dl4j-acc-cuda-outOfMem-issue

SenderClassiferExample.java

import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.datavec.api.util.ClassPathResource;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.WorkspaceMode;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.indexaccum.IAMax;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;


public class SenderClassifierExample {
    private static Logger log = LoggerFactory.getLogger(SenderClassifierExample.class);

    public static void main(String[] args) throws Exception {
        //First: get the dataset using the record reader. CSVRecordReader handles loading/parsing
        int numLinesToSkip = 0;
        char delimiter = ',';
        //Second: the RecordReaderDataSetIterator handles conversion to DataSet objects, ready for use in neural network
        int labelIndex = 15484;     //15485 values in each row of the feature_label_small.csv CSV: 15484 input features followed by an integer label (class) index. Labels are the 15485th value (index 15484) in each row
        int numClasses = 24501;     //24501 classes (types of senders) in the data set. Classes have integer values 0, 1 or 2 ... and so on
        int batchSize = 32;       //Iris data set: 150 examples total. We are loading all of them into one DataSet (not recommended for large data sets)
        // 516348 examples, with batchSize is 64, around 16000 iterations per epoch
        int printIterationsNum = 200; // print score every 200 iterations

        RecordReader recordReader = new CSVRecordReader(numLinesToSkip,delimiter);
        recordReader.initialize(new FileSplit(new ClassPathResource("feature_label_train.csv").getFile()));
        DataSetIterator trainIter = new RecordReaderDataSetIterator(recordReader,batchSize,labelIndex,numClasses);

        RecordReader testRecordReader = new CSVRecordReader(numLinesToSkip,delimiter);
        testRecordReader.initialize(new FileSplit(new ClassPathResource("feature_label_test.csv").getFile()));
        DataSetIterator testIter = new RecordReaderDataSetIterator(testRecordReader,batchSize,labelIndex,numClasses);

        final int numInputs = 15484;
        int hiddenLayer1Num = 2000;
        int iterations = 1;
        long seed = 42;
        int nEpochs = 20;

        log.info("Build model....");
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .trainingWorkspaceMode(WorkspaceMode.SEPARATE)
                .iterations(iterations)
                .activation(Activation.RELU)
                .weightInit(WeightInit.XAVIER)
                .learningRate(0.02)
                .updater(Adam.builder().beta1(0.9).beta2(0.999).build())
//                .regularization(true).l2(1e-4)
                .list()
                .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(hiddenLayer1Num)
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                        .activation(Activation.SOFTMAX)
                        .nIn(hiddenLayer1Num).nOut(numClasses).build())
                .backprop(true).pretrain(false)
                .build();

        //run the model
        MultiLayerNetwork model = new MultiLayerNetwork(conf);
        model.init();
        model.setListeners(new ScoreIterationListener(printIterationsNum));

        for ( int n = 0; n < nEpochs; n++) {

//            model.fit(trainIter); // commentted so to test the evaluation code below directly

            // evaluate the model on test data, once every epoch
            if ((n + 1) % 1 == 0) {
                //evaluate the model on the test set
                Evaluation eval = new Evaluation(numClasses);

                testIter.reset();
                DataSet t = testIter.next();
                INDArray features = t.getFeatures();
                INDArray labels = t.getLabels();
                INDArray predicted = model.output(features, false);

                INDArray labels_s = Nd4j.argMax(labels, 1);
                INDArray predicted_s = Nd4j.argMax(predicted, 1);

                while(testIter.hasNext()) {
                    t = testIter.next();
                    features = t.getFeatures();
                    INDArray labels2 = t.getLabels();
                    INDArray predicted2 = model.output(features, false);

