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From Nirmal Fernando <nir...@wso2.com>
Subject Re: [MLLib][Kmeans] KMeansModel.computeCost takes lot of time
Date Tue, 14 Jul 2015 03:34:11 GMT
Can it be the limited memory causing this slowness?

On Tue, Jul 14, 2015 at 9:00 AM, Nirmal Fernando <nirmal@wso2.com> wrote:

> Thanks Burak.
>
> Now it takes minutes to repartition;
>
> Active Stages (1) Stage IdDescriptionSubmittedDurationTasks:
> Succeeded/TotalInputOutputShuffle Read Shuffle Write  42 (kill)
> <http://localhost:4040/stages/stage/kill/?id=42&terminate=true> repartition
> at UnsupervisedSparkModelBuilder.java:120
> <http://localhost:4040/stages/stage?id=42&attempt=0> +details
>
> org.apache.spark.api.java.JavaRDD.repartition(JavaRDD.scala:100)
> org.wso2.carbon.ml.core.spark.algorithms.UnsupervisedSparkModelBuilder.buildKMeansModel(UnsupervisedSparkModelBuilder.java:120)
> org.wso2.carbon.ml.core.spark.algorithms.UnsupervisedSparkModelBuilder.build(UnsupervisedSparkModelBuilder.java:84)
> org.wso2.carbon.ml.core.impl.MLModelHandler$ModelBuilder.run(MLModelHandler.java:576)
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> java.lang.Thread.run(Thread.java:745)
>
>  2015/07/14 08:59:30 3.6 min
>  0/3
>  14.6 MB Pending Stages (1) Stage IdDescriptionSubmittedDurationTasks:
> Succeeded/TotalInputOutputShuffle Read Shuffle Write  43 sum at
> KMeansModel.scala:70 <http://localhost:4040/stages/stage?id=43&attempt=0> +details
>
>
> org.apache.spark.rdd.DoubleRDDFunctions.sum(DoubleRDDFunctions.scala:33)
> org.apache.spark.mllib.clustering.KMeansModel.computeCost(KMeansModel.scala:70)
> org.wso2.carbon.ml.core.spark.algorithms.UnsupervisedSparkModelBuilder.buildKMeansModel(UnsupervisedSparkModelBuilder.java:121)
> org.wso2.carbon.ml.core.spark.algorithms.UnsupervisedSparkModelBuilder.build(UnsupervisedSparkModelBuilder.java:84)
> org.wso2.carbon.ml.core.impl.MLModelHandler$ModelBuilder.run(MLModelHandler.java:576)
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
> java.lang.Thread.run(Thread.java:745)
>
>  Unknown Unknown
>  0/8
>
> On Mon, Jul 13, 2015 at 11:44 PM, Burak Yavuz <brkyvz@gmail.com> wrote:
>
>> Can you call repartition(8) or 16 on data.rdd(), before KMeans, and also,
>> .cache()?
>>
>> something like, (I'm assuming you are using Java):
>> ```
>> JavaRDD<Vector> input = data.repartition(8).cache();
>> org.apache.spark.mllib.clustering.KMeans.train(input.rdd(), 3, 20);
>> ```
>>
>> On Mon, Jul 13, 2015 at 11:10 AM, Nirmal Fernando <nirmal@wso2.com>
>> wrote:
>>
>>> I'm using;
>>>
>>> org.apache.spark.mllib.clustering.KMeans.train(data.rdd(), 3, 20);
>>>
>>> Cpu cores: 8 (using default Spark conf thought)
>>>
>>> On partitions, I'm not sure how to find that.
>>>
>>> On Mon, Jul 13, 2015 at 11:30 PM, Burak Yavuz <brkyvz@gmail.com> wrote:
>>>
>>>> What are the other parameters? Are you just setting k=3? What about #
>>>> of runs? How many partitions do you have? How many cores does your machine
>>>> have?
>>>>
>>>> Thanks,
>>>> Burak
>>>>
>>>> On Mon, Jul 13, 2015 at 10:57 AM, Nirmal Fernando <nirmal@wso2.com>
>>>> wrote:
>>>>
>>>>> Hi Burak,
>>>>>
>>>>> k = 3
>>>>> dimension = 785 features
>>>>> Spark 1.4
>>>>>
>>>>> On Mon, Jul 13, 2015 at 10:28 PM, Burak Yavuz <brkyvz@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> How are you running K-Means? What is your k? What is the dimension
of
>>>>>> your dataset (columns)? Which Spark version are you using?
>>>>>>
>>>>>> Thanks,
>>>>>> Burak
>>>>>>
>>>>>> On Mon, Jul 13, 2015 at 2:53 AM, Nirmal Fernando <nirmal@wso2.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> For a fairly large dataset, 30MB, KMeansModel.computeCost takes
lot
>>>>>>> of time (16+ mints).
>>>>>>>
>>>>>>> It takes lot of time at this task;
>>>>>>>
>>>>>>> org.apache.spark.rdd.DoubleRDDFunctions.sum(DoubleRDDFunctions.scala:33)
>>>>>>> org.apache.spark.mllib.clustering.KMeansModel.computeCost(KMeansModel.scala:70)
>>>>>>>
>>>>>>> Can this be improved?
>>>>>>>
>>>>>>> --
>>>>>>>
>>>>>>> Thanks & regards,
>>>>>>> Nirmal
>>>>>>>
>>>>>>> Associate Technical Lead - Data Technologies Team, WSO2 Inc.
>>>>>>> Mobile: +94715779733
>>>>>>> Blog: http://nirmalfdo.blogspot.com/
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>>
>>>>> Thanks & regards,
>>>>> Nirmal
>>>>>
>>>>> Associate Technical Lead - Data Technologies Team, WSO2 Inc.
>>>>> Mobile: +94715779733
>>>>> Blog: http://nirmalfdo.blogspot.com/
>>>>>
>>>>>
>>>>>
>>>>
>>>
>>>
>>> --
>>>
>>> Thanks & regards,
>>> Nirmal
>>>
>>> Associate Technical Lead - Data Technologies Team, WSO2 Inc.
>>> Mobile: +94715779733
>>> Blog: http://nirmalfdo.blogspot.com/
>>>
>>>
>>>
>>
>
>
> --
>
> Thanks & regards,
> Nirmal
>
> Associate Technical Lead - Data Technologies Team, WSO2 Inc.
> Mobile: +94715779733
> Blog: http://nirmalfdo.blogspot.com/
>
>
>


-- 

Thanks & regards,
Nirmal

Associate Technical Lead - Data Technologies Team, WSO2 Inc.
Mobile: +94715779733
Blog: http://nirmalfdo.blogspot.com/

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