Note that the installation process of HDFS version was tested only on Linux. HDFS library is needed: details for installation can be found in Installation Guide. Build HDFS Version pip install lightgbm -install-option =-hdfs Build CUDA Version pip install lightgbm -install-option =-cudaĪll requirements from Build from Sources section apply for this installation option as well, and CMake (version 3.16 or higher) is strongly required.ĬUDA library (version 9.0 or higher) is needed: details for installation can be found in Installation Guide. Almost always you also need to pass OpenCL_INCLUDE_DIR, OpenCL_LIBRARY options for Linux and BOOST_ROOT, BOOST_LIBRARYDIR options for Windows to CMake via pip options, like pip install lightgbm -install-option =-gpu -install-option = "-opencl-include-dir=/usr/local/cuda/include/" -install-option = "-opencl-library=/usr/local/cuda/lib64/libOpenCL.so"įor more details see FindBoost and FindOpenCL. Build GPU Version pip install lightgbm -install-option =-gpuįor Windows users, CMake (version 3.8 or higher) is strongly required.īoost and OpenCL are needed: details for installation can be found in Installation Guide. MPI libraries are needed: details for installation can be found in Installation Guide. It is strongly not recommended to use this version of LightGBM! Build MPI Version pip install lightgbm -install-option =-mpiĪll requirements from Build from Sources section apply for this installation option as well.įor Windows users, compilation with MinGW-w64 is not supported and CMake (version 3.8 or higher) is strongly required. Build Threadless Version pip install lightgbm -install-option =-nompĪll requirements, except the OpenMP requirement, from Build from Sources section apply for this installation option as well. If you get any errors during installation, you may need to install CMake (version 3.8 or higher). ![]() In case you prefer gcc, you need to install it (details for installation can be found in Installation Guide) and specify compilers by running export CXX=g++-7 CC=gcc-7 (replace “7” with version of gcc installed on your machine) first.įor Windows users, Visual Studio (or VS Build Tools) is needed. In case you prefer Apple Clang, you should install OpenMP (details for installation can be found in Installation Guide) first and CMake version 3.16 or higher is required. Also, in some rare cases you may need to install OpenMP runtime library separately (use your package manager and search for libomp for doing this).įor macOS users, you can perform installation either with Apple Clang or gcc. Refer to Installation Guide for installation of gcc-8 with OpenMP support.įor version smaller than 2.1.2, gcc-7 with OpenMP is required.īuild from Sources pip install -no-binary :all: lightgbmįor Linux and macOS users, installation from sources requires installed CMake.įor Linux users, glibc >= 2.14 is required. You can install the OpenMP library by the following command: brew install libomp.įor version smaller than 2.2.1 and not smaller than 2.1.2, gcc-8 with OpenMP support must be installed first. Instead of that you need to install the OpenMP library, which is required for running LightGBM on the system with the Apple Clang compiler. This means that you don’t need to install the gcc compiler anymore. Starting from version 2.2.1, the library file in distribution wheels is built by the Apple Clang (Xcode_8.3.3 for versions 2.2.1 - 2.3.1, and Xcode_9.4.1 from version 2.3.2) compiler. Also, in some rare cases, when you hit OSError: libgomp.so.1: cannot open shared object file: No such file or directory error during importing LightGBM, you need to install OpenMP runtime library separately (use your package manager and search for libomp for doing this).įor macOS (we provide wheels for 3 newest macOS versions) users: If you would like your AMD or Intel CPU to act like a GPU (for testing and debugging) you can install AMD APP SDK.įor Windows users, VC runtime is needed if Visual Studio (2015 or newer) is not installed.įor Linux users, glibc >= 2.14 is required. ![]() For NVIDIA and AMD GPU they are included in the ordinary drivers for your graphics card, so no action is required. To use GPU version you only need to install OpenCL Runtime libraries. This feature is experimental and available only for Windows currently. You may need to install wheel via pip install wheel first.Ĭompiled library that is included in the wheel file supports both GPU and CPU versions out of the box. If you have a strong need to install with 32-bit Python, refer to Build 32-bit Version with 32-bit Python section. _global_ void testKernel(optix::float3* cudaBuf). Now I want to add some computation on the GPU using Cuda.Īs a starting point I created this simple kernel, in a file called “sort_cuda.cu”: #include I created a shared library that use OptiX.
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