Unit/integration test split + test speedup (#166)

* Add maven profile + base tests methods for integration tests

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

* Switch from system property to environment variable; seems more reliable in intellij

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

* Add nd4j-common-tests module, and common base test; cleanup

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

* Ensure all ND4J tests extend BaseND4JTest

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

* Test spam reduction, import fix

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

* Add test logging to nd4j-aeron

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

* Fix unintended change

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

* Reduce sprint test log spam

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

* More test spam cleanup

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

* Significantly speed up TSNE tests

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

* W2V iterator test unit/integration split

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

* More NLP test speedups

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

* Avoid debug/verbose mode leaking between tests

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

* test tweak

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

* Arbiter extends base DL4J test

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

* Arbiter test speedup

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

* nlp-uima test speedup

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

* More test speedups

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

* Fix ND4J base test

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

* Few small ND4J test speed improvements

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

* DL4J tests speedup

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

* More tweaks

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

* Even more test speedups

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

* More tweaks

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

* Various test fixes

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

* More test fixes

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

* Add ability to specify number of threads for C++ ops in BaseDL4JTest and BaseND4JTest

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

* nd4j-aeron test profile fix for CUDA

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

arbiter/arbiter-core/pom.xml

@@ -84,6 +84,13 @@
             <artifactId>jackson</artifactId>
             <version>${nd4j.version}</version>
         </dependency>
+
+        <dependency>
+            <groupId>org.deeplearning4j</groupId>
+            <artifactId>deeplearning4j-common-tests</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
     </dependencies>
 
     <profiles>

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.api.CandidateGenerator;
 import org.deeplearning4j.arbiter.optimize.api.score.ScoreFunction;
 import org.deeplearning4j.arbiter.optimize.api.termination.MaxCandidatesCondition;
@@ -32,7 +33,7 @@ import org.deeplearning4j.arbiter.optimize.runner.listener.impl.LoggingStatusLis
 import org.junit.Assert;
 import org.junit.Test;
 
-public class TestGeneticSearch {
+public class TestGeneticSearch extends BaseDL4JTest {
     public class TestSelectionOperator extends SelectionOperator {
 
         @Override

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.api.CandidateGenerator;
 import org.deeplearning4j.arbiter.optimize.api.data.DataSetIteratorFactoryProvider;
 import org.deeplearning4j.arbiter.optimize.generator.GridSearchCandidateGenerator;
@@ -26,7 +27,7 @@ import java.util.Map;
 
 import static org.junit.Assert.*;
 
-public class TestGridSearch {
+public class TestGridSearch extends BaseDL4JTest {
 
     @Test
     public void testIndexing() {

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

@@ -19,6 +19,7 @@ package org.deeplearning4j.arbiter.optimize;
 import org.apache.commons.math3.distribution.LogNormalDistribution;
 import org.apache.commons.math3.distribution.NormalDistribution;
 import org.apache.commons.math3.distribution.UniformIntegerDistribution;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.api.CandidateGenerator;
 import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
 import org.deeplearning4j.arbiter.optimize.api.data.DataSetIteratorFactoryProvider;
@@ -49,7 +50,7 @@ import static org.junit.Assert.assertEquals;
 /**
  * Created by Alex on 02/02/2017.
  */
-public class TestJson {
+public class TestJson extends BaseDL4JTest {
 
     protected static ObjectMapper getObjectMapper(JsonFactory factory) {
         ObjectMapper om = new ObjectMapper(factory);

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.api.CandidateGenerator;
 import org.deeplearning4j.arbiter.optimize.api.data.DataSetIteratorFactoryProvider;
 import org.deeplearning4j.arbiter.optimize.api.termination.MaxCandidatesCondition;
@@ -34,7 +35,7 @@ import java.util.Map;
  * Test random search on the Branin Function:
  * http://www.sfu.ca/~ssurjano/branin.html
  */
-public class TestRandomSearch {
+public class TestRandomSearch extends BaseDL4JTest {
 
     @Test
     public void test() throws Exception {

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/distribution/TestLogUniform.java

@@ -17,12 +17,13 @@
 package org.deeplearning4j.arbiter.optimize.distribution;
 
 import org.apache.commons.math3.distribution.RealDistribution;
+import org.deeplearning4j.BaseDL4JTest;
 import org.junit.Test;
 
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
-public class TestLogUniform {
+public class TestLogUniform extends BaseDL4JTest {
 
     @Test
     public void testSimple(){

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
 import org.apache.commons.math3.random.RandomGenerator;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.ArithmeticCrossover;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverResult;
 import org.deeplearning4j.arbiter.optimize.genetic.TestParentSelection;
@@ -24,7 +25,7 @@ import org.deeplearning4j.arbiter.optimize.genetic.TestRandomGenerator;
 import org.junit.Assert;
 import org.junit.Test;
 
-public class ArithmeticCrossoverTests {
+public class ArithmeticCrossoverTests extends BaseDL4JTest {
 
     @Test
     public void ArithmeticCrossover_Crossover_OutsideCrossoverRate_ShouldReturnParent0() {

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationInitializer;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
 import org.deeplearning4j.arbiter.optimize.genetic.TestCrossoverOperator;
@@ -23,7 +24,7 @@ import org.deeplearning4j.arbiter.optimize.genetic.TestPopulationInitializer;
 import org.junit.Assert;
 import org.junit.Test;
 
-public class CrossoverOperatorTests {
+public class CrossoverOperatorTests extends BaseDL4JTest {
 
     @Test
     public void CrossoverOperator_initializeInstance_ShouldInitPopulationModel() throws IllegalAccessException {

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
 import org.apache.commons.math3.random.RandomGenerator;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.utils.CrossoverPointsGenerator;
 import org.deeplearning4j.arbiter.optimize.genetic.TestRandomGenerator;
 import org.junit.Assert;
@@ -24,7 +25,7 @@ import org.junit.Test;
 
 import java.util.Deque;
 
