Transform ML models into native code with zero dependencies

Re:Transform ML models into native code with zero dependencies by wespiser_2018 on Tuesday March 05, 2019 @02:12PM (#19313230)

One big improvement for this project would be to somehow break out the model weights into an additional dependency, or file, to allow for very large models and separation between "code" and "data. Overall, pretty good, the alternative approach is either using the underlying C++ lib, or just doing some matrix math!

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Re:Transform ML models into native code with zero dependencies by darius_morawiec on Tuesday March 05, 2019 @03:30PM (#19313932)

The package sklearn-porter supports the separation between inference (code) and model data (parameters) by passing `export_data=True` while transpiling the trained estimator.

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Re:Transform ML models into native code with zero dependencies by wespiser_2018 on Tuesday March 05, 2019 @03:36PM (#19313983)

Thanks, I'm coming over from R and will certainly check that out!

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