Hi Alexander, Thanks for your reply.Actually I am working with a modified version of the actual MNIST dataset ( maximum samples = 8.2 M) https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html. I have been running different sized versions*( 10000,100000,500000,1M,8M samples)* on different number of workers(*1,2,3,4,5*) and obtaining results. I have observed that when I specify partitions manually, the cluster actually shows scalability performance with decrease in time taken with increase in number of cores. With default settings, Spark automatically divides the data into partitions ( I guess based on data size,etc) and this number is fixed irrespective of the actual number of workers present in the cluster.
As per the data residing on two machines is concerned, I am reading the data from HDFS ( multi-node hadoop cluster setup done for all worker machines). With default number of partitions, Spark gives better results ( less time and better accuracy) as compared to when I manually set the number of partitions; but the problem here is that I can't observe the effect of scalability. My question is that if I have to obtain both scalability and optimality how should I go about it in Spark? Because clearly in my case, scalable implementation is not necessarily optimal. Here, by scalability I mean that if I increase he number of worker machines , I should get a better performance ( less time taken). Thanks and Regards Disha On Mon, Oct 12, 2015 at 11:45 PM, Ulanov, Alexander < alexander.ula...@hpe.com> wrote: > Hi Disha, > > > > The problem might be as follows. The data that you have might physically > reside only on two nodes and Spark launches data-local tasks. As a result, > only two workers are used. You might want to force Spark to distribute the > data across all nodes, however it does not seem to be worthwhile for this > rather small dataset. > > > > Best regards, Alexander > > > > *From:* Disha Shrivastava [mailto:dishu....@gmail.com] > *Sent:* Sunday, October 11, 2015 9:29 AM > *To:* Mike Hynes > *Cc:* dev@spark.apache.org; Ulanov, Alexander > *Subject:* Re: No speedup in MultiLayerPerceptronClassifier with increase > in number of cores > > > > Actually I have 5 workers running ( 1 per physical machine) as displayed > by the spark UI on spark://IP_of_the_master:7077. I have entered all the > physical machines IP in a file named slaves in spark/conf directory and > using the script start-all.sh to start the cluster. > > My question is that is there a way to control how the tasks are > distributed among different workers? To my knowledge it is done by Spark > automatically and is not in our control. > > > > On Sun, Oct 11, 2015 at 9:49 PM, Mike Hynes <91m...@gmail.com> wrote: > > Having only 2 workers for 5 machines would be your problem: you > probably want 1 worker per physical machine, which entails running the > spark-daemon.sh script to start a worker on those machines. > The partitioning is agnositic to how many executors are available for > running the tasks, so you can't do scalability tests in the manner > you're thinking by changing the partitioning. > > > On 10/11/15, Disha Shrivastava <dishu....@gmail.com> wrote: > > Dear Spark developers, > > > > I am trying to study the effect of increasing number of cores ( CPU's) on > > speedup and accuracy ( scalability with spark ANN ) performance for the > > MNIST dataset using ANN implementation provided in the latest spark > > release. > > > > I have formed a cluster of 5 machines with 88 cores in total.The thing > > which is troubling me is that even if I have more than 2 workers in my > > spark cluster the job gets divided only to 2 workers.( executors) which > > Spark takes by default and hence it takes the same time . I know we can > set > > the number of partitions manually using sc.parallelize(train_data,10) > > suppose which then divides the data in 10 partitions and all the workers > > are involved in the computation.I am using the below code: > > > > > > import org.apache.spark.ml.classification.MultilayerPerceptronClassifier > > import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator > > import org.apache.spark.mllib.util.MLUtils > > import org.apache.spark.sql.Row > > > > // Load training data > > val data = MLUtils.loadLibSVMFile(sc, "data/10000_libsvm").toDF() > > // Split the data into train and test > > val splits = data.randomSplit(Array(0.7, 0.3), seed = 1234L) > > val train = splits(0) > > val test = splits(1) > > //val tr=sc.parallelize(train,10); > > // specify layers for the neural network: > > // input layer of size 4 (features), two intermediate of size 5 and 4 and > > output of size 3 (classes) > > val layers = Array[Int](784,160,10) > > // create the trainer and set its parameters > > val trainer = new > > > MultilayerPerceptronClassifier().setLayers(layers).setBlockSize(128).setSeed(1234L).setMaxIter(100) > > // train the model > > val model = trainer.fit(train) > > // compute precision on the test set > > val result = model.transform(test) > > val predictionAndLabels = result.select("prediction", "label") > > val evaluator = new > > MulticlassClassificationEvaluator().setMetricName("precision") > > println("Precision:" + evaluator.evaluate(predictionAndLabels)) > > > > Can you please suggest me how can I ensure that the data/task is divided > > equally to all the worker machines? > > > > Thanks and Regards, > > Disha Shrivastava > > Masters student, IIT Delhi > > > > -- > Thanks, > Mike > > >