-public class CrossoverPointsGeneratorTests {
+public class CrossoverPointsGeneratorTests extends BaseDL4JTest {
 
     @Test
     public void CrossoverPointsGenerator_FixedNumberCrossovers() {

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
 import org.apache.commons.math3.random.RandomGenerator;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverResult;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.KPointCrossover;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.TwoParentSelection;
@@ -25,7 +26,7 @@ import org.deeplearning4j.arbiter.optimize.genetic.TestRandomGenerator;
 import org.junit.Assert;
 import org.junit.Test;
 
-public class KPointCrossoverTests {
+public class KPointCrossoverTests extends BaseDL4JTest {
 
     @Test
     public void KPointCrossover_BelowCrossoverRate_ShouldReturnParent0() {

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
 import org.deeplearning4j.arbiter.optimize.genetic.TestParentSelection;
 import org.junit.Assert;
@@ -24,7 +25,7 @@ import org.junit.Test;
 import java.util.ArrayList;
 import java.util.List;
 
-public class ParentSelectionTests {
+public class ParentSelectionTests extends BaseDL4JTest {
 
     @Test
     public void ParentSelection_InitializeInstance_ShouldInitPopulation() {

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
 import org.apache.commons.math3.random.RandomGenerator;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.RandomTwoParentSelection;
 import org.deeplearning4j.arbiter.optimize.genetic.TestRandomGenerator;
@@ -26,7 +27,7 @@ import org.junit.Test;
 import java.util.ArrayList;
 import java.util.List;
 
-public class RandomTwoParentSelectionTests {
+public class RandomTwoParentSelectionTests extends BaseDL4JTest {
     @Test
     public void RandomTwoParentSelection_ShouldReturnTwoDifferentParents() {
         RandomGenerator rng = new TestRandomGenerator(new int[] {1, 1, 1, 0}, null);

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
 import org.apache.commons.math3.random.RandomGenerator;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverResult;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.SinglePointCrossover;
 import org.deeplearning4j.arbiter.optimize.genetic.TestParentSelection;
@@ -24,7 +25,7 @@ import org.deeplearning4j.arbiter.optimize.genetic.TestRandomGenerator;
 import org.junit.Assert;
 import org.junit.Test;
 
-public class SinglePointCrossoverTests {
+public class SinglePointCrossoverTests extends BaseDL4JTest {
     @Test
     public void SinglePointCrossover_BelowCrossoverRate_ShouldReturnParent0() {
         RandomGenerator rng = new TestRandomGenerator(null, new double[] {1.0});

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.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverResult;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.TwoParentsCrossoverOperator;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.parentselection.TwoParentSelection;
@@ -27,7 +28,7 @@ import org.junit.Assert;
 import org.junit.Test;
 import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
-public class TwoParentsCrossoverOperatorTests {
+public class TwoParentsCrossoverOperatorTests extends BaseDL4JTest {
 
     class TestTwoParentsCrossoverOperator extends TwoParentsCrossoverOperator {
 

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.optimize.genetic.crossover;
 
 import org.apache.commons.math3.random.RandomGenerator;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverResult;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.UniformCrossover;
 import org.deeplearning4j.arbiter.optimize.genetic.TestParentSelection;
@@ -24,7 +25,7 @@ import org.deeplearning4j.arbiter.optimize.genetic.TestRandomGenerator;
 import org.junit.Assert;
 import org.junit.Test;
 
-public class UniformCrossoverTests {
+public class UniformCrossoverTests extends BaseDL4JTest {
 
     @Test
     public void UniformCrossover_BelowCrossoverRate_ShouldReturnParent0() {

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.culling;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.culling.LeastFitCullOperator;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationInitializer;
@@ -27,7 +28,7 @@ import org.junit.Test;
 import java.util.ArrayList;
 import java.util.List;
 
-public class LeastFitCullOperatorTests {
+public class LeastFitCullOperatorTests extends BaseDL4JTest {
 
     @Test
     public void LeastFitCullingOperation_ShouldCullLastElements() {

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.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.culling.RatioCullOperator;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationInitializer;
@@ -27,7 +28,7 @@ import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
 import java.util.List;
 
-public class RatioCullOperatorTests {
+public class RatioCullOperatorTests extends BaseDL4JTest {
 
     class TestRatioCullOperator extends RatioCullOperator {
 

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.mutation;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.mutation.RandomMutationOperator;
 import org.deeplearning4j.arbiter.optimize.genetic.TestRandomGenerator;
 import org.junit.Assert;
@@ -24,7 +25,7 @@ import org.junit.Test;
 import java.lang.reflect.Field;
 import java.util.Arrays;
 
-public class RandomMutationOperatorTests {
+public class RandomMutationOperatorTests extends BaseDL4JTest {
     @Test
     public void RandomMutationOperator_DefaultBuild_ShouldNotBeNull() {
         RandomMutationOperator sut = new RandomMutationOperator.Builder().build();

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize.genetic.population;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.Chromosome;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.culling.CullOperator;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationInitializer;
@@ -27,7 +28,7 @@ import org.junit.Test;
 
 import java.util.List;
 
-public class PopulationModelTests {
+public class PopulationModelTests extends BaseDL4JTest {
 
     private class TestCullOperator implements CullOperator {
 

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.optimize.genetic.selection;
 
 import org.apache.commons.math3.random.RandomGenerator;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.ChromosomeFactory;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverOperator;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.crossover.CrossoverResult;
@@ -36,7 +37,7 @@ import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
 import static org.junit.Assert.assertArrayEquals;
 
