Conda-Forge channel (recommended for users by defaut):.Linux*, Windows*, and MacOS are supported (x86 architecture only). View more details in XGBoost Python* Installation Guide. There are multiple options to only install XGBoost Optimized for Intel® Architecture *. You can find more detailed information about the toolkit here. To download XGBoost from the Intel® oneAPI AI Analytics Toolkit, visit here and choose the installation method of your choice. It is distributed through several channels – Anaconda, Docker containers, Package managers (Yum, Apt, Zypper) and an online/offline installer from Intel. There are multiple ways to get the toolkit and its components. Intel® AI Analytics Toolkit includes XGBoost with Intel optimizations for XPU. Supported Installation Options Install via Intel® oneAPI AI Analytics Toolkit However, recent versions of XGBoost have the latest Intel optimizations which can increase and improve performance. Please note that if you already have one of the latest versions of the XGBoost package installed (any version after 0.81), you do not need to remove or re-install it. This well-known, machine-learning package for gradient-boosted decision trees now includes seamless, drop-in acceleration for Intel® architectures to significantly speed up model training and improve accuracy for better predictions.įor more information on the purpose and functionality of the XGBoost package, please refer to the XGBoost documentation. Starting with XGBoost 0.81 version onward, Intel has been directly upstreaming many optimizations to provide superior performance on Intel® CPUs. About XGBoost Optimized for Intel® Architecture
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