![]() To see how much time and money we save using the Intel® Extension for Scikit-learn, we should look at the timing benchmarks between the patched Scikit-learn and the original Scikit-learn. Internally, it uses oneDAL to deliver the best performance. Also, it provides model converters for XGBoost and LightGBM to run fast inference without loss of accuracy. Underneath Intel Extension for Scikit-learn, the user-accessible daal4py also provides highly configurable machine learning kernels, some of which support streaming input data and can scale out to clusters of workstations easily and efficiently. We achieve this through the use of the Intel® oneAPI Data Analytics Library (oneDAL) and its convenient Python API daal4py powering the extension underneath. ![]() Intel® Extension for Scikit-learn provides drop-in replacement patching functionality for a seamless way to speed up your Scikit-learn application. Through our collaboration with Intel, enterprises can now leverage open-source innovation on powerful hardware, resulting in accelerated time to value for the business. When it comes to machine learning, better performance can be the difference between models getting into production or not. Built on top of these libraries is the machine learning module Scikit-learn, a staple for data science practitioners. As most of you might know, these packages are at the heart of scientific computing in the Python ecosystem. To deliver the best possible user experience for data science practitioners, popular data science packages and libraries optimized for Intel® oneAPI Math Kernel Library (oneMKL), including NumPy and SciPy, are now easily accessible in the defaults channel of the Anaconda repository. ![]() ![]() This post was co-authored by Anthony DiPietro, Software Engineer, Anaconda and Kirill Petrov, Machine Learning Engineer, Intel.Īnaconda and Intel are collaborating to build key open-source data science packages optimized for Intel hardware to make machine learning fast and scalable for practitioners everywhere. ![]()
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