sklearn Logistic Regression with n_jobs=-1 doesn't actually parallelize
<p>I'm trying to train a huge dataset with sklearn's logistic regression.
I've set the parameter n_jobs=-1 (also have tried n_jobs = 5, 10, ...), but when I open htop, I can see that it still uses only one core.</p>

<p>Does it mean that logistic regression just ignores the n_jobs parameter?</p>

<p>How can I fix this? I really need this process to become parallelized...</p>

<p>P.S. I am using sklearn 0.17.1</p>

<p>the parallel process backend also depends on the solver method. if you want to utilize multi core, the <code>multiprocessing</code> backend is needed. </p>

<p>but solver like 'sag' can only use <code>threading</code> backend.</p>

<p>and also mostly, it can be blocked due to a lot of pre-processing.</p>

python python-2.7 parallel-processing scikit-learn logistic-regression