV{JAVA_HOME}/include)
if(WIN32)
FILE(MAKE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/windows/x86_64")
include_directories($ENV{JAVA_HOME}/include/win32)
elseif(APPLE)
FILE(MAKE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/osx/x86_64")
include_directories($ENV{JAVA_HOME}/include/darwin)
else()
FILE(MAKE_DIRECTORY "${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/linux/x86_64")
include_directories($ENV{JAVA_HOME}/include/linux)
endif()
endif(USE_SWIG)
if(USE_R35)
ADD_DEFINITIONS(-DR_VER_ABOVE_35)
endif(USE_R35)
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if(USE_MPI)
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find_package(MPI REQUIRED)
ADD_DEFINITIONS(-DUSE_MPI)
MESSAGE(STATUS "MPI libraries: " ${MPI_LIBRARIES})
MESSAGE(STATUS "MPI C++ libraries: " ${MPI_CXX_LIBRARIES})
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else()
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ADD_DEFINITIONS(-DUSE_SOCKET)
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endif(USE_MPI)
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if(USE_OPENMP)
find_package(OpenMP REQUIRED)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
else()
# Ignore unknown #pragma warning
if((CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
OR (CMAKE_CXX_COMPILER_ID STREQUAL "GNU"))
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unknown-pragmas")
endif()
endif(USE_OPENMP)
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Initial GPU acceleration support for LightGBM (#368) * add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
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if(USE_GPU)
SET(BOOST_COMPUTE_HEADER_DIR ${PROJECT_SOURCE_DIR}/compute/include)
include_directories(${BOOST_COMPUTE_HEADER_DIR})
Initial GPU acceleration support for LightGBM (#368) * add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
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find_package(OpenCL REQUIRED)
include_directories(${OpenCL_INCLUDE_DIRS})
MESSAGE(STATUS "OpenCL include directory: " ${OpenCL_INCLUDE_DIRS})
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if (WIN32)
set(Boost_USE_STATIC_LIBS ON)
endif()
Initial GPU acceleration support for LightGBM (#368) * add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
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find_package(Boost 1.56.0 COMPONENTS filesystem system REQUIRED)
if (WIN32)
# disable autolinking in boost
add_definitions(-DBOOST_ALL_NO_LIB)
endif()
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include_directories(${Boost_INCLUDE_DIRS})
Initial GPU acceleration support for LightGBM (#368) * add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
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ADD_DEFINITIONS(-DUSE_GPU)
endif(USE_GPU)
if(USE_HDFS)
find_package(JNI REQUIRED)
find_path(HDFS_INCLUDE_DIR hdfs.h REQUIRED)
find_library(HDFS_LIB NAMES hdfs REQUIRED)
include_directories(${HDFS_INCLUDE_DIR})
ADD_DEFINITIONS(-DUSE_HDFS)
SET(HDFS_CXX_LIBRARIES ${HDFS_LIB} ${JAVA_JVM_LIBRARY})
endif(USE_HDFS)
include(CheckCXXSourceCompiles)
check_cxx_source_compiles("
#include <xmmintrin.h>
int main() {
int a = 0;
_mm_prefetch(&a, _MM_HINT_NTA);
return 0;
}
" MM_PREFETCH)
if(${MM_PREFETCH})
message(STATUS "Use _mm_prefetch")
ADD_DEFINITIONS(-DMM_PREFETCH)
endif()
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if(UNIX OR MINGW OR CYGWIN)
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SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -pthread -O3 -Wextra -Wall -Wno-ignored-attributes -Wno-unknown-pragmas -Wno-return-type")
if(USE_SWIG)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fno-strict-aliasing")
endif()
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endif()
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if(WIN32 AND MINGW)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -static-libstdc++")
endif()
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if(MSVC)
SET(variables
CMAKE_C_FLAGS_DEBUG
CMAKE_C_FLAGS_MINSIZEREL
CMAKE_C_FLAGS_RELEASE
CMAKE_C_FLAGS_RELWITHDEBINFO
CMAKE_CXX_FLAGS_DEBUG
CMAKE_CXX_FLAGS_MINSIZEREL
CMAKE_CXX_FLAGS_RELEASE
CMAKE_CXX_FLAGS_RELWITHDEBINFO
)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /W4 /O2 /Ob2 /Oi /Ot /Oy /GL /MP")
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else()