-public class GeneticSelectionOperatorTests {
+public class GeneticSelectionOperatorTests extends BaseDL4JTest {
 
     private class TestCullOperator implements CullOperator {
 

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.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.ChromosomeFactory;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationInitializer;
 import org.deeplearning4j.arbiter.optimize.generator.genetic.population.PopulationModel;
@@ -25,7 +26,7 @@ import org.junit.Assert;
 import org.junit.Test;
 import sun.reflect.generics.reflectiveObjects.NotImplementedException;
 
-public class SelectionOperatorTests {
+public class SelectionOperatorTests extends BaseDL4JTest {
     private class TestSelectionOperator extends SelectionOperator {
 
         public PopulationModel getPopulationModel() {

arbiter/arbiter-core/src/test/java/org/deeplearning4j/arbiter/optimize/parameter/TestParameterSpaces.java

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.optimize.parameter;
 
 import org.apache.commons.math3.distribution.NormalDistribution;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.optimize.api.ParameterSpace;
 import org.deeplearning4j.arbiter.optimize.parameter.continuous.ContinuousParameterSpace;
 import org.deeplearning4j.arbiter.optimize.parameter.discrete.DiscreteParameterSpace;
@@ -25,7 +26,7 @@ import org.junit.Test;
 
 import static org.junit.Assert.assertEquals;
 
-public class TestParameterSpaces {
+public class TestParameterSpaces extends BaseDL4JTest {
 
 
     @Test

arbiter/arbiter-deeplearning4j/pom.xml

@@ -63,6 +63,13 @@
             <artifactId>gson</artifactId>
             <version>${gson.version}</version>
         </dependency>
+
+        <dependency>
+            <groupId>org.deeplearning4j</groupId>
+            <artifactId>deeplearning4j-common-tests</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
     </dependencies>
 
     <profiles>

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.computationgraph;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.ComputationGraphSpace;
 import org.deeplearning4j.arbiter.TestUtils;
 import org.deeplearning4j.arbiter.conf.updater.SgdSpace;
@@ -44,7 +45,7 @@ import java.util.Random;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
-public class TestComputationGraphSpace {
+public class TestComputationGraphSpace extends BaseDL4JTest {
 
     @Test
     public void testBasic() {

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.computationgraph;
 
 import lombok.extern.slf4j.Slf4j;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.ComputationGraphSpace;
 import org.deeplearning4j.arbiter.conf.updater.AdamSpace;
 import org.deeplearning4j.arbiter.conf.updater.SgdSpace;
@@ -85,7 +86,7 @@ import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
 @Slf4j
-public class TestGraphLocalExecution {
+public class TestGraphLocalExecution extends BaseDL4JTest {
 
     @Rule
     public TemporaryFolder testDir = new TemporaryFolder();
@@ -126,7 +127,7 @@ public class TestGraphLocalExecution {
             if(dataApproach == 0){
                 ds = TestDL4JLocalExecution.MnistDataSource.class;
                 dsP = new Properties();
-                dsP.setProperty("minibatch", "8");
+                dsP.setProperty("minibatch", "2");
                 candidateGenerator = new RandomSearchGenerator(mls);
             } else if(dataApproach == 1) {
                 //DataProvider approach
@@ -150,8 +151,8 @@ public class TestGraphLocalExecution {
                     .dataSource(ds, dsP)
                     .modelSaver(new FileModelSaver(modelSave))
                     .scoreFunction(new TestSetLossScoreFunction())
-                    .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES),
+                    .terminationConditions(new MaxTimeCondition(5, TimeUnit.SECONDS),
-                            new MaxCandidatesCondition(5))
+                            new MaxCandidatesCondition(3))
                     .build();
 
             IOptimizationRunner runner = new LocalOptimizationRunner(configuration,new ComputationGraphTaskCreator(new ClassificationEvaluator()));
@@ -159,7 +160,7 @@ public class TestGraphLocalExecution {
             runner.execute();
 
             List<ResultReference> results = runner.getResults();
-            assertEquals(5, results.size());
+            assertTrue(results.size() > 0);
 
             System.out.println("----- COMPLETE - " + results.size() + " results -----");
         }
@@ -203,8 +204,8 @@ public class TestGraphLocalExecution {
         OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
                 .candidateGenerator(candidateGenerator).dataProvider(dataProvider)
                 .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(ScoreFunctions.testSetLoss(true))
-                .terminationConditions(new MaxTimeCondition(20, TimeUnit.SECONDS),
+                .terminationConditions(new MaxTimeCondition(5, TimeUnit.SECONDS),
-                        new MaxCandidatesCondition(10))
+                        new MaxCandidatesCondition(3))
                 .build();
 
         IOptimizationRunner runner = new LocalOptimizationRunner(configuration,
@@ -223,7 +224,7 @@ public class TestGraphLocalExecution {
                 .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                 .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.1)))
                 .l2(new ContinuousParameterSpace(0.0001, 0.01)).addInputs("in")
-                .setInputTypes(InputType.feedForward(4))
+                .setInputTypes(InputType.feedForward(784))
                 .addLayer("layer0",
                         new DenseLayerSpace.Builder().nIn(784).nOut(new IntegerParameterSpace(2, 10))
                                 .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
@@ -250,8 +251,8 @@ public class TestGraphLocalExecution {
                 .candidateGenerator(candidateGenerator)
                 .dataProvider(new TestMdsDataProvider(1, 32))
                 .modelSaver(new FileModelSaver(modelSavePath)).scoreFunction(ScoreFunctions.testSetLoss(true))
-                .terminationConditions(new MaxTimeCondition(20, TimeUnit.SECONDS),
+                .terminationConditions(new MaxTimeCondition(5, TimeUnit.SECONDS),
-                        new MaxCandidatesCondition(10))
+                        new MaxCandidatesCondition(3))
                 .scoreFunction(ScoreFunctions.testSetAccuracy())
                 .build();
 
