@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
>>> >
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