Once installed, you can accelerate scikit-learn applications.
#Intel c compiler performance install
pip install scikit-learn-intelex conda install scikit-learn-intelex -c conda-forge Using Intel® Extension for Scikit-learn It supports Linux, Windows, and Mac systems on x86 architectures.
#Intel c compiler performance download
Select and download the distribution package that you prefer and follow the Get Started Guide for post-installation instructions.Īlternately, you can download Intel® Extension for Scikit-learn using either PyPI or Anaconda Cloud (available from main, conda-forge and intel channels). The AI Kit, which includes all of Intel’s scikit-learn optimizations, is distributed through many common channels, including Intel’s website, YUM, APT, Anaconda, and more. Installing Intel® Extension for Scikit-learn We also see that the Intel-optimized scikit-learn consistently outperformed the NVIDIA V100 GPU (shown in purple). The Intel Advanced Vector Extensions (AVX-512), unavailable on AMD processors, provide much of the performance improvement. Using Intel performance as the baseline, shown as the solid blue line at 1.00 in the chart below, we see that the Intel-optimized scikit-learn algorithms outperform the same algorithms run on the AMD EPYC 7742 processor (shown in orange). In a recent benchmark, Intel engineers analyzed how Intel-optimized scikit-learn performs on the 2nd Generation Intel Xeon Scalable processors compared to AMD and NVIDIA processors. Intel has invested in optimizing the performance of Python and has optimized key data science libraries like scikit-learn, XGBoost, NumPy, and SciPy. Intel® Extension for Scikit-learn, made available through Intel oneAPI AI Analytics Toolkit (AI Kit), reduce run times and gives data scientists time back to focus on their models. Scikit-learn accelerators can analyze ML data across many industry use-cases while driving efficient use of hardware resources. Scikit-learn is one of the most widely used Python packages for data science and machine learning (ML).