Test fixes + cleanup (#245)

* Test spam reduction

Signed-off-by: Alex Black <blacka101@gmail.com>

* Arbiter bad import fixes

Signed-off-by: Alex Black <blacka101@gmail.com>

* Small spark test tweak

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Arbiter test log spam reduction

Signed-off-by: Alex Black <blacka101@gmail.com>

* More test spam reduction

Signed-off-by: Alex Black <blacka101@gmail.com>

arbiter/arbiter-core/src/main/java/org/deeplearning4j/arbiter/util/WebUtils.java

@@ -1,47 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://www.apache.org/licenses/LICENSE-2.0.
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- * License for the specific language governing permissions and limitations
- * under the License.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.deeplearning4j.arbiter.util;
-
-import org.slf4j.Logger;
-
-import java.awt.*;
-import java.net.URI;
-
-/**
- * Various utilities for webpages and dealing with browsers
- */
-public class WebUtils {
-
-    public static void tryOpenBrowser(String path, Logger log) {
-        try {
-            WebUtils.openBrowser(new URI(path));
-        } catch (Exception e) {
-            log.error("Could not open browser", e);
-            System.out.println("Browser could not be launched automatically.\nUI path: " + path);
-        }
-    }
-
-    public static void openBrowser(URI uri) throws Exception {
-        if (Desktop.isDesktopSupported()) {
-            Desktop.getDesktop().browse(uri);
-        } else {
-            throw new UnsupportedOperationException(
-                            "Cannot open browser on this platform: Desktop.isDesktopSupported() == false");
-        }
-    }
-
-}

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/BraninFunction.java

@@ -127,7 +127,7 @@ public class BraninFunction {
                     BraninConfig candidate = (BraninConfig) c.getValue();
 
                     double score = scoreFunction.score(candidate, null, (Map) null);
-                    System.out.println(candidate.getX1() + "\t" + candidate.getX2() + "\t" + score);
+//                    System.out.println(candidate.getX1() + "\t" + candidate.getX2() + "\t" + score);
 
                     Thread.sleep(20);
 

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/TestRandomSearch.java

@@ -54,7 +54,7 @@ public class TestRandomSearch extends BaseDL4JTest {
         runner.execute();
 
 
-        System.out.println("----- Complete -----");
+//        System.out.println("----- Complete -----");
     }
 
 

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/genetic/TestRandomGenerator.java

@@ -16,8 +16,8 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic;
 
+import org.apache.commons.lang3.NotImplementedException;
 import org.apache.commons.math3.random.RandomGenerator;
-import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
 public class TestRandomGenerator implements RandomGenerator {
     private final int[] intRandomNumbers;
@@ -63,17 +63,17 @@ public class TestRandomGenerator implements RandomGenerator {
 
     @Override
     public long nextLong() {
-        throw new NotImplementedException();
+        throw new NotImplementedException("Not implemented");
     }
 
     @Override
     public boolean nextBoolean() {
-        throw new NotImplementedException();
+        throw new NotImplementedException("Not implemented");
     }
 
     @Override
     public float nextFloat() {
-        throw new NotImplementedException();
+        throw new NotImplementedException("Not implemented");
     }
 
     @Override
@@ -83,6 +83,6 @@ public class TestRandomGenerator implements RandomGenerator {
 
     @Override
     public double nextGaussian() {
-        throw new NotImplementedException();
+        throw new NotImplementedException("Not implemented");
     }
 }

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/genetic/crossover/TwoParentsCrossoverOperatorTests.java

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
+import org.apache.commons.lang3.NotImplementedException;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverResult;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.TwoParentsCrossoverOperator;
@@ -26,7 +27,6 @@ import org.deeplearning4j.arbiter.optimize.genetic.TestParentSelection;
 import org.deeplearning4j.arbiter.optimize.genetic.TestPopulationInitializer;
 import org.junit.Assert;
 import org.junit.Test;
-import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
 public class TwoParentsCrossoverOperatorTests extends BaseDL4JTest {
 
@@ -42,7 +42,7 @@ public class TwoParentsCrossoverOperatorTests extends BaseDL4JTest {
 
         @Override
         public CrossoverResult crossover() {
-            throw new NotImplementedException();
+            throw new NotImplementedException("Not implemented");
         }
     }
 

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/genetic/culling/RatioCullOperatorTests.java

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.culling;
 
+import org.apache.commons.lang3.NotImplementedException;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.culling.RatioCullOperator;
@@ -24,7 +25,6 @@ import org.deeplearning4j.arbiter.optimize.generator.genetic.population.Populati
 import org.deeplearning4j.arbiter.optimize.genetic.TestPopulationInitializer;
 import org.junit.Assert;
 import org.junit.Test;
-import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
 import java.util.List;
 
@@ -46,7 +46,7 @@ public class RatioCullOperatorTests extends BaseDL4JTest {
 
         @Override
         public void cullPopulation() {
-            throw new NotImplementedException();
+            throw new NotImplementedException("Not implemented");
         }
 
         public double getCullRatio() {

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/genetic/selection/GeneticSelectionOperatorTests.java

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.selection;
 
+import org.apache.commons.lang3.NotImplementedException;
 import org.apache.commons.math3.random.RandomGenerator;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.ChromosomeFactory;
@@ -33,7 +34,6 @@ import org.deeplearning4j.arbiter.optimize.genetic.TestPopulationInitializer;
 import org.deeplearning4j.arbiter.optimize.genetic.TestRandomGenerator;
 import org.junit.Assert;
 import org.junit.Test;
-import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
 import static org.junit.Assert.assertArrayEquals;
 
@@ -55,7 +55,7 @@ public class GeneticSelectionOperatorTests extends BaseDL4JTest {
 
         @Override
         public void cullPopulation() {
-            throw new NotImplementedException();
+            throw new NotImplementedException("Not implemented");
         }
 
