scikit-learn: machine learning in Python
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:
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.
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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 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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