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fPIC")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -funroll-loops")
endif(MSVC)
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SET(LightGBM_HEADER_DIR ${PROJECT_SOURCE_DIR}/include)
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SET(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR})
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SET(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR})
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include_directories(${LightGBM_HEADER_DIR})
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if(APPLE)
if(APPLE_OUTPUT_DYLIB)
SET(CMAKE_SHARED_LIBRARY_SUFFIX ".dylib")
else()
SET(CMAKE_SHARED_LIBRARY_SUFFIX ".so")
endif()
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endif(APPLE)
if(USE_MPI)
include_directories(${MPI_CXX_INCLUDE_PATH})
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endif(USE_MPI)
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file(GLOB SOURCES
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src/application/*.cpp
src/boosting/*.cpp
src/io/*.cpp
src/metric/*.cpp
src/objective/*.cpp
src/network/*.cpp
src/treelearner/*.cpp
)
add_executable(lightgbm src/main.cpp ${SOURCES})
add_library(_lightgbm SHARED src/c_api.cpp src/lightgbm_R.cpp ${SOURCES})
if(MSVC)
set_target_properties(_lightgbm PROPERTIES OUTPUT_NAME "lib_lightgbm")
endif(MSVC)
if(USE_SWIG)
set_property(SOURCE swig/lightgbmlib.i PROPERTY CPLUSPLUS ON)
LIST(APPEND swig_options -package com.microsoft.ml.lightgbm)
set_property(SOURCE swig/lightgbmlib.i PROPERTY SWIG_FLAGS "${swig_options}")
swig_add_module(_lightgbm_swig java swig/lightgbmlib.i)
swig_link_libraries(_lightgbm_swig _lightgbm)
# needed to ensure Linux build does not have lib prefix specified twice, e.g. liblib_lightgbm_swig
set_target_properties(_lightgbm_swig PROPERTIES PREFIX "")
# needed in some versions of CMake for VS and MinGW builds to ensure output dll has lib prefix
set_target_properties(_lightgbm_swig PROPERTIES OUTPUT_NAME "lib_lightgbm_swig")
if(WIN32)
if(MINGW OR CYGWIN)
add_custom_command(TARGET _lightgbm_swig POST_BUILD
COMMAND "${Java_JAVAC_EXECUTABLE}" -d . java/*.java
COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${PROJECT_SOURCE_DIR}/lib_lightgbm.dll" com/microsoft/ml/lightgbm/windows/x86_64
COMMAND "${CMAKE_COMMAND}" -E copy_if_different "${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.dll" com/microsoft/ml/lightgbm/windows/x86_64
COMMAND "${Java_JAR_EXECUTABLE}" -cf lightgbmlib.jar com)
else()
add_custom_command(TARGET _lightgbm_swig POST_BUILD
COMMAND "${Java_JAVAC_EXECUTABLE}" -d . java/*.java
COMMAND cp "${PROJECT_SOURCE_DIR}/Release/*.dll" com/microsoft/ml/lightgbm/windows/x86_64
COMMAND "${Java_JAR_EXECUTABLE}" -cf lightgbmlib.jar com)
endif()
elseif(APPLE)
add_custom_command(TARGET _lightgbm_swig POST_BUILD
COMMAND "${Java_JAVAC_EXECUTABLE}" -d . java/*.java
COMMAND cp "${PROJECT_SOURCE_DIR}/*.dylib" com/microsoft/ml/lightgbm/osx/x86_64
COMMAND cp "${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.jnilib" com/microsoft/ml/lightgbm/osx/x86_64/lib_lightgbm_swig.dylib
COMMAND "${Java_JAR_EXECUTABLE}" -cf lightgbmlib.jar com)
else()
add_custom_command(TARGET _lightgbm_swig POST_BUILD
COMMAND "${Java_JAVAC_EXECUTABLE}" -d . java/*.java
COMMAND cp "${PROJECT_SOURCE_DIR}/*.so" com/microsoft/ml/lightgbm/linux/x86_64
COMMAND "${Java_JAR_EXECUTABLE}" -cf lightgbmlib.jar com)
endif()
endif(USE_SWIG)
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if(USE_MPI)
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TARGET_LINK_LIBRARIES(lightgbm ${MPI_CXX_LIBRARIES})
TARGET_LINK_LIBRARIES(_lightgbm ${MPI_CXX_LIBRARIES})
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endif(USE_MPI)
if(USE_OPENMP)
if(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang")
TARGET_LINK_LIBRARIES(lightgbm OpenMP::OpenMP_CXX)
TARGET_LINK_LIBRARIES(_lightgbm OpenMP::OpenMP_CXX)
endif()
endif(USE_OPENMP)
Initial GPU acceleration support for LightGBM (#368) * add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
2017-04-09 16:53:14 +03:00
if(USE_GPU)
2017-06-20 09:18:37 +03:00
TARGET_LINK_LIBRARIES(lightgbm ${OpenCL_LIBRARY} ${Boost_LIBRARIES})
TARGET_LINK_LIBRARIES(_lightgbm ${OpenCL_LIBRARY} ${Boost_LIBRARIES})
