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
>>>> >
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