         @Override

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/genetic/selection/SelectionOperatorTests.java

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.selection;
 
+import org.apache.commons.lang3.NotImplementedException;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.ChromosomeFactory;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationInitializer;
@@ -24,7 +25,6 @@ import org.deeplearning4j.arbiter.optimize.generator.genetic.selection.Selection
 import org.deeplearning4j.arbiter.optimize.genetic.TestPopulationInitializer;
 import org.junit.Assert;
 import org.junit.Test;
-import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
 public class SelectionOperatorTests extends BaseDL4JTest {
     private class TestSelectionOperator extends SelectionOperator {
@@ -39,7 +39,7 @@ public class SelectionOperatorTests extends BaseDL4JTest {
 
         @Override
         public double[] buildNextGenes() {
-            throw new NotImplementedException();
+            throw new NotImplementedException("Not implemented");
         }
     }
 

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/computationgraph/TestComputationGraphSpace.java

@@ -158,7 +158,7 @@ public class TestComputationGraphSpace extends BaseDL4JTest {
             }
         }
 
-        System.out.println("ReLU vs. Tanh: " + reluCount + "\t" + tanhCount);
+//        System.out.println("ReLU vs. Tanh: " + reluCount + "\t" + tanhCount);
         assertTrue(reluCount > 0);
         assertTrue(tanhCount > 0);
 

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/computationgraph/TestGraphLocalExecution.java

@@ -162,7 +162,7 @@ public class TestGraphLocalExecution extends BaseDL4JTest {
             List<ResultReference> results = runner.getResults();
             assertTrue(results.size() > 0);
 
-            System.out.println("----- COMPLETE - " + results.size() + " results -----");
+//            System.out.println("----- COMPLETE - " + results.size() + " results -----");
         }
     }
 

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/computationgraph/TestGraphLocalExecutionGenetic.java

@@ -165,7 +165,7 @@ public class TestGraphLocalExecutionGenetic extends BaseDL4JTest {
             List<ResultReference> results = runner.getResults();
             assertTrue(results.size() > 0);
 
-            System.out.println("----- COMPLETE - " + results.size() + " results -----");
+//            System.out.println("----- COMPLETE - " + results.size() + " results -----");
         }
     }
 

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestLayerSpace.java

@@ -101,7 +101,7 @@ public class TestLayerSpace extends BaseDL4JTest {
             double l2 = TestUtils.getL2(l);
             IActivation activation = l.getActivationFn();
 
-            System.out.println(lr + "\t" + l2 + "\t" + activation);
+//            System.out.println(lr + "\t" + l2 + "\t" + activation);
 
             assertTrue(lr >= 0.3 && lr <= 0.4);
             assertTrue(l2 >= 0.01 && l2 <= 0.1);
@@ -190,7 +190,7 @@ public class TestLayerSpace extends BaseDL4JTest {
             ActivationLayer al = als.getValue(d);
             IActivation activation = al.getActivationFn();
 
-            System.out.println(activation);
+//            System.out.println(activation);
 
             assertTrue(containsActivationFunction(actFns, activation));
         }
@@ -228,7 +228,7 @@ public class TestLayerSpace extends BaseDL4JTest {
             IActivation activation = el.getActivationFn();
             long nOut = el.getNOut();
 
-            System.out.println(activation + "\t" + nOut);
+//            System.out.println(activation + "\t" + nOut);
 
             assertTrue(containsActivationFunction(actFns, activation));
             assertTrue(nOut >= 10 && nOut <= 20);
@@ -295,7 +295,7 @@ public class TestLayerSpace extends BaseDL4JTest {
             long nOut = el.getNOut();
             double forgetGate = el.getForgetGateBiasInit();
 
-            System.out.println(activation + "\t" + nOut + "\t" + forgetGate);
+//            System.out.println(activation + "\t" + nOut + "\t" + forgetGate);
 
             assertTrue(containsActivationFunction(actFns, activation));
             assertTrue(nOut >= 10 && nOut <= 20);

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestMultiLayerSpace.java

@@ -293,8 +293,8 @@ public class TestMultiLayerSpace extends BaseDL4JTest {
             assertTrue(nLayerCounts[i] >= 5); //Expect approx equal (50/3 each), but some variation randomly
         }
 
-        System.out.println("Number of layers: " + Arrays.toString(nLayerCounts));
+//        System.out.println("Number of layers: " + Arrays.toString(nLayerCounts));
-        System.out.println("ReLU vs. Tanh: " + reluCount + "\t" + tanhCount);
+//        System.out.println("ReLU vs. Tanh: " + reluCount + "\t" + tanhCount);
 
     }
 

arbiter/arbiter-server/src/test/java/org/deeplearning4j/arbiter/server/ArbiterCLIRunnerTest.java

@@ -98,7 +98,8 @@ public class ArbiterCLIRunnerTest extends BaseDL4JTest {
         assertEquals(configuration,OptimizationConfiguration.fromJson(configuration.toJson()));
 
         FileUtils.writeStringToFile(new File(configPath),configuration.toJson());
-        System.out.println(configuration.toJson());
+//        System.out.println(configuration.toJson());
+        configuration.toJson();
 
         log.info("Starting test");
         cliRunner.runMain(

deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/data/TestGraphLoading.java

@@ -41,7 +41,7 @@ public class TestGraphLoading extends BaseDL4JTest {
 
         IGraph<String, String> graph = GraphLoader
                         .loadUndirectedGraphEdgeListFile(cpr.getTempFileFromArchive().getAbsolutePath(), 7, ",");
-        System.out.println(graph);
+//        System.out.println(graph);
 
         assertEquals(graph.numVertices(), 7);
         int[][] edges = {{1, 2}, {0, 2, 4}, {0, 1, 3, 4}, {2, 4, 5}, {1, 2, 3, 5, 6}, {3, 4, 6}, {4, 5}};
@@ -66,7 +66,7 @@ public class TestGraphLoading extends BaseDL4JTest {
                         edgeLineProcessor, vertexFactory, 10, false);
 