@@ -279,7 +280,7 @@ public class TestGraphLocalExecution {
         @Override
         public Object trainData(Map<String, Object> dataParameters) {
             try {
-                DataSetIterator underlying = new MnistDataSetIterator(batchSize, Math.min(60000, 10 * batchSize), false, true, true, 12345);
+                DataSetIterator underlying = new MnistDataSetIterator(batchSize, Math.min(60000, 3 * batchSize), false, true, true, 12345);
                 return new MultiDataSetIteratorAdapter(new MultipleEpochsIterator(numEpochs, underlying));
             } catch (IOException e) {
                 throw new RuntimeException(e);
@@ -289,7 +290,7 @@ public class TestGraphLocalExecution {
         @Override
         public Object testData(Map<String, Object> dataParameters) {
             try {
-                DataSetIterator underlying = new MnistDataSetIterator(batchSize, Math.min(10000, 5 * batchSize), false, false, false, 12345);
+                DataSetIterator underlying = new MnistDataSetIterator(batchSize, Math.min(10000, 2 * batchSize), false, false, false, 12345);
                 return new MultiDataSetIteratorAdapter(underlying);
             } catch (IOException e) {
                 throw new RuntimeException(e);
@@ -305,7 +306,7 @@ public class TestGraphLocalExecution {
     @Test
     public void testLocalExecutionEarlyStopping() throws Exception {
         EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>()
-                .epochTerminationConditions(new MaxEpochsTerminationCondition(4))
+                .epochTerminationConditions(new MaxEpochsTerminationCondition(2))
                 .scoreCalculator(new ScoreProvider())
                 .modelSaver(new InMemoryModelSaver()).build();
         Map<String, Object> commands = new HashMap<>();
@@ -348,8 +349,8 @@ public class TestGraphLocalExecution {
                 .dataProvider(dataProvider)
                 .scoreFunction(ScoreFunctions.testSetF1())
                 .modelSaver(new FileModelSaver(modelSavePath))
-                .terminationConditions(new MaxTimeCondition(45, TimeUnit.SECONDS),
+                .terminationConditions(new MaxTimeCondition(15, TimeUnit.SECONDS),
-                        new MaxCandidatesCondition(10))
+                        new MaxCandidatesCondition(3))
                 .build();
 
 
@@ -364,7 +365,7 @@ public class TestGraphLocalExecution {
         @Override
         public ScoreCalculator get() {
             try {
-                return new DataSetLossCalculatorCG(new MnistDataSetIterator(128, 1280), true);
+                return new DataSetLossCalculatorCG(new MnistDataSetIterator(4, 8), true);
             } catch (Exception e){
                 throw new RuntimeException(e);
             }

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.computationgraph;
 
 import lombok.extern.slf4j.Slf4j;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.ComputationGraphSpace;
 import org.deeplearning4j.arbiter.conf.updater.AdamSpace;
 import org.deeplearning4j.arbiter.conf.updater.SgdSpace;
@@ -79,11 +80,16 @@ import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
 @Slf4j
-public class TestGraphLocalExecutionGenetic {
+public class TestGraphLocalExecutionGenetic extends BaseDL4JTest {
 
     @Rule
     public TemporaryFolder testDir = new TemporaryFolder();
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 45000L;
+    }
+
     @Test
     public void testLocalExecutionDataSources() throws Exception {
         for (int dataApproach = 0; dataApproach < 3; dataApproach++) {
@@ -115,7 +121,7 @@ public class TestGraphLocalExecutionGenetic {
             if (dataApproach == 0) {
                 ds = TestDL4JLocalExecution.MnistDataSource.class;
                 dsP = new Properties();
-                dsP.setProperty("minibatch", "8");
+                dsP.setProperty("minibatch", "2");
 
                 candidateGenerator = new GeneticSearchCandidateGenerator.Builder(mls, scoreFunction)
                         .populationModel(new PopulationModel.Builder().populationSize(5).build())
@@ -148,7 +154,7 @@ public class TestGraphLocalExecutionGenetic {
                     .dataSource(ds, dsP)
                     .modelSaver(new FileModelSaver(modelSave))
                     .scoreFunction(new TestSetLossScoreFunction())
-                    .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES),
+                    .terminationConditions(new MaxTimeCondition(5, TimeUnit.SECONDS),
                             new MaxCandidatesCondition(10))
                     .build();
 
@@ -157,7 +163,7 @@ public class TestGraphLocalExecutionGenetic {
             runner.execute();
 
             List<ResultReference> results = runner.getResults();
-            assertEquals(10, results.size());
+            assertTrue(results.size() > 0);
 
             System.out.println("----- COMPLETE - " + results.size() + " results -----");
         }

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/json/TestJson.java

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.json;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.ComputationGraphSpace;
 import org.deeplearning4j.arbiter.MultiLayerSpace;
 import org.deeplearning4j.arbiter.conf.updater.AdaMaxSpace;
@@ -71,7 +72,7 @@ import static org.junit.Assert.assertNotNull;
 /**
  * Created by Alex on 14/02/2017.
  */
-public class TestJson {
+public class TestJson extends BaseDL4JTest {
 
     @Test
     public void testMultiLayerSpaceJson() {

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.multilayernetwork;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.MultiLayerSpace;
 import org.deeplearning4j.arbiter.conf.updater.SgdSpace;
 import org.deeplearning4j.arbiter.layers.ConvolutionLayerSpace;
@@ -59,7 +60,7 @@ import java.util.concurrent.TimeUnit;
 // import org.deeplearning4j.arbiter.optimize.ui.listener.UIOptimizationRunnerStatusListener;
 
