Enable Python to utilize all cores for fitting scikit-learn models
numpy, python, python-2.7, scikit-learn, scipy
Solution
Very few sklearn models can run in parallel by them-selves. `GridSearchCV` with `n_jobs=-1` or `n_jobs=4` in a non `__main__` interactive python session (e.g. in a script) [1] should be able to do multiprocessing under windows (as long as the underlying individual `fit` calls last more than 1s for instance).
The chrome stuff is probably unrelated: just close chrome if you don't want it to use any CPU. You probably have a tab executing some javascript or buggy flash application in the background.
[1] http://docs.python.org/2/library/multiprocessing.html#windows
Problem
I'm running `python 2.7` with `ipython` on Windows 8 64bit with a system that has 4 cores. When fitting a `scikit-learn` model, the CPU usage is 50%, 25% from `python` and 25% from `Chrome`. Why is `chrome` using as much CPU resources as `python`? Are there multithreaded version of `scikit-learn` model fitting functions so utilizing multicores can be as easy as setting a variable? Like... `grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1)`