 
-        System.out.println(graph);
+//        System.out.println(graph);
 
         for (int i = 0; i < 10; i++) {
             List<Edge<String>> edges = graph.getEdgesOut(i);
@@ -111,7 +111,7 @@ public class TestGraphLoading extends BaseDL4JTest {
         Graph<String, String> graph = GraphLoader.loadGraph(verticesCPR.getTempFileFromArchive().getAbsolutePath(),
                         edgesCPR.getTempFileFromArchive().getAbsolutePath(), vertexLoader, edgeLineProcessor, false);
 
-        System.out.println(graph);
+//        System.out.println(graph);
 
         for (int i = 0; i < 10; i++) {
             List<Edge<String>> edges = graph.getEdgesOut(i);

deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/data/TestGraphLoadingWeighted.java

@@ -71,7 +71,7 @@ public class TestGraphLoadingWeighted extends BaseDL4JTest {
             }
         }
 
-        System.out.println(graph);
+//        System.out.println(graph);
     }
 
 

deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/graph/TestGraph.java

@@ -220,7 +220,7 @@ public class TestGraph extends BaseDL4JTest {
                 sum += transitionProb[i][j];
             for (int j = 0; j < transitionProb[i].length; j++)
                 transitionProb[i][j] /= sum;
-            System.out.println(Arrays.toString(transitionProb[i]));
+//            System.out.println(Arrays.toString(transitionProb[i]));
         }
 
         //Check that transition probs are essentially correct (within bounds of random variation)

deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/models/deepwalk/DeepWalkGradientCheck.java

@@ -145,8 +145,8 @@ public class DeepWalkGradientCheck extends BaseDL4JTest {
 
                         if (relError > MAX_REL_ERROR && absErr > MIN_ABS_ERROR)
                             fail(msg);
-                        else
+//                        else
-                            System.out.println(msg);
+//                            System.out.println(msg);
                     }
                 }
 
@@ -333,10 +333,10 @@ public class DeepWalkGradientCheck extends BaseDL4JTest {
 
                     if (relError > MAX_REL_ERROR && absErr > MIN_ABS_ERROR)
                         fail(msg);
-                    else
+//                    else
-                        System.out.println(msg);
+//                        System.out.println(msg);
                 }
-                System.out.println();
+//                System.out.println();
             }
 
         }

deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/models/deepwalk/TestDeepWalk.java

@@ -67,7 +67,7 @@ public class TestDeepWalk extends BaseDL4JTest {
         for (int i = 0; i < 7; i++) {
             INDArray vector = deepWalk.getVertexVector(i);
             assertArrayEquals(new long[] {vectorSize}, vector.shape());
-            System.out.println(Arrays.toString(vector.dup().data().asFloat()));
+//            System.out.println(Arrays.toString(vector.dup().data().asFloat()));
         }
 
         GraphWalkIterator<String> iter = new RandomWalkIterator<>(graph, 8);
@@ -77,11 +77,11 @@ public class TestDeepWalk extends BaseDL4JTest {
         for (int t = 0; t < 5; t++) {
             iter.reset();
             deepWalk.fit(iter);
-            System.out.println("--------------------");
+//            System.out.println("--------------------");
             for (int i = 0; i < 7; i++) {
                 INDArray vector = deepWalk.getVertexVector(i);
                 assertArrayEquals(new long[] {vectorSize}, vector.shape());
-                System.out.println(Arrays.toString(vector.dup().data().asFloat()));
+//                System.out.println(Arrays.toString(vector.dup().data().asFloat()));
             }
         }
     }
@@ -160,7 +160,7 @@ public class TestDeepWalk extends BaseDL4JTest {
                 continue;
 
             double sim = deepWalk.similarity(i, nearestTo);
-            System.out.println(i + "\t" + nearestTo + "\t" + sim);
+//            System.out.println(i + "\t" + nearestTo + "\t" + sim);
             assertTrue(sim <= minSimNearest);
         }
     }
@@ -211,7 +211,7 @@ public class TestDeepWalk extends BaseDL4JTest {
         Graph<String, String> graph = GraphLoader
                         .loadUndirectedGraphEdgeListFile(cpr.getTempFileFromArchive().getAbsolutePath(), 13, ",");
 
-        System.out.println(graph);
+//        System.out.println(graph);
 
         Nd4j.getRandom().setSeed(12345);
 
@@ -229,11 +229,13 @@ public class TestDeepWalk extends BaseDL4JTest {
 
         //Calculate similarity(0,i)
         for (int i = 0; i < nVertices; i++) {
-            System.out.println(deepWalk.similarity(0, i));
+//            System.out.println(deepWalk.similarity(0, i));
+            deepWalk.similarity(0, i);
         }
 
         for (int i = 0; i < nVertices; i++)
-            System.out.println(deepWalk.getVertexVector(i));
+//            System.out.println(deepWalk.getVertexVector(i));
+            deepWalk.getVertexVector(i);
     }
 
     @Test(timeout = 60000L)

deeplearning4j/deeplearning4j-graph/src/test/java/org/deeplearning4j/graph/models/deepwalk/TestGraphHuffman.java

@@ -38,9 +38,11 @@ public class TestGraphHuffman extends BaseDL4JTest {
 
         gh.buildTree(vertexDegrees);
 
