Hi, Waseem,
I think lowering the value of n_jobs would help; as far as I know, each process 
get a copy of the data? Just stumbled upon spark-sklearn a few days ago, maybe 
that could help as well:

https://databricks.com/blog/2016/02/08/auto-scaling-scikit-learn-with-spark.html

When I understand correctly, the data is still copied, but here, each node gets 
a copy instead of one machine with many copies.




> On Feb 12, 2016, at 11:35 AM, muhammad waseem <m.waseem.ah...@gmail.com> 
> wrote:
> 
> Hi, 
> 
> I am trying to fit my model using regression trees but the problem is, it 
> consumes a lot of RAM, which makes my code unresponsive. By looking at 
> different forums and platforms, I think this is a common problem. I was 
> wondering, how you free up memory or what are the best ways to run the 
> fitting process/cross-validation without running out of memory? This problem 
> is mostly with all regression trees (I think with other ML algorithms as 
> well). Shall I try to run without n_job=-1 and use some other value (e.g. 
> n_jobs=10) in cross_validation?
> 
> Thanks
> Kindest Regards
> Waseem
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