In particular, it seems that when I've got matrices which are too big, the
forked processes will hang and never finish (aka, they take up 0 computing
time and remain that way indefinitely).
In particular, I've noticed this problem when using cross_val_score with
Ridge regression. This isn't a problem when I have input matrices on the
order of 800,000 by 100, but it does hang when they're on the order of
800,000 by 800. If I don't use parallel, then it fits fine, but setting
n_jobs > 1 will create a hang.
I'm using anaconda on CentOS, and I've tried this both with and without MKL
optimizations.
Anyone experience anything like this?
(if you'd like more detail, I also opened this as an issue on the sklearn
repo)
Chris
--
_____________________________________
PhD Candidate in Neuroscience | UC Berkeley <http://hwni.org/>
Editor and Web Master | Berkeley Science
Review<http://sciencereview.berkeley.edu/>
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