Initial GPU acceleration support for LightGBM (#368) * add dummy gpu solver code * initial GPU code * fix crash bug * first working version * use asynchronous copy * use a better kernel for root * parallel read histogram * sparse features now works, but no acceleration, compute on CPU * compute sparse feature on CPU simultaneously * fix big bug; add gpu selection; add kernel selection * better debugging * clean up * add feature scatter * Add sparse_threshold control * fix a bug in feature scatter * clean up debug * temporarily add OpenCL kernels for k=64,256 * fix up CMakeList and definition USE_GPU * add OpenCL kernels as string literals * Add boost.compute as a submodule * add boost dependency into CMakeList * fix opencl pragma * use pinned memory for histogram * use pinned buffer for gradients and hessians * better debugging message * add double precision support on GPU * fix boost version in CMakeList * Add a README * reconstruct GPU initialization code for ResetTrainingData * move data to GPU in parallel * fix a bug during feature copy * update gpu kernels * update gpu code * initial port to LightGBM v2 * speedup GPU data loading process * Add 4-bit bin support to GPU * re-add sparse_threshold parameter * remove kMaxNumWorkgroups and allows an unlimited number of features * add feature mask support for skipping unused features * enable kernel cache * use GPU kernels withoug feature masks when all features are used * REAdme. * REAdme. * update README * fix typos (#349) * change compile to gcc on Apple as default * clean vscode related file * refine api of constructing from sampling data. * fix bug in the last commit. * more efficient algorithm to sample k from n. * fix bug in filter bin * change to boost from average output. * fix tests. * only stop training when all classes are finshed in multi-class. * limit the max tree output. change hessian in multi-class objective. * robust tree model loading. * fix test. * convert the probabilities to raw score in boost_from_average of classification. * fix the average label for binary classification. * Add boost_from_average to docs (#354) * don't use "ConvertToRawScore" for self-defined objective function. * boost_from_average seems doesn't work well in binary classification. remove it. * For a better jump link (#355) * Update Python-API.md * for a better jump in page A space is needed between `#` and the headers content according to Github's markdown format [guideline](https://guides.github.com/features/mastering-markdown/) After adding the spaces, we can jump to the exact position in page by click the link. * fixed something mentioned by @wxchan * Update Python-API.md * add FitByExistingTree. * adapt GPU tree learner for FitByExistingTree * avoid NaN output. * update boost.compute * fix typos (#361) * fix broken links (#359) * update README * disable GPU acceleration by default * fix image url * cleanup debug macro * remove old README * do not save sparse_threshold_ in FeatureGroup * add details for new GPU settings * ignore submodule when doing pep8 check * allocate workspace for at least one thread during builing Feature4 * move sparse_threshold to class Dataset * remove duplicated code in GPUTreeLearner::Split * Remove duplicated code in FindBestThresholds and BeforeFindBestSplit * do not rebuild ordered gradients and hessians for sparse features * support feature groups in GPUTreeLearner * Initial parallel learners with GPU support * add option device, cleanup code * clean up FindBestThresholds; add some omp parallel * constant hessian optimization for GPU * Fix GPUTreeLearner crash when there is zero feature * use np.testing.assert_almost_equal() to compare lists of floats in tests * travis for GPU
2017-04-09 16:53:14 +03:00
endif(USE_GPU)
if(USE_HDFS)
TARGET_LINK_LIBRARIES(lightgbm ${HDFS_CXX_LIBRARIES})
TARGET_LINK_LIBRARIES(_lightgbm ${HDFS_CXX_LIBRARIES})
endif(USE_HDFS)
2016-12-07 06:45:55 +03:00
if(WIN32 AND (MINGW OR CYGWIN))
2017-06-20 09:18:37 +03:00
TARGET_LINK_LIBRARIES(lightgbm Ws2_32)
TARGET_LINK_LIBRARIES(_lightgbm Ws2_32)
TARGET_LINK_LIBRARIES(lightgbm IPHLPAPI)
TARGET_LINK_LIBRARIES(_lightgbm IPHLPAPI)
2016-12-07 06:45:55 +03:00
endif()
2016-08-05 09:06:01 +03:00
2017-06-20 09:18:37 +03:00
install(TARGETS lightgbm _lightgbm
RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/bin
LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/lib
ARCHIVE DESTINATION ${CMAKE_INSTALL_PREFIX}/lib)
2017-01-24 11:53:58 +03:00
install(DIRECTORY ${LightGBM_HEADER_DIR}/LightGBM DESTINATION ${CMAKE_INSTALL_PREFIX}/include)