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<mailto: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<mailto: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