                    INDArray labels2_s = Nd4j.argMax(labels2, 1);
                    INDArray predicted2_s = Nd4j.argMax(predicted2, 1);

                    labels = Nd4j.vstack(labels,labels2);     // This would cause out of memory issue without the break call below
                    predicted = Nd4j.vstack(predicted,predicted2);

                    labels_s = Nd4j.vstack(labels_s,labels2_s); // This would NOT cause out of memory issue, but causes a CUDA issue without the break call below
                    predicted_s = Nd4j.vstack(predicted_s,predicted2_s);

//                    System.out.println(labels.shape()[0]);
                    if (labels.shape()[0] > 10000) break; // When number of testing data is too high, we have out of memory issue on GPU.
                    // TODO A CUDA error occurs when labels' lengths is above 15000
                    // TODO Test this issue without GPU and make a sample code example later to see if it repeat
                }

                // One way to evaluate accuracy
                float acc = labels_s.eq(predicted_s).mean(0).getFloat(0, 0);
                // Another way to evaluate accuracy
                eval.eval(labels, predicted);

                log.info(String.format("Evaluation on test data - [Epoch %d] [AccHand: %.3f, Accuracy: %.3f, P: %.3f, R: %.3f, F1: %.3f] ",
                        n + 1, acc, eval.accuracy(), eval.precision(), eval.recall(), eval.f1()));
                // log.info(eval.stats());
            }
        }