 /** Not strictly a unit test. Rather: part example, part debugging on MNIST */
-public class MNISTOptimizationTest {
+public class MNISTOptimizationTest extends BaseDL4JTest {
 
     public static void main(String[] args) throws Exception {
         EarlyStoppingConfiguration<MultiLayerNetwork> esConf =

arbiter/arbiter-deeplearning4j/src/test/java/org/deeplearning4j/arbiter/multilayernetwork/TestDL4JLocalExecution.java

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.multilayernetwork;
 
 import lombok.extern.slf4j.Slf4j;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.MultiLayerSpace;
 import org.deeplearning4j.arbiter.conf.updater.SgdSpace;
 import org.deeplearning4j.arbiter.evaluator.multilayer.ClassificationEvaluator;
@@ -72,9 +73,10 @@ import java.util.Properties;
 import java.util.concurrent.TimeUnit;
 
 import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
 
 @Slf4j
-public class TestDL4JLocalExecution {
+public class TestDL4JLocalExecution extends BaseDL4JTest {
 
     @Rule
     public TemporaryFolder testDir = new TemporaryFolder();
@@ -112,7 +114,7 @@ public class TestDL4JLocalExecution {
             if(dataApproach == 0){
                 ds = MnistDataSource.class;
                 dsP = new Properties();
-                dsP.setProperty("minibatch", "8");
+                dsP.setProperty("minibatch", "2");
                 candidateGenerator = new RandomSearchGenerator(mls);
             } else if(dataApproach == 1) {
                 //DataProvider approach
@@ -136,7 +138,7 @@ public class TestDL4JLocalExecution {
                     .dataSource(ds, dsP)
                     .modelSaver(new FileModelSaver(modelSave))
                     .scoreFunction(new TestSetLossScoreFunction())
-                    .terminationConditions(new MaxTimeCondition(2, TimeUnit.MINUTES),
+                    .terminationConditions(new MaxTimeCondition(5, TimeUnit.SECONDS),
                             new MaxCandidatesCondition(5))
                     .build();
 
@@ -146,7 +148,7 @@ public class TestDL4JLocalExecution {
             runner.execute();
 
             List<ResultReference> results = runner.getResults();
-            assertEquals(5, results.size());
+            assertTrue(results.size() > 0);
 
             System.out.println("----- COMPLETE - " + results.size() + " results -----");
         }

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.multilayernetwork;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.ComputationGraphSpace;
 import org.deeplearning4j.arbiter.MultiLayerSpace;
 import org.deeplearning4j.arbiter.layers.DenseLayerSpace;
@@ -39,7 +40,7 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 
 import java.io.File;
 
-public class TestErrors {
+public class TestErrors extends BaseDL4JTest {
 
     @Rule
     public TemporaryFolder temp = new TemporaryFolder();
@@ -60,7 +61,7 @@ public class TestErrors {
         CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);
 
         OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
-                .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 10))
+                .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
                 .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
                 .terminationConditions(
                         new MaxCandidatesCondition(5))
@@ -87,7 +88,7 @@ public class TestErrors {
         CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);
 
         OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
-                .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 10))
+                .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
                 .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
                 .terminationConditions(
                         new MaxCandidatesCondition(5))
@@ -116,7 +117,7 @@ public class TestErrors {
         CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);
 
         OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
-                .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 10))
+                .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
                 .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
                 .terminationConditions(new MaxCandidatesCondition(5))
                 .build();
@@ -143,7 +144,7 @@ public class TestErrors {
         CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);
 
         OptimizationConfiguration configuration = new OptimizationConfiguration.Builder()
-                .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 10))
+                .candidateGenerator(candidateGenerator).dataProvider(new TestDataProviderMnist(32, 3))
                 .modelSaver(new FileModelSaver(f)).scoreFunction(new TestSetLossScoreFunction(true))
                 .terminationConditions(
                         new MaxCandidatesCondition(5))

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.arbiter.multilayernetwork;
 
 import org.apache.commons.lang3.ArrayUtils;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.TestUtils;
 import org.deeplearning4j.arbiter.conf.updater.SgdSpace;
 import org.deeplearning4j.arbiter.layers.*;
@@ -44,7 +45,7 @@ import java.util.Random;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertTrue;
 
-public class TestLayerSpace {
+public class TestLayerSpace extends BaseDL4JTest {
 
     @Test
     public void testBasic1() {

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

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.multilayernetwork;
 
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.DL4JConfiguration;
 import org.deeplearning4j.arbiter.MultiLayerSpace;
 import org.deeplearning4j.arbiter.TestUtils;
@@ -86,7 +87,7 @@ import java.util.*;
 
 import static org.junit.Assert.*;
 
-public class TestMultiLayerSpace {
+public class TestMultiLayerSpace extends BaseDL4JTest {
 
     @Rule
     public TemporaryFolder testDir = new TemporaryFolder();

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

@@ -18,6 +18,7 @@ package org.deeplearning4j.arbiter.multilayernetwork;
 
 import lombok.AllArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.MultiLayerSpace;
 import org.deeplearning4j.arbiter.conf.updater.AdamSpace;
 import org.deeplearning4j.arbiter.layers.OutputLayerSpace;
@@ -60,7 +61,13 @@ import java.util.Map;
 import static org.junit.Assert.assertEquals;
 
 @Slf4j
-public class TestScoreFunctions {
+public class TestScoreFunctions extends BaseDL4JTest {
+
+
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 60000L;
+    }
 
     @Test
     public void testROCScoreFunctions() throws Exception {
@@ -107,7 +114,7 @@ public class TestScoreFunctions {
                 List<ResultReference> list = runner.getResults();
 
                 for (ResultReference rr : list) {
-                    DataSetIterator testIter = new MnistDataSetIterator(32, 2000, false, false, true, 12345);
+                    DataSetIterator testIter = new MnistDataSetIterator(4, 16, false, false, false, 12345);
                     testIter.setPreProcessor(new PreProc(rocType));
 
