Hm, unfortunately, that's what I thought -- sounds like a bug involved in joblib? Does someone has any ideas how to track this down?
@Waseem Can you also try n_jobs=2? Here, I'd expect that it 1) would use maybe 2 times the 12% plus a little bit extra if everything is working correctly with the multi-threading. 2) If you see something like ~30%, I'd say that there's an unnecessary copy made 3) If you see something like > 30% there would be a memory leak somewhere I mentioned scenario 3, because I observed a very similar behavior once: (see https://github.com/scikit-learn/scikit-learn/issues/3973) "I made some weird observations that my GridSearches keep failing after a couple of hours and I initially couldn't figure out why. I monitored the memory usage then over time and saw that it it started with a few gigabytes (~6 Gb) and kept increasing until it crashed the node when it reached the max. 128 Gb the hardware can take. I was experimenting with random forests for classification of a large number of text documents. For simplicity -- to figure out what's going on -- I went back to naive Bayes. ... After some experimentation, I finally found out that gc.collect() len(gc.get_objects()) # particularly this part! in the for loop solves the problem and the memory usage stays constantly at 6.5 Gb over the run time of ~10 hours. > On Feb 15, 2016, at 9:37 AM, muhammad waseem <m.waseem.ah...@gmail.com> wrote: > > @Sebastian: I have tried to run cross_validation by using n_jobs=1 and it did > not use SWAP memory, even the RAM usage was quite low (maximum 12%). However, > this will take a longer time to finish. Any idea what to try now? > > Thanks > Kindest Regards > Waseem > > On Fri, Feb 12, 2016 at 9:58 PM, Jacob Schreiber <jmschreibe...@gmail.com> > wrote: > I don't think that the data is copied for tree based classifiers. It uses the > threading backend, so each thread should be sharing memory. > > On Fri, Feb 12, 2016 at 12:32 PM, Sebastian Raschka <se.rasc...@gmail.com> > wrote: > I'd suggest trying n_jobs=1 and check if swap memory is used (you don't have > to run it until completion). If this runs fine without swap, we can work > further from there. > > Sent from my iPhone > > On Feb 12, 2016, at 2:57 PM, muhammad waseem <m.waseem.ah...@gmail.com> wrote: > >> @Sebastian: I tried with n_jobs=10 (total is equal to 12) and it still >> created the same problem. I could try running it by using n_jobs=1 but it >> would be so slow that it will take ages to complete. The machine has 32GB >> RAM and it started using Swap memory after consuming full RAM. >> >> Is there a way to tackle or you really think that all this k-fold cross >> validation, training should be done using Spark's MLib? >> >> Thanks >> Regards >> Waseem >> >> >> On Fri, Feb 12, 2016 at 6:40 PM, Sebastian Raschka <se.rasc...@gmail.com> >> wrote: >> Thanks for the note, Manoj, didn't know that! >> >> @muhammad So if there's no duplication of data across all processes, I guess >> that the you would also run into troubles with n_jobs=1. But just to make >> sure that data duplication is not an issue, could you try running it with >> n_jobs=1? In this case, probably only a smaller data set or machine with >> larger memory would help. Here, I'd probably think about using Spark's MLlib >> to deal with this particular dataset. >> >>> On Feb 12, 2016, at 12:30 PM, muhammad waseem <m.waseem.ah...@gmail.com> >>> wrote: >>> >>> Hi Sebastian and Manoj, >>> @Manoj: What should be the value of max_nbytes parameter and will this >>> affect the results and time it takes to run cross_validation, grid_search >>> etc? >>> @Sebastian: Will the Spark implication will also improve the memory use or >>> just the CPU? >>> >>> >>> Thanks >>> Kindest Regards >>> >>> On Fri, Feb 12, 2016 at 5:29 PM, muhammad waseem <m.waseem.ah...@gmail.com> >>> wrote: >>> Hi Sebastian and Manoj, >>> @Manoj: What should be the value of max_nbytes parameter and will this >>> affect the results and time it takes to run cross_validation, grid_search >>> etc? >>> >>> Thanks >>> Kindest Regards >>> Waseem >>> >>> On Fri, Feb 12, 2016 at 4:42 PM, Sebastian Raschka <se.rasc...@gmail.com> >>> wrote: >>> 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 >>> > ------------------------------------------------------------------------------ >>> > Site24x7 APM Insight: Get Deep Visibility into Application Performance >>> > APM + Mobile APM + RUM: Monitor 3 App instances at just $35/Month >>> > Monitor end-to-end web transactions and take corrective actions now >>> > Troubleshoot faster and improve end-user experience. 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