-        for (int i = 0; i < 7; i++)
+        for (int i = 0; i < 7; i++) {
-            System.out.println(i + "\t" + gh.getCodeLength(i) + "\t" + gh.getCodeString(i) + "\t\t" + gh.getCode(i)
+            String s = i + "\t" + gh.getCodeLength(i) + "\t" + gh.getCodeString(i) + "\t\t" + gh.getCode(i)
-                            + "\t\t" + Arrays.toString(gh.getPathInnerNodes(i)));
+                    + "\t\t" + Arrays.toString(gh.getPathInnerNodes(i));
+//            System.out.println(s);
+        }
 
         int[] expectedLengths = {3, 2, 2, 5, 4, 2, 5};
         for (int i = 0; i < vertexDegrees.length; i++) {

deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper-parameter-server/src/test/java/org/deeplearning4j/parallelism/parameterserver/ParameterServerParallelWrapperTest.java

@@ -79,8 +79,9 @@ public class ParameterServerParallelWrapperTest extends BaseDL4JTest {
         model.init();
 
         ParallelWrapper parameterServerParallelWrapper =
-                        new ParallelWrapper.Builder(model).trainerFactory(new ParameterServerTrainerContext())
+                        new ParallelWrapper.Builder(model)
-                                        .workers(Runtime.getRuntime().availableProcessors())
+                                .workers(Math.min(4, Runtime.getRuntime().availableProcessors()))
+                                .trainerFactory(new ParameterServerTrainerContext())
                                         .reportScoreAfterAveraging(true).prefetchBuffer(3).build();
         parameterServerParallelWrapper.fit(mnistTrain);
 

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp-java8/src/test/java/org/deeplearning4j/spark/models/word2vec/SparkWord2VecTest.java

@@ -104,7 +104,7 @@ public class SparkWord2VecTest extends BaseDL4JTest {
         public void call(ExportContainer<VocabWord> v) throws Exception {
             assertNotNull(v.getElement());
             assertNotNull(v.getArray());
-            System.out.println(v.getElement() + " - " + v.getArray());
+//            System.out.println(v.getElement() + " - " + v.getArray());
         }
     }
 }

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/TestEarlyStoppingSpark.java

@@ -66,7 +66,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
 
         JavaRDD<DataSet> irisData = getIris();
@@ -119,7 +119,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MSE).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
         JavaRDD<DataSet> irisData = getIris();
         EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
@@ -155,7 +155,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
         JavaRDD<DataSet> irisData = getIris();
 
@@ -198,7 +198,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
         JavaRDD<DataSet> irisData = getIris();
 
@@ -231,7 +231,7 @@ public class TestEarlyStoppingSpark extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
 
         JavaRDD<DataSet> irisData = getIris();

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/TestEarlyStoppingSparkCompGraph.java

@@ -69,7 +69,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
 
         JavaRDD<DataSet> irisData = getIris();
@@ -120,7 +120,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MSE).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
         JavaRDD<DataSet> irisData = getIris();
         EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
@@ -158,7 +158,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
         JavaRDD<DataSet> irisData = getIris();
 
@@ -203,7 +203,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
         JavaRDD<DataSet> irisData = getIris();
 
@@ -238,7 +238,7 @@ public class TestEarlyStoppingSparkCompGraph extends BaseSparkTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.setListeners(new ScoreIterationListener(5));
 
 
         JavaRDD<DataSet> irisData = getIris();

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/data/TestShuffleExamples.java

@@ -59,7 +59,7 @@ public class TestShuffleExamples extends BaseSparkTest {
         int totalExampleCount = 0;
         for (DataSet ds : shuffledList) {
             totalExampleCount += ds.getFeatures().length();
-            System.out.println(Arrays.toString(ds.getFeatures().data().asFloat()));
+//            System.out.println(Arrays.toString(ds.getFeatures().data().asFloat()));
 
             assertEquals(ds.getFeatures(), ds.getLabels());
         }

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/datavec/TestExport.java

@@ -86,7 +86,7 @@ public class TestExport extends BaseSparkTest {
         for (File file : files) {
             if (!file.getPath().endsWith(".bin"))
                 continue;
-            System.out.println(file);
+//            System.out.println(file);
             DataSet ds = new DataSet();
             ds.load(file);
             assertEquals(minibatchSize, ds.numExamples());
@@ -144,7 +144,7 @@ public class TestExport extends BaseSparkTest {
         for (File file : files) {
             if (!file.getPath().endsWith(".bin"))
                 continue;
-            System.out.println(file);
+//            System.out.println(file);
             MultiDataSet ds = new org.nd4j.linalg.dataset.MultiDataSet();
             ds.load(file);
             assertEquals(minibatchSize, ds.getFeatures(0).size(0));

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/common/repartition/HashingBalancedPartitionerTest.java

@@ -92,9 +92,9 @@ public class HashingBalancedPartitionerTest extends BaseSparkTest {
 
         int[][] colorCountsByPartition = new int[3][2];
         for (final Tuple2<Tuple2<Long, Integer>, String> val : testList) {
-            System.out.println(val);
+//            System.out.println(val);
             Integer partition = hbp.getPartition(val._1());
-            System.out.println(partition);
+//            System.out.println(partition);
 
             if (val._2().equals("red"))
                 colorCountsByPartition[partition][0] += 1;
@@ -102,9 +102,9 @@ public class HashingBalancedPartitionerTest extends BaseSparkTest {
                 colorCountsByPartition[partition][1] += 1;
         }
 
-        for (int i = 0; i < 3; i++) {
+//        for (int i = 0; i < 3; i++) {
-            System.out.println(Arrays.toString(colorCountsByPartition[i]));
+//            System.out.println(Arrays.toString(colorCountsByPartition[i]));
-        }
+//        }
         for (int i = 0; i < 3; i++) {
             // avg red per partition : 2.33
             assertTrue(colorCountsByPartition[i][0] >= 1 && colorCountsByPartition[i][0] < 4);
@@ -178,12 +178,12 @@ public class HashingBalancedPartitionerTest extends BaseSparkTest {
                 colorCountsByPartition[partition][1] += 1;
         }
 
