LightGBM/.gitmodules

[submodule "include/boost/compute"]
	path = compute
	url = https://github.com/boostorg/compute
[submodule "external_libs/fmt"]
	path = external_libs/fmt
	url = https://github.com/fmtlib/fmt.git
[submodule "external_libs/fast_double_parser"]
	path = external_libs/fast_double_parser
	url = https://github.com/lemire/fast_double_parser.git
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			`[submodule "include/boost/compute"]`
			`path = compute`
			`url = https://github.com/boostorg/compute`
Fix model locale issue and improve model R/W performance. (#3405) * Fix LightGBM models locale sensitivity and improve R/W performance. When Java is used, the default C++ locale is broken. This is true for Java providers that use the C API or even Python models that require JEP. This patch solves that issue making the model reads/writes insensitive to such settings. To achieve it, within the model read/write codebase: - C++ streams are imbued with the classic locale - Calls to functions that are dependent on the locale are replaced - The default locale is not changed! This approach means: - The user's locale is never tampered with, avoiding issues such as https://github.com/microsoft/LightGBM/issues/2979 with the previous approach https://github.com/microsoft/LightGBM/pull/2891 - Datasets can still be read according the user's locale - The model file has a single format independent of locale Changes: - Add CommonC namespace which provides faster locale-independent versions of Common's methods - Model code makes conversions through CommonC - Cleanup unused Common methods - Performance improvements. Use fast libraries for locale-agnostic conversion: - value->string: https://github.com/fmtlib/fmt - string->double: https://github.com/lemire/fast_double_parser (10x faster double parsing according to their benchmark) Bugfixes: - https://github.com/microsoft/LightGBM/issues/2500 - https://github.com/microsoft/LightGBM/issues/2890 - https://github.com/ninia/jep/issues/205 (as it is related to LGBM as well) * Align CommonC namespace * Add new external_libs/ to python setup * Try fast_double_parser fix #1 Testing commit e09e5aad828bcb16bea7ed0ed8322e019112fdbe If it works it should fix more LGBM builds * CMake: Attempt to link fmt without explicit PUBLIC tag * Exclude external_libs from linting * Add exernal_libs to MANIFEST.in * Set dynamic linking option for fmt. * linting issues * Try to fix lint includes * Try to pass fPIC with static fmt lib * Try CMake P_I_C option with fmt library * [R-package] Add CMake support for R and CRAN * Cleanup CMakeLists * Try fmt hack to remove stdout * Switch to header-only mode * Add PRIVATE argument to target_link_libraries * use fmt in header-only mode * Remove CMakeLists comment * Change OpenMP to PUBLIC linking in Mac * Update fmt submodule to 7.1.2 * Use fmt in header-only-mode * Remove fmt from CMakeLists.txt * Upgrade fast_double_parser to v0.2.0 * Revert "Add PRIVATE argument to target_link_libraries" This reverts commit 3dd45dde7b92531b2530ab54522bb843c56227a7. * Address James Lamb's comments * Update R-package/.Rbuildignore Co-authored-by: James Lamb <jaylamb20@gmail.com> * Upgrade to fast_double_parser v0.3.0 - Solaris support * Use legacy code only in Solaris * Fix lint issues * Fix comment * Address StrikerRUS's comments (solaris ifdef). * Change header guards Co-authored-by: James Lamb <jaylamb20@gmail.com> 2020-12-08 16:36:24 +03:00			`[submodule "external_libs/fmt"]`
			`path = external_libs/fmt`
			`url = https://github.com/fmtlib/fmt.git`
			`[submodule "external_libs/fast_double_parser"]`
			`path = external_libs/fast_double_parser`
			`url = https://github.com/lemire/fast_double_parser.git`