Machine Learning with PyTorch and Scikit-Learn – The *New* Python ML Book

1. cuml

   1 10 4,625 9.7 C++

   cuML - RAPIDS Machine Learning Library

   

2. Judoscale

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3. scikit-learn-intelex

   2 4 1,269 9.7 Python

   Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

   

4. hummingbird

   3 9 3,432 5.4 Python

   Hummingbird compiles trained ML models into tensor computation for faster inference.

   

   I think Rapids AI's cuML tried to go into this direction (essentially scikit-learn on the GPU): https://docs.rapids.ai/api/cuml/stable/api.html#logistic-reg.... For some reason it never took really off though.

   Btw., going on a tangent, you might like Hummingbird (https://github.com/microsoft/hummingbird). It allows you trained scikit-learn tree-based models to PyTorch. I watched the SciPy talk last year, and it's a super smart & elegant idea.

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