                     OptimizationResult or = rr.getResult();
@@ -141,10 +148,10 @@ public class TestScoreFunctions {
                     }
 
 
-                    DataSetIterator iter = new MnistDataSetIterator(32, 8000, false, true, true, 12345);
+                    DataSetIterator iter = new MnistDataSetIterator(4, 16, false, false, false, 12345);
                     iter.setPreProcessor(new PreProc(rocType));
 
-                    assertEquals(msg, expScore, or.getScore(), 1e-5);
+                    assertEquals(msg, expScore, or.getScore(), 1e-4);
                 }
             }
         }
@@ -158,7 +165,7 @@ public class TestScoreFunctions {
         @Override
         public Object trainData(Map<String, Object> dataParameters) {
             try {
-                DataSetIterator iter = new MnistDataSetIterator(32, 8000, false, true, true, 12345);
+                DataSetIterator iter = new MnistDataSetIterator(4, 16, false, false, false, 12345);
                 iter.setPreProcessor(new PreProc(rocType));
                 return iter;
             } catch (IOException e){
@@ -169,7 +176,7 @@ public class TestScoreFunctions {
         @Override
         public Object testData(Map<String, Object> dataParameters) {
             try {
-                DataSetIterator iter = new MnistDataSetIterator(32, 2000, false, false, true, 12345);
+                DataSetIterator iter = new MnistDataSetIterator(4, 16, false, false, false, 12345);
                 iter.setPreProcessor(new PreProc(rocType));
                 return iter;
             } catch (IOException e){

arbiter/arbiter-server/pom.xml

@@ -49,6 +49,13 @@
             <version>${junit.version}</version>
             <scope>test</scope>
         </dependency>
+
+        <dependency>
+            <groupId>org.deeplearning4j</groupId>
+            <artifactId>deeplearning4j-common-tests</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
     </dependencies>
 
     <profiles>

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

@@ -18,6 +18,7 @@ package org.deeplearning4j.arbiter.server;
 
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.io.FileUtils;
+import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.arbiter.MultiLayerSpace;
 import org.deeplearning4j.arbiter.conf.updater.SgdSpace;
 import org.deeplearning4j.arbiter.layers.DenseLayerSpace;
@@ -52,7 +53,7 @@ import static org.junit.Assert.assertEquals;
  * Created by agibsonccc on 3/12/17.
  */
 @Slf4j
-public class ArbiterCLIRunnerTest {
+public class ArbiterCLIRunnerTest extends BaseDL4JTest {
 
 
     @Test

deeplearning4j/deeplearning4j-common-tests/src/main/java/org/deeplearning4j/BaseDL4JTest.java

@@ -24,6 +24,7 @@ import org.junit.Before;
 import org.junit.Rule;
 import org.junit.rules.TestName;
 import org.junit.rules.Timeout;
+import org.nd4j.base.Preconditions;
 import org.nd4j.config.ND4JSystemProperties;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.memory.MemoryWorkspace;
@@ -36,6 +37,8 @@ import java.util.List;
 import java.util.Map;
 import java.util.Properties;
 
+import static org.junit.Assume.assumeTrue;
+
 @Slf4j
 public abstract class BaseDL4JTest {
 
@@ -47,6 +50,17 @@ public abstract class BaseDL4JTest {
     protected long startTime;
     protected int threadCountBefore;
 
+    private final int DEFAULT_THREADS = Runtime.getRuntime().availableProcessors();
+
+    /**
+     * Override this to specify the number of threads for C++ execution, via
+     * {@link org.nd4j.linalg.factory.Environment#setMaxMasterThreads(int)}
+     * @return Number of threads to use for C++ op execution
+     */
+    public int numThreads(){
+        return DEFAULT_THREADS;
+    }
+
     /**
      * Override this method to set the default timeout for methods in the test class
      */
@@ -72,6 +86,28 @@ public abstract class BaseDL4JTest {
         return getDataType();
     }
 
+    protected Boolean integrationTest;
+
+    /**
+     * @return True if integration tests maven profile is enabled, false otherwise.
+     */
+    public boolean isIntegrationTests(){
+        if(integrationTest == null){
+            String prop = System.getenv("DL4J_INTEGRATION_TESTS");
+            integrationTest = Boolean.parseBoolean(prop);
+        }
+        return integrationTest;
+    }
+
+    /**
+     * Call this as the first line of a test in order to skip that test, only when the integration tests maven profile is not enabled.
+     * This can be used to dynamically skip integration tests when the integration test profile is not enabled.
+     * Note that the integration test profile is not enabled by default - "integration-tests" profile
+     */
+    public void skipUnlessIntegrationTests(){
+        assumeTrue("Skipping integration test - integration profile is not enabled", isIntegrationTests());
+    }
+
     @Before
     public void beforeTest(){
         log.info("{}.{}", getClass().getSimpleName(), name.getMethodName());
@@ -81,6 +117,14 @@ public abstract class BaseDL4JTest {
         Nd4j.getExecutioner().setProfilingMode(getProfilingMode());
         Nd4j.getExecutioner().setProfilingConfig(ProfilerConfig.builder().build());
         Nd4j.setDefaultDataTypes(getDataType(), getDefaultFPDataType());
+        Nd4j.getExecutioner().setProfilingConfig(ProfilerConfig.builder().build());
+        Nd4j.getExecutioner().enableDebugMode(false);
+        Nd4j.getExecutioner().enableVerboseMode(false);
+        int numThreads = numThreads();
+        Preconditions.checkState(numThreads > 0, "Number of threads must be > 0");
+        if(numThreads != Nd4j.getEnvironment().maxMasterThreads()) {
+            Nd4j.getEnvironment().setMaxMasterThreads(numThreads);
+        }
         startTime = System.currentTimeMillis();
         threadCountBefore = ManagementFactory.getThreadMXBean().getThreadCount();
     }

