Intel Extension for Scikit-Learn

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  • scikit-learn

    scikit-learn: machine learning in Python

    Hi all,

    Currently some works is being done to improve computational primitives of scikit-learn to enhance its overhaul performances natively.

    You can have a look at this exploratory PR:

    This other PR is a clear revamp of this previous one:

  • scikit-learn-intelex

    Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

    Looks like they are responding to

    I completely agree. I hope some Intel competitor funds a scikit-learn developer to read this code and extract all the portable performance improvements.

  • Scout APM

    Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.

  • cuml

    cuML - RAPIDS Machine Learning Library

    > cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents. For details on performance, see the cuML Benchmarks Notebook.

  • numba-dppy

    Numba extension for Intel(R) XPUs

    > Intel are focused on data-parallel C++ for delivering high performance, rightly or wrongly.

    They also invest efforts in making it possible to write high performance kernels in Python using an extension to the numba Python compiler:

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