-        for (int i = 0; i < numPartitions; i++) {
+//        for (int i = 0; i < numPartitions; i++) {
-            System.out.println(Arrays.toString(colorCountsByPartition[i]));
+//            System.out.println(Arrays.toString(colorCountsByPartition[i]));
-        }
+//        }
-
+//
-        System.out.println("Ideal red # per partition: " + avgRed);
+//        System.out.println("Ideal red # per partition: " + avgRed);
-        System.out.println("Ideal blue # per partition: " + avgBlue);
+//        System.out.println("Ideal blue # per partition: " + avgBlue);
 
         for (int i = 0; i < numPartitions; i++) {
             // avg red per partition : 2.33

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/graph/TestSparkComputationGraph.java

@@ -115,7 +115,7 @@ public class TestSparkComputationGraph extends BaseSparkTest {
         TrainingMaster tm = new ParameterAveragingTrainingMaster(true, numExecutors(), 1, 10, 1, 0);
 
         SparkComputationGraph scg = new SparkComputationGraph(sc, cg, tm);
-        scg.setListeners(Collections.singleton((TrainingListener) new ScoreIterationListener(1)));
+        scg.setListeners(Collections.singleton((TrainingListener) new ScoreIterationListener(5)));
 
         JavaRDD<MultiDataSet> rdd = sc.parallelize(list);
         scg.fitMultiDataSet(rdd);

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/multilayer/TestSparkDl4jMultiLayer.java

@@ -31,8 +31,11 @@ import org.deeplearning4j.spark.impl.paramavg.ParameterAveragingTrainingMaster;
 import org.junit.Test;
 import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.buffer.DataType;
 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.learning.config.Nesterovs;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
@@ -45,8 +48,24 @@ import static org.junit.Assert.assertTrue;
 @Slf4j
 public class TestSparkDl4jMultiLayer extends BaseSparkTest {
 
-    @Test(timeout = 120000L)
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 120000L;
+    }
+
+    @Override
+    public DataType getDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Override
+    public DataType getDefaultFPDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Test
     public void testEvaluationSimple() throws Exception {
+        Nd4j.getRandom().setSeed(12345);
 
         for( int evalWorkers : new int[]{1, 4, 8}) {
             //Simple test to validate DL4J issue 4099 is fixed...
@@ -75,18 +94,18 @@ public class TestSparkDl4jMultiLayer extends BaseSparkTest {
             //----------------------------------
             //Create network configuration and conduct network training
             MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
+                    .dataType(DataType.FLOAT)
                     .seed(12345)
                     .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                     .activation(Activation.LEAKYRELU)
                     .weightInit(WeightInit.XAVIER)
-                    .updater(new Nesterovs(0.02, 0.9))
+                    .updater(new Adam(1e-3))
-                    .l2(1e-4)
+                    .l2(1e-5)
                     .list()
                     .layer(0, new DenseLayer.Builder().nIn(28 * 28).nOut(500).build())
                     .layer(1, new DenseLayer.Builder().nIn(500).nOut(100).build())
                     .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                             .activation(Activation.SOFTMAX).nIn(100).nOut(10).build())
-
                     .build();
 
             //Configuration for Spark training: see https://deeplearning4j.org/docs/latest/deeplearning4j-scaleout-howto for explanation of these configuration options

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/paramavg/TestCompareParameterAveragingSparkVsSingleMachine.java

@@ -333,15 +333,16 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
                     sparkNet.fit(rdd);
                 }
 
-                System.out.println(sparkNet.getSparkTrainingStats().statsAsString());
+//                System.out.println(sparkNet.getSparkTrainingStats().statsAsString());
+                sparkNet.getSparkTrainingStats().statsAsString();
 
                 INDArray finalSparkParams = sparkNet.getNetwork().params().dup();
 
-                System.out.println("Initial (Local) params:       " + Arrays.toString(initialParams.data().asFloat()));
+//                System.out.println("Initial (Local) params:       " + Arrays.toString(initialParams.data().asFloat()));
-                System.out.println("Initial (Spark) params:       "
+//                System.out.println("Initial (Spark) params:       "
-                                + Arrays.toString(initialSparkParams.data().asFloat()));
+//                                + Arrays.toString(initialSparkParams.data().asFloat()));
-                System.out.println("Final (Local) params: " + Arrays.toString(finalParams.data().asFloat()));
+//                System.out.println("Final (Local) params: " + Arrays.toString(finalParams.data().asFloat()));
-                System.out.println("Final (Spark) params: " + Arrays.toString(finalSparkParams.data().asFloat()));
+//                System.out.println("Final (Spark) params: " + Arrays.toString(finalSparkParams.data().asFloat()));
                 assertEquals(initialParams, initialSparkParams);
                 assertNotEquals(initialParams, finalParams);
                 assertEquals(finalParams, finalSparkParams);
@@ -405,15 +406,16 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
                     sparkNet.fit(rdd);
                 }
 
-                System.out.println(sparkNet.getSparkTrainingStats().statsAsString());
+//                System.out.println(sparkNet.getSparkTrainingStats().statsAsString());
+                sparkNet.getSparkTrainingStats().statsAsString();
 
                 INDArray finalSparkParams = sparkNet.getNetwork().params().dup();
 