        System.out.println("Finished...");
    }
}

test1.log

/home/fuyang/Software/Java/jdk-9.0.1/bin/java -Xms2G -Xmx6G -Dorg.bytedeco.javacpp.maxbytes=8G -Dorg.bytedeco.javacpp.maxphysicalbytes=9G -javaagent:/home/fuyang/Software/idea-IC-173.4127.27/lib/idea_rt.jar=41523:/home/fuyang/Software/idea-IC-173.4127.27/bin -Dfile.encoding=UTF-8 -classpath 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eveldbjni-1.7.jar:/home/fuyang/.m2/repository/org/fusesource/hawtjni/hawtjni-runtime/1.8/hawtjni-runtime-1.8.jar:/home/fuyang/.m2/repository/org/fusesource/leveldbjni/leveldbjni-osx/1.5/leveldbjni-osx-1.5.jar:/home/fuyang/.m2/repository/org/fusesource/leveldbjni/leveldbjni-linux32/1.5/leveldbjni-linux32-1.5.jar:/home/fuyang/.m2/repository/org/fusesource/leveldbjni/leveldbjni-linux64/1.5/leveldbjni-linux64-1.5.jar:/home/fuyang/.m2/repository/org/fusesource/leveldbjni/leveldbjni-win32/1.5/leveldbjni-win32-1.5.jar:/home/fuyang/.m2/repository/org/fusesource/leveldbjni/leveldbjni-win64/1.5/leveldbjni-win64-1.5.jar:/home/fuyang/.m2/repository/com/fasterxml/jackson/core/jackson-core/2.4.4/jackson-core-2.4.4.jar:/home/fuyang/.m2/repository/com/fasterxml/jackson/core/jackson-databind/2.4.4/jackson-databind-2.4.4.jar:/home/fuyang/.m2/repository/com/fasterxml/jackson/core/jackson-annotations/2.4.4/jackson-annotations-2.4.4.jar:/home/fuyang/.m2/repository/com/fasterxml/jackson/module/jackson-module-scala_2.11/2.4.4/jackson-module-scala_2.11-2.4.4.jar:/home/fuyang/.m2/repository/org/scala-lang/scala-reflect/2.11.2/scala-reflect-2.11.2.jar:/home/fuyang/.m2/repository/com/thoughtworks/paranamer/paranamer/2.6/paranamer-2.6.jar:/home/fuyang/.m2/repository/com/fasterxml/jackson/datatype/jackson-datatype-jdk8/2.4.4/jackson-datatype-jdk8-2.4.4.jar:/home/fuyang/.m2/repository/com/fasterxml/jackson/datatype/jackson-datatype-jsr310/2.4.4/jackson-datatype-jsr310-2.4.4.jar:/home/fuyang/.m2/repository/com/typesafe/akka/akka-cluster_2.11/2.3.13/akka-cluster_2.11-2.3.13.jar:/home/fuyang/.m2/repository/javax/ws/rs/javax.ws.rs-api/2.0/javax.ws.rs-api-2.0.jar:/home/fuyang/.m2/repository/org/deeplearning4j/deeplearning4j-ui-model/0.9.1/deeplearning4j-ui-model-0.9.1.jar:/home/fuyang/.m2/repository/org/agrona/Agrona/0.5.4/Agrona-0.5.4.jar:/home/fuyang/.m2/repository/org/mapdb/mapdb/3.0.5/mapdb-3.0.5.jar:/home/fuyang/.m2/repository/org/jetbrains/kotlin/kotlin-stdlib/1.0.7/kotlin-stdlib-1.0.7.jar:/home/fuyang/.m2/repository/org/jetbrains/kotlin/kotlin-runtime/1.0.7/kotlin-runtime-1.0.7.jar:/home/fuyang/.m2/repository/org/eclipse/collections/eclipse-collections-api/7.1.1/eclipse-collections-api-7.1.1.jar:/home/fuyang/.m2/repository/net/jcip/jcip-annotations/1.0/jcip-annotations-1.0.jar:/home/fuyang/.m2/repository/org/eclipse/collections/eclipse-collections/7.1.1/eclipse-collections-7.1.1.jar:/home/fuyang/.m2/repository/org/eclipse/collections/eclipse-collections-forkjoin/7.1.1/eclipse-collections-forkjoin-7.1.1.jar:/home/fuyang/.m2/repository/net/jpountz/lz4/lz4/1.3.0/lz4-1.3.0.jar:/home/fuyang/.m2/repository/org/mapdb/elsa/3.0.0-M5/elsa-3.0.0-M5.jar:/home/fuyang/.m2/repository/org/xerial/sqlite-jdbc/3.15.1/sqlite-jdbc-3.15.1.jar:/home/fuyang/.m2/repository/org/deeplearning4j/deeplearning4j-ui-resources/0.9.1/deeplearning4j-ui-resources-0.9.1.jar:/home/fuyang/.m2/repository/org/deeplearning4j/deeplearning4j-parallel-wrapper_2.11/0.9.1/deeplearning4j-parallel-wrapper_2.11-0.9.1.jar:/home/fuyang/.m2/repository/com/beust/jcommander/1.27/jcommander-1.27.jar:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server/0.9.1/nd4j-parameter-server-0.9.1.jar:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server-model/0.9.1/nd4j-parameter-server-model-0.9.1.jar:/home/fuyang/.m2/repository/com/mashape/unirest/unirest-java/1.4.9/unirest-java-1.4.9.jar:/home/fuyang/.m2/repository/org/apache/httpcomponents/httpasyncclient/4.1.1/httpasyncclient-4.1.1.jar:/home/fuyang/.m2/repository/org/apache/httpcomponents/httpcore-nio/4.4.4/httpcore-nio-4.4.4.jar:/home/fuyang/.m2/repository/org/apache/httpcomponents/httpmime/4.5.2/httpmime-4.5.2.jar:/home/fuyang/.m2/repository/org/json/json/20160212/json-20160212.jar:/home/fuyang/.m2/repository/org/nd4j/nd4j-aeron/0.9.1/nd4j-aeron-0.9.1.jar:/home/fuyang/.m2/repository/io/aeron/aeron-all/1.0.4/aeron-all-1.0.4.jar:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server-client/0.9.1/nd4j-parameter-server-client-0.9.1.jar:/home/fuyang/.m2/repository/org/deeplearning4j/arbiter-deeplearning4j/0.9.1/arbiter-deeplearning4j-0.9.1.jar:/home/fuyang/.m2/repository/org/nd4j/nd4j-jackson-reflectionloader/0.9.1/nd4j-jackson-reflectionloader-0.9.1.jar:/home/fuyang/.m2/repository/org/deeplearning4j/arbiter-core/0.9.1/arbiter-core-0.9.1.jar:/home/fuyang/.m2/repository/org/deeplearning4j/arbiter-ui_2.11/0.9.1/arbiter-ui_2.11-0.9.1.jar:/home/fuyang/.m2/repository/org/datavec/datavec-data-codec/0.9.1/datavec-data-codec-0.9.1.jar:/home/fuyang/.m2/repository/org/jcodec/jcodec/0.1.5/jcodec-0.1.5.jar:/home/fuyang/.m2/repository/jfree/jfreechart/1.0.13/jfreechart-1.0.13.jar:/home/fuyang/.m2/repository/jfree/jcommon/1.0.16/jcommon-1.0.16.jar:/home/fuyang/.m2/repository/org/jfree/jcommon/1.0.23/jcommon-1.0.23.jar:/home/fuyang/.m2/repository/org/apache/httpcomponents/httpclient/4.3.5/httpclient-4.3.5.jar:/home/fuyang/.m2/repository/org/apache/httpcomponents/httpcore/4.3.2/httpcore-4.3.2.jar:/home/fuyang/.m2/repository/commons-logging/commons-logging/1.1.3/commons-logging-1.1.3.jar:/home/fuyang/.m2/repository/ch/qos/logback/logback-classic/1.1.7/logback-classic-1.1.7.jar:/home/fuyang/.m2/repository/ch/qos/logback/logback-core/1.1.7/logback-core-1.1.7.jar