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/LayerHelperValidationUtil.java

@@ -158,7 +158,7 @@ public class LayerHelperValidationUtil {
                         double d2 = arr2.dup('c').getDouble(idx);
                         System.out.println("Different values at index " + idx + ": " + d1 + ", " + d2 + " - RE = " + maxRE);
                     }
-                    assertTrue(s + layerName + "activations - max RE: " + maxRE, maxRE < t.getMaxRelError());
+                    assertTrue(s + layerName + " activations - max RE: " + maxRE, maxRE < t.getMaxRelError());
                     log.info("Forward pass, max relative error: " + layerName + " - " + maxRE);
                 }
 

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/AsyncDataSetIteratorTest.java

@@ -78,8 +78,16 @@ public class AsyncDataSetIteratorTest extends BaseDL4JTest {
 
     @Test
     public void hasNextWithResetAndLoad() throws Exception {
+        int[] prefetchSizes;
+        if(isIntegrationTests()){
+            prefetchSizes = new int[]{2, 3, 4, 5, 6, 7, 8};
+        } else {
+            prefetchSizes = new int[]{2, 3, 8};
+        }
+
+
         for (int iter = 0; iter < ITERATIONS; iter++) {
-            for (int prefetchSize = 2; prefetchSize <= 8; prefetchSize++) {
+            for(int prefetchSize : prefetchSizes){
                 AsyncDataSetIterator iterator = new AsyncDataSetIterator(backIterator, prefetchSize);
                 TestDataSetConsumer consumer = new TestDataSetConsumer(EXECUTION_SMALL);
                 int cnt = 0;
@@ -161,8 +169,14 @@ public class AsyncDataSetIteratorTest extends BaseDL4JTest {
 
     @Test
     public void testVariableTimeSeries1() throws Exception {
+        int numBatches = isIntegrationTests() ? 1000 : 100;
+        int batchSize = isIntegrationTests() ? 32 : 8;
+        int timeStepsMin = 10;
+        int timeStepsMax = isIntegrationTests() ? 500 : 100;
+        int valuesPerTimestep = isIntegrationTests() ? 128 : 16;
+
         AsyncDataSetIterator adsi = new AsyncDataSetIterator(
-                        new VariableTimeseriesGenerator(1192, 1000, 32, 128, 10, 500, 10), 2, true);
+                        new VariableTimeseriesGenerator(1192, numBatches, batchSize, valuesPerTimestep, timeStepsMin, timeStepsMax, 10), 2, true);
 
         for (int e = 0; e < 10; e++) {
             int cnt = 0;

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/AsyncMultiDataSetIteratorTest.java

@@ -18,21 +18,10 @@ package org.deeplearning4j.datasets.iterator;
 
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
-import org.datavec.api.records.reader.RecordReader;
-import org.datavec.api.records.reader.SequenceRecordReader;
-import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
-import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader;
-import org.datavec.api.split.NumberedFileInputSplit;
 import org.deeplearning4j.BaseDL4JTest;
-import org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator;
 import org.deeplearning4j.datasets.iterator.tools.VariableMultiTimeseriesGenerator;
 import org.junit.Test;
 import org.nd4j.linalg.dataset.api.MultiDataSet;
-import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
-import org.nd4j.linalg.dataset.api.preprocessor.MultiDataNormalization;
-import org.nd4j.linalg.dataset.api.preprocessor.MultiNormalizerStandardize;
-
-import java.util.Arrays;
 
 import static org.junit.Assert.assertEquals;
 
@@ -49,7 +38,13 @@ public class AsyncMultiDataSetIteratorTest extends BaseDL4JTest {
      */
     @Test
     public void testVariableTimeSeries1() throws Exception {
-        val iterator = new VariableMultiTimeseriesGenerator(1192, 1000, 32, 128, 10, 500, 10);
+        int numBatches = isIntegrationTests() ? 1000 : 100;
+        int batchSize = isIntegrationTests() ? 32 : 8;
+        int timeStepsMin = 10;
+        int timeStepsMax = isIntegrationTests() ? 500 : 100;
+        int valuesPerTimestep = isIntegrationTests() ? 128 : 16;
+
+        val iterator = new VariableMultiTimeseriesGenerator(1192, numBatches, batchSize, valuesPerTimestep, timeStepsMin, timeStepsMax, 10);
         iterator.reset();
         iterator.hasNext();
         val amdsi = new AsyncMultiDataSetIterator(iterator, 2, true);
@@ -81,7 +76,13 @@ public class AsyncMultiDataSetIteratorTest extends BaseDL4JTest {
 
     @Test
     public void testVariableTimeSeries2() throws Exception {
-        val iterator = new VariableMultiTimeseriesGenerator(1192, 1000, 32, 128, 10, 500, 10);
+        int numBatches = isIntegrationTests() ? 1000 : 100;
+        int batchSize = isIntegrationTests() ? 32 : 8;
+        int timeStepsMin = 10;
+        int timeStepsMax = isIntegrationTests() ? 500 : 100;
+        int valuesPerTimestep = isIntegrationTests() ? 128 : 16;
+
+        val iterator = new VariableMultiTimeseriesGenerator(1192, numBatches, batchSize, valuesPerTimestep, timeStepsMin, timeStepsMax, 10);
 
         for (int e = 0; e < 10; e++) {
             iterator.reset();

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/TestEmnistDataSetIterator.java

@@ -46,17 +46,17 @@ public class TestEmnistDataSetIterator extends BaseDL4JTest {
     @Test
     public void testEmnistDataSetIterator() throws Exception {
 
