What is your overall memory usage like when this happens? Sounds like
classic memory swapping/thrashing to me - what are your system specs?
One quick thing to try might be to change the dtype of the matrices to save
some space. float32 vs float64 can make a large memory difference if you
don't need double precision. Also as far as I know, sklearn/joblib doesn't
do any kind of scheduling or optimization based on available resources,
though someone may correct me here. This means that if required memory to
run n jobs is >> than your system memory, very bad things (TM) will happen
On Tue, May 27, 2014 at 3:39 PM, Chris Holdgraf <[email protected]>wrote:
> 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
>
>
> --
> _____________________________________
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