3 ms·
A tip for anyone who suffers with the slow training times of the sklearn logistic regression: you can write it with skorch in no time and get _much_ faster trai
by savant_penguin 5y ago
A tip for anyone who suffers with the slow training times of the sklearn logistic regression: you can write it with skorch in no time and get _much_ faster training times.
I wonder if sklearn will have a pytorch backend one day
- gh02t 5y agoGPyTorch also absolutely crushes the Scikit implementation for Gaussian processes in my experience. Scikit is a treasure, but maybe not my first choice for performance.
- hnnemo 5y agoskorch also supports GPyTorch, see https://skorch.readthedocs.io/en/stable/user/probabilistic.html https://skorch.readthedocs.io/en/stable/user/probabilistic.h... :)
- zetazzed 5y agoConsider also GPU accelerating the whole thing if you have a GPU around. cuML matches the sklearn API https://github.com/rapidsai/cuml/ https://github.com/rapidsai/cuml/. Pays off very quickly if you have large datasets.
- antman 5y agoA drop in replacement for a large part of sklearn for Intel CPUs: https://github.com/intel/scikit-learn-intelex https://github.com/intel/scikit-learn-intelex
- sbbq 5y agoI 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-regression 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 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.