SenderClassifierExample
2018-01-01T13:11:04.908 [main] INFO  SenderClassifierExample - Build model....
2018-01-01T13:11:05.014 [main] INFO  org.nd4j.linalg.factory.Nd4jBackend - Loaded [JCublasBackend] backend
2018-01-01T13:11:05.055 [main] WARN  org.reflections.Reflections - given scan urls are empty. set urls in the configuration
2018-01-01T13:11:05.768 [main] INFO  org.nd4j.nativeblas.NativeOpsHolder - Number of threads used for NativeOps: 32
2018-01-01T13:11:06.215 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.217 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.218 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.218 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.219 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.219 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.220 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.220 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.221 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.222 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.222 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.223 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.223 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.224 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.225 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.225 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.226 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.226 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.227 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.228 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.228 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.229 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.229 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.230 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.231 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.231 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.232 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.233 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.233 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.234 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.235 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.236 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]...
2018-01-01T13:11:06.245 [main] DEBUG org.reflections.Reflections - going to scan these urls:
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1-linux-ppc64le.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server-model/0.9.1/nd4j-parameter-server-model-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-base64/0.9.1/nd4j-base64-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-native-api/0.9.1/nd4j-native-api-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/jackson/0.9.1/jackson-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1-macosx-x86_64.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-jackson-reflectionloader/0.9.1/nd4j-jackson-reflectionloader-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-aeron/0.9.1/nd4j-aeron-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server/0.9.1/nd4j-parameter-server-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-jackson/0.9.1/nd4j-jackson-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-api/0.9.1/nd4j-api-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-buffer/0.9.1/nd4j-buffer-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server-client/0.9.1/nd4j-parameter-server-client-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-context/0.9.1/nd4j-context-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-common/0.9.1/nd4j-common-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1-linux-x86_64.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1-windows-x86_64.jar!/
2018-01-01T13:11:06.318 [main] INFO  org.reflections.Reflections - Reflections took 70 ms to scan 18 urls, producing 31 keys and 227 values 
2018-01-01T13:11:06.375 [main] INFO  o.n.l.a.o.e.DefaultOpExecutioner - Backend used: [CUDA]; OS: [Linux]
2018-01-01T13:11:06.376 [main] INFO  o.n.l.a.o.e.DefaultOpExecutioner - Cores: [4]; Memory: [6.0GB];