-        //        EmnistFetcher fetcher = new EmnistFetcher(EmnistDataSetIterator.Set.COMPLETE);
-        //        File baseEmnistDir = fetcher.getFILE_DIR();
-        //        if(baseEmnistDir.exists()){
-        //            FileUtils.deleteDirectory(baseEmnistDir);
-        //        }
-        //        assertFalse(baseEmnistDir.exists());
-
 
         int batchSize = 128;
 
-        for (EmnistDataSetIterator.Set s : EmnistDataSetIterator.Set.values()) {
+        EmnistDataSetIterator.Set[] sets;
+        if(isIntegrationTests()){
+            sets = EmnistDataSetIterator.Set.values();
+        } else {
+            sets = new EmnistDataSetIterator.Set[]{EmnistDataSetIterator.Set.MNIST, EmnistDataSetIterator.Set.LETTERS};
+        }
+
+        for (EmnistDataSetIterator.Set s : sets) {
             boolean isBalanced = EmnistDataSetIterator.isBalanced(s);
             int numLabels = EmnistDataSetIterator.numLabels(s);
             INDArray labelCounts = null;

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/eval/EvalTest.java

@@ -476,7 +476,7 @@ public class EvalTest extends BaseDL4JTest {
 
         net.setListeners(new EvaluativeListener(iterTest, 3));
 
-        for( int i=0; i<10; i++ ){
+        for( int i=0; i<3; i++ ){
             net.fit(iter);
         }
     }

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java

@@ -339,9 +339,6 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
 
                             if (PRINT_RESULTS) {
                                 log.info(msg);
-                                for (int j = 0; j < net.getnLayers(); j++) {
-                                    log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
-                                }
                             }
 
                             boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
@@ -623,13 +620,10 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
 
             if (PRINT_RESULTS) {
                 log.info(msg);
-//                for (int j = 0; j < net.getnLayers(); j++) {
-//                    log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
-//                }
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
-                    .labels(labels).subset(true).maxPerParam(128));
+                    .labels(labels).subset(true).maxPerParam(64));
 
             assertTrue(msg, gradOK);
 

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java

@@ -557,60 +557,52 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
         int[] minibatchSizes = {2};
         int width = 5;
         int height = 5;
-        int[] inputDepths = {1, 2, 4};
-
-        Activation[] activations = {Activation.SIGMOID, Activation.TANH};
 
         Nd4j.getRandom().setSeed(12345);
 
-        for (int inputDepth : inputDepths) {
+        int[] inputDepths = new int[]{1, 2, 4};
-            for (Activation afn : activations) {
+        Activation[] activations = {Activation.SIGMOID, Activation.TANH, Activation.SOFTPLUS};
-                for (int minibatchSize : minibatchSizes) {
+        int[] minibatch = {2, 1, 3};
-                    INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
-                    INDArray labels = Nd4j.zeros(minibatchSize, nOut);
-                    for (int i = 0; i < minibatchSize; i++) {
-                        labels.putScalar(new int[]{i, i % nOut}, 1.0);
-                    }
 
-                    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).updater(new NoOp())
+        for( int i=0; i<inputDepths.length; i++ ){
-                            .dataType(DataType.DOUBLE)
+            int inputDepth = inputDepths[i];
-                            .activation(afn)
+            Activation afn = activations[i];
-                            .list()
+            int minibatchSize = minibatch[i];
-                            .layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1)
+            INDArray input = Nd4j.rand(minibatchSize, width * height * inputDepth);
-                                    .padding(0, 0).nIn(inputDepth).nOut(2).build())//output: (5-2+0)/1+1 = 4
+            INDArray labels = TestUtils.randomOneHot(minibatchSize, nOut);
-                            .layer(1, new LocallyConnected2D.Builder().nIn(2).nOut(7).kernelSize(2, 2)
-                                    .setInputSize(4, 4).convolutionMode(ConvolutionMode.Strict).hasBias(false)
-                                    .stride(1, 1).padding(0, 0).build()) //(4-2+0)/1+1 = 3
-                            .layer(2, new ConvolutionLayer.Builder().nIn(7).nOut(2).kernelSize(2, 2)
-                                    .stride(1, 1).padding(0, 0).build()) //(3-2+0)/1+1 = 2
-                            .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
-                                    .activation(Activation.SOFTMAX).nIn(2 * 2 * 2).nOut(nOut)
-                                    .build())
-                            .setInputType(InputType.convolutionalFlat(height, width, inputDepth)).build();
 
-                    assertEquals(ConvolutionMode.Truncate,
+            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).updater(new NoOp())
-                            ((ConvolutionLayer) conf.getConf(0).getLayer()).getConvolutionMode());
+                    .dataType(DataType.DOUBLE)
+                    .activation(afn)
+                    .list()
+                    .layer(0, new ConvolutionLayer.Builder().kernelSize(2, 2).stride(1, 1)
+                            .padding(0, 0).nIn(inputDepth).nOut(2).build())//output: (5-2+0)/1+1 = 4
+                    .layer(1, new LocallyConnected2D.Builder().nIn(2).nOut(7).kernelSize(2, 2)
+                            .setInputSize(4, 4).convolutionMode(ConvolutionMode.Strict).hasBias(false)
+                            .stride(1, 1).padding(0, 0).build()) //(4-2+0)/1+1 = 3
+                    .layer(2, new ConvolutionLayer.Builder().nIn(7).nOut(2).kernelSize(2, 2)
+                            .stride(1, 1).padding(0, 0).build()) //(3-2+0)/1+1 = 2
+                    .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
+                            .activation(Activation.SOFTMAX).nIn(2 * 2 * 2).nOut(nOut)
+                            .build())
+                    .setInputType(InputType.convolutionalFlat(height, width, inputDepth)).build();
 
-                    MultiLayerNetwork net = new MultiLