Intel Extension for Scikit-Learn

This page summarizes the projects mentioned and recommended in the original post on news.ycombinator.com
#Machine Learning #machine-learning-algorithms #Python #Data Analysis #Oneapi

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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: https://github.com/scikit-learn/scikit-learn/pull/20254

    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 https://github.com/intel/scikit-learn-intelex#-acceleration

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

  • InfluxDB

    Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Platform where developers build real-time applications for analytics, IoT and cloud-native services. Easy to start, it is available in the cloud or on-premises.

  • cuml

    cuML - RAPIDS Machine Learning Library

    https://github.com/rapidsai/cuml

    > 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-dpex

    Data Parallel Extension for Numba

    > 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:

    https://github.com/IntelPython/numba-dppy

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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