-                System.out.println("Initial (Local) params:       " + Arrays.toString(initialParams.data().asFloat()));
+//                System.out.println("Initial (Local) params:       " + Arrays.toString(initialParams.data().asFloat()));
-                System.out.println("Initial (Spark) params:       "
+//                System.out.println("Initial (Spark) params:       "
-                                + Arrays.toString(initialSparkParams.data().asFloat()));
+//                                + Arrays.toString(initialSparkParams.data().asFloat()));
-                System.out.println("Final (Local) params: " + Arrays.toString(finalParams.data().asFloat()));
+//                System.out.println("Final (Local) params: " + Arrays.toString(finalParams.data().asFloat()));
-                System.out.println("Final (Spark) params: " + Arrays.toString(finalSparkParams.data().asFloat()));
+//                System.out.println("Final (Spark) params: " + Arrays.toString(finalSparkParams.data().asFloat()));
                 assertArrayEquals(initialParams.data().asFloat(), initialSparkParams.data().asFloat(), 1e-8f);
                 assertArrayEquals(finalParams.data().asFloat(), finalSparkParams.data().asFloat(), 1e-6f);
 
@@ -478,18 +480,19 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
                     sparkNet.fit(rdd);
                 }
 
-                System.out.println(sparkNet.getSparkTrainingStats().statsAsString());
+//                System.out.println(sparkNet.getSparkTrainingStats().statsAsString());
+                sparkNet.getSparkTrainingStats().statsAsString();
 
                 INDArray finalSparkParams = sparkNet.getNetwork().params().dup();
                 //                executioner.addToWatchdog(finalSparkParams, "finalSparkParams");
 
                 float[] fp = finalParams.data().asFloat();
                 float[] fps = finalSparkParams.data().asFloat();
-                System.out.println("Initial (Local) params:       " + Arrays.toString(initialParams.data().asFloat()));
+//                System.out.println("Initial (Local) params:       " + Arrays.toString(initialParams.data().asFloat()));
-                System.out.println("Initial (Spark) params:       "
+//                System.out.println("Initial (Spark) params:       "
-                                + Arrays.toString(initialSparkParams.data().asFloat()));
+//                                + Arrays.toString(initialSparkParams.data().asFloat()));
-                System.out.println("Final (Local) params: " + Arrays.toString(fp));
+//                System.out.println("Final (Local) params: " + Arrays.toString(fp));
-                System.out.println("Final (Spark) params: " + Arrays.toString(fps));
+//                System.out.println("Final (Spark) params: " + Arrays.toString(fps));
 
                 assertEquals(initialParams, initialSparkParams);
                 assertNotEquals(initialParams, finalParams);
@@ -551,14 +554,15 @@ public class TestCompareParameterAveragingSparkVsSingleMachine {
                     sparkNet.fit(rdd);
                 }
 
-                System.out.println(sparkNet.getSparkTrainingStats().statsAsString());
+//                System.out.println(sparkNet.getSparkTrainingStats().statsAsString());
+                sparkNet.getSparkTrainingStats().statsAsString();
 
                 INDArray finalSparkParams = sparkNet.getNetwork().params().dup();
 
-                System.out.println("Initial (Local) params:  " + Arrays.toString(initialParams.data().asFloat()));
+//                System.out.println("Initial (Local) params:  " + Arrays.toString(initialParams.data().asFloat()));
-                System.out.println("Initial (Spark) params:  " + Arrays.toString(initialSparkParams.data().asFloat()));
+//                System.out.println("Initial (Spark) params:  " + Arrays.toString(initialSparkParams.data().asFloat()));
-                System.out.println("Final (Local) params:    " + Arrays.toString(finalParams.data().asFloat()));
+//                System.out.println("Final (Local) params:    " + Arrays.toString(finalParams.data().asFloat()));
-                System.out.println("Final (Spark) params:    " + Arrays.toString(finalSparkParams.data().asFloat()));
+//                System.out.println("Final (Spark) params:    " + Arrays.toString(finalSparkParams.data().asFloat()));
                 assertArrayEquals(initialParams.data().asFloat(), initialSparkParams.data().asFloat(), 1e-8f);
                 assertArrayEquals(finalParams.data().asFloat(), finalSparkParams.data().asFloat(), 1e-6f);
 

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/paramavg/TestJsonYaml.java

@@ -37,7 +37,7 @@ public class TestJsonYaml {
         String json = tm.toJson();
         String yaml = tm.toYaml();
 
-        System.out.println(json);
+//        System.out.println(json);
 
         TrainingMaster fromJson = ParameterAveragingTrainingMaster.fromJson(json);
         TrainingMaster fromYaml = ParameterAveragingTrainingMaster.fromYaml(yaml);

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/paramavg/TestSparkMultiLayerParameterAveraging.java

@@ -389,7 +389,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         List<EventStats> workerFitStats = stats.getValue("ParameterAveragingWorkerFitTimesMs");
         for (EventStats e : workerFitStats) {
             ExampleCountEventStats eces = (ExampleCountEventStats) e;
-            System.out.println(eces.getTotalExampleCount());
+//            System.out.println(eces.getTotalExampleCount());
         }
 
         for (EventStats e : workerFitStats) {
@@ -457,7 +457,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         assertNotEquals(paramsBefore, paramsAfter);
 
         SparkTrainingStats stats = sparkNet.getSparkTrainingStats();
-        System.out.println(stats.statsAsString());
+//        System.out.println(stats.statsAsString());
+        stats.statsAsString();
 
         sparkNet.getTrainingMaster().deleteTempFiles(sc);
     }
@@ -483,7 +484,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
             i++;
         }
 
-        System.out.println("Saved to: " + tempDirF.getAbsolutePath());
+//        System.out.println("Saved to: " + tempDirF.getAbsolutePath());
 
 
 
@@ -527,7 +528,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         SparkTrainingStats stats = sparkNet.getSparkTrainingStats();
 
         //Expect
-        System.out.println(stats.statsAsString());
+//        System.out.println(stats.statsAsString());
+        stats.statsAsString();
         assertEquals(numSplits, stats.getValue("ParameterAveragingMasterRepartitionTimesMs").size());
 
         List<EventStats> list = stats.getValue("ParameterAveragingWorkerFitTimesMs");
@@ -566,8 +568,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
             i++;
         }
 
-        System.out.println("Saved to: " + tempDirF.getAbsolutePath());
+//        System.out.println("Saved to: " + tempDirF.getAbsolutePath());
-        System.out.println("Saved to: " + tempDirF2.getAbsolutePath());
+//        System.out.println("Saved to: " + tempDirF2.getAbsolutePath());
 
 
 
@@ -610,7 +612,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         assertNotEquals(paramsBefore, paramsAfter);
 
         SparkTrainingStats stats = sparkNet.getSparkTrainingStats();
-        System.out.println(stats.statsAsString());
+//        System.out.println(stats.statsAsString());
+        stats.statsAsString();
 
         //Same thing, buf for MultiDataSet objects:
         config = new Configuration();
@@ -631,7 +634,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         assertNotEquals(paramsBefore, paramsAfter);
 
         stats = sparkNet.getSparkTrainingStats();
-        System.out.println(stats.statsAsString());
+//        System.out.println(stats.statsAsString());
+        stats.statsAsString();
     }
 
 
@@ -730,13 +734,13 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
                         .build();
 
         for (int avgFreq : new int[] {1, 5, 10}) {
-            System.out.println("--- Avg freq " + avgFreq + " ---");
+//            System.out.println("--- Avg freq " + avgFreq + " ---");
             SparkDl4jMultiLayer sparkNet = new SparkDl4jMultiLayer(sc, conf.clone(),
                             new ParameterAveragingTrainingMaster.Builder(numExecutors(), dataSetObjSize)
                                             .batchSizePerWorker(batchSizePerExecutor).averagingFrequency(avgFreq)
                                             .repartionData(Repartition.Always).build());
 
-            sparkNet.setListeners(new ScoreIterationListener(1));
+            sparkNet.setListeners(new ScoreIterationListener(5));
 
 
 
@@ -778,13 +782,13 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
                         .setOutputs("1").build();
 
         for (int avgFreq : new int[] {1, 5, 10}) {
-            System.out.println("--- Avg freq " + avgFreq + " ---");
+//            System.out.println("--- Avg freq " + avgFreq + " ---");
             SparkComputationGraph sparkNet = new SparkComputationGraph(sc, conf.clone(),
                             new ParameterAveragingTrainingMaster.Builder(numExecutors(), dataSetObjSize)
                                             .batchSizePerWorker(batchSizePerExecutor).averagingFrequency(avgFreq)
                                             .repartionData(Repartition.Always).build());
 
-            sparkNet.setListeners(new ScoreIterationListener(1));
+            sparkNet.setListeners(new ScoreIterationListener(5));
 
             JavaRDD<DataSet> rdd = sc.parallelize(list);
 

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/stats/TestTrainingStatsCollection.java

@@ -107,7 +107,7 @@ public class TestTrainingStatsCollection extends BaseSparkTest {
                 expectedStatNames.addAll(c);
             }
 
-            System.out.println(expectedStatNames);
+//            System.out.println(expectedStatNames);
 
 
             SparkTrainingStats stats = sparkNet.getSparkTrainingStats();
@@ -119,7 +119,7 @@ public class TestTrainingStatsCollection extends BaseSparkTest {
             }
 
             String statsAsString = stats.statsAsString();
-            System.out.println(statsAsString);
+//            System.out.println(statsAsString);
             assertEquals(actualKeySet.size(), statsAsString.split("\n").length); //One line per stat
 
 

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/time/TestTimeSource.java

@@ -35,7 +35,7 @@ public class TestTimeSource {
             long systemTime = System.currentTimeMillis();
             long ntpTime = timeSource.currentTimeMillis();
             long offset = ntpTime - systemTime;
-            System.out.println("System: " + systemTime + "\tNTPTimeSource: " + ntpTime + "\tOffset: " + offset);
+//            System.out.println("System: " + systemTime + "\tNTPTimeSource: " + ntpTime + "\tOffset: " + offset);
             Thread.sleep(500);
         }
     }
@@ -49,7 +49,7 @@ public class TestTimeSource {
             long systemTime = System.currentTimeMillis();
             long ntpTime = timeSource.currentTimeMillis();
             long offset = ntpTime - systemTime;
-            System.out.println("System: " + systemTime + "\tSystemClockTimeSource: " + ntpTime + "\tOffset: " + offset);
+//            System.out.println("System: " + systemTime + "\tSystemClockTimeSource: " + ntpTime + "\tOffset: " + offset);
             assertEquals(systemTime, ntpTime, 2); //Should be exact, but we might randomly tick over between one ms and the next
             Thread.sleep(500);
         }

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/ui/TestListeners.java

@@ -87,7 +87,7 @@ public class TestListeners extends BaseSparkTest {
         net.fit(rdd);
 
         List<String> sessions = ss.listSessionIDs();
-        System.out.println("Sessions: " + sessions);
+//        System.out.println("Sessions: " + sessions);
         assertEquals(1, sessions.size());
 
         String sid = sessions.get(0);
@@ -95,15 +95,15 @@ public class TestListeners extends BaseSparkTest {
         List<String> typeIDs = ss.listTypeIDsForSession(sid);
         List<String> workers = ss.listWorkerIDsForSession(sid);
 
-        System.out.println(sid + "\t" + typeIDs + "\t" + workers);
+//        System.out.println(sid + "\t" + typeIDs + "\t" + workers);
 
         List<Persistable> lastUpdates = ss.getLatestUpdateAllWorkers(sid, StatsListener.TYPE_ID);
-        System.out.println(lastUpdates);
+//        System.out.println(lastUpdates);
 
-        System.out.println("Static info:");
+//        System.out.println("Static info:");
         for (String wid : workers) {
             Persistable staticInfo = ss.getStaticInfo(sid, StatsListener.TYPE_ID, wid);
-            System.out.println(sid + "\t" + wid);
+//            System.out.println(sid + "\t" + wid);
         }
 
         assertEquals(1, typeIDs.size());

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/util/TestRepartitioning.java

@@ -63,7 +63,7 @@ public class TestRepartitioning extends BaseSparkTest {
         assertEquals(10, rdd2.partitions().size());
         for (int i = 0; i < 10; i++) {
             List<String> partition = rdd2.collectPartitions(new int[] {i})[0];
-            System.out.println("Partition " + i + " size: " + partition.size());
+//            System.out.println("Partition " + i + " size: " + partition.size());
             assertEquals(100, partition.size()); //Should be exactly 100, for the util method (but NOT spark .repartition)
         }
     }
@@ -170,7 +170,7 @@ public class TestRepartitioning extends BaseSparkTest {
 
         List<Tuple2<Integer, Integer>> partitionCounts = initial.values().mapPartitionsWithIndex(new CountPartitionsFunction<Integer>(), true).collect();
 
-        System.out.println(partitionCounts);
+//        System.out.println(partitionCounts);
 
         List<Tuple2<Integer,Integer>> initialExpected = Arrays.asList(
                 new Tuple2<>(0,29),
@@ -185,7 +185,7 @@ public class TestRepartitioning extends BaseSparkTest {
 
         JavaRDD<Integer> afterRepartition = SparkUtils.repartitionBalanceIfRequired(initial.values(), Repartition.Always, 2, 112);
         List<Tuple2<Integer, Integer>> partitionCountsAfter = afterRepartition.mapPartitionsWithIndex(new CountPartitionsFunction<Integer>(), true).collect();
-        System.out.println(partitionCountsAfter);
+//        System.out.println(partitionCountsAfter);
 
         for(Tuple2<Integer,Integer> t2 : partitionCountsAfter){
             assertEquals(2, (int)t2._2());
@@ -219,8 +219,8 @@ public class TestRepartitioning extends BaseSparkTest {
             }
         }
 
-        System.out.println("min: " + min + "\t@\t" + minIdx);
+//        System.out.println("min: " + min + "\t@\t" + minIdx);
-        System.out.println("max: " + max + "\t@\t" + maxIdx);
+//        System.out.println("max: " + max + "\t@\t" + maxIdx);
 
         assertEquals(1, min);
         assertEquals(2, max);
@@ -244,7 +244,7 @@ public class TestRepartitioning extends BaseSparkTest {
 
         for (int i = 0; i < 10; i++) {
             List<String> partition = rdd2.collectPartitions(new int[] {i})[0];
-            System.out.println("Partition " + i + " size: " + partition.size());
+//            System.out.println("Partition " + i + " size: " + partition.size());
             assertTrue(partition.size() >= 90 && partition.size() <= 110);
         }
     }

nd4s/src/test/scala/org/nd4s/NDArrayExtractionTest.scala

@@ -123,7 +123,7 @@ trait NDArrayExtractionTestBase extends FlatSpec { self: OrderingForTest =>
     val expectedSlice = expectedArray.slice(0)
     val actualSlice = expectedArray(0, ->)
 
-    Console.println(expectedSlice)
+//    Console.println(expectedSlice)
 
     assert(actualSlice == expectedSlice)
   }

nd4s/src/test/scala/org/nd4s/samediff/TrainingTest.scala

@@ -28,7 +28,7 @@ class TrainingTest extends FlatSpec with Matchers {
       val unused3 = unused1.div(unused2)
       val loss1 = add.std("l1", true)
       val loss2 = mmul.mean("l2")
-      Console.println(sd.summary)
+//      Console.println(sd.summary)
       if (i == 0) {
         sd.setLossVariables("l1", "l2")
         sd.createGradFunction()

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/learning/HistoryProcessorTest.java

@@ -43,8 +43,8 @@ public class HistoryProcessorTest {
         hp.add(a);
         INDArray[] h = hp.getHistory();
         assertEquals(4, h.length);
-        System.out.println(Arrays.toString(a.shape()));
+//        System.out.println(Arrays.toString(a.shape()));
-        System.out.println(Arrays.toString(h[0].shape()));
+//        System.out.println(Arrays.toString(h[0].shape()));
         assertEquals(           1, h[0].shape()[0]);
         assertEquals(a.shape()[0], h[0].shape()[1]);
         assertEquals(a.shape()[1], h[0].shape()[2]);

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/network/ac/ActorCriticTest.java

@@ -100,8 +100,8 @@ public class ActorCriticTest {
                 double error2 = gradient2 - gradient.getDouble(1);
                 double relError1 = error1 / gradient.getDouble(0);
                 double relError2 = error2 / gradient.getDouble(1);
-                System.out.println(gradient.getDouble(0) + "  " + gradient1 + " " + relError1);
+//                System.out.println(gradient.getDouble(0) + "  " + gradient1 + " " + relError1);
-                System.out.println(gradient.getDouble(1) + "  " + gradient2 + " " + relError2);
+//                System.out.println(gradient.getDouble(1) + "  " + gradient2 + " " + relError2);
                 assertTrue(gradient.getDouble(0) < maxRelError || Math.abs(relError1) < maxRelError);
                 assertTrue(gradient.getDouble(1) < maxRelError || Math.abs(relError2) < maxRelError);
             }

rl4j/rl4j-core/src/test/java/org/deeplearning4j/rl4j/policy/PolicyTest.java

@@ -158,7 +158,7 @@ public class PolicyTest {
         for (int i = 0; i < 100; i++) {
             count[policy.nextAction(input)]++;
         }
-        System.out.println(count[0] + " " + count[1] + " " + count[2] + " " + count[3]);
+//        System.out.println(count[0] + " " + count[1] + " " + count[2] + " " + count[3]);
         assertTrue(count[0] < 20);
         assertTrue(count[1] < 30);
         assertTrue(count[2] < 40);