2018-01-01T13:11:06.376 [main] INFO  o.n.l.a.o.e.DefaultOpExecutioner - Blas vendor: [CUBLAS]
2018-01-01T13:11:06.376 [main] INFO  o.n.l.j.o.e.CudaExecutioner - Device name: [GeForce GTX 1080 Ti]; CC: [6.1]; Total/free memory: [11711807488]
2018-01-01T13:11:06.427 [main] DEBUG o.n.j.handler.impl.CudaZeroHandler - Creating bucketID: 0
2018-01-01T13:11:06.560 [main] WARN  org.reflections.Reflections - given scan urls are empty. set urls in the configuration
2018-01-01T13:11:06.611 [main] INFO  o.d.nn.multilayer.MultiLayerNetwork - Starting MultiLayerNetwork with WorkspaceModes set to [training: SEPARATE; inference: SEPARATE]
2018-01-01T13:11:06.702 [main] DEBUG o.n.j.handler.impl.CudaZeroHandler - Creating bucketID: 2
2018-01-01T13:11:06.706 [main] DEBUG o.n.j.handler.impl.CudaZeroHandler - Creating bucketID: 3
2018-01-01T13:11:06.735 [main] DEBUG org.reflections.Reflections - going to scan these urls:
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1-linux-ppc64le.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server-model/0.9.1/nd4j-parameter-server-model-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-base64/0.9.1/nd4j-base64-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-native-api/0.9.1/nd4j-native-api-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/jackson/0.9.1/jackson-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1-macosx-x86_64.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-jackson-reflectionloader/0.9.1/nd4j-jackson-reflectionloader-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-aeron/0.9.1/nd4j-aeron-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server/0.9.1/nd4j-parameter-server-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-jackson/0.9.1/nd4j-jackson-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-api/0.9.1/nd4j-api-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-buffer/0.9.1/nd4j-buffer-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-parameter-server-client/0.9.1/nd4j-parameter-server-client-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-context/0.9.1/nd4j-context-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-common/0.9.1/nd4j-common-0.9.1.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1-linux-x86_64.jar!/
jar:file:/home/fuyang/.m2/repository/org/nd4j/nd4j-cuda-8.0/0.9.1/nd4j-cuda-8.0-0.9.1-windows-x86_64.jar!/
2018-01-01T13:11:06.935 [main] INFO  org.reflections.Reflections - Reflections took 200 ms to scan 18 urls, producing 416 keys and 1637 values 
2018-01-01T13:11:07.007 [main] DEBUG o.n.j.handler.impl.CudaZeroHandler - Creating bucketID: 4
2018-01-01T13:11:07.112 [main] DEBUG o.n.j.handler.impl.CudaZeroHandler - Creating bucketID: 1
2018-01-01T13:11:07.112 [main] DEBUG o.n.j.handler.impl.CudaZeroHandler - Creating bucketID: 5
2018-01-01T13:11:07.833 [main] INFO  org.nd4j.nativeblas.Nd4jBlas - Number of threads used for BLAS: 0
2018-01-01T13:15:47.656 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 1] [AccHand: 0.000, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:19:51.552 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 2] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:23:57.433 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 3] [AccHand: 0.001, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:28:18.013 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 4] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:32:27.192 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 5] [AccHand: 0.001, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:36:28.800 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 6] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:40:27.348 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 7] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:44:46.317 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 8] [AccHand: 0.001, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:49:00.142 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 9] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:54:12.288 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 10] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T13:59:46.859 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 11] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T14:03:44.656 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 12] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T14:07:50.872 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 13] [AccHand: 0.001, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T14:11:49.484 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 14] [AccHand: 0.002, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001] 
2018-01-01T14:15:53.651 [main] INFO  SenderClassifierExample - Evaluation on test data - [Epoch 15] [AccHand: 0.001, Accuracy: 0.000, P: 0.000, R: 0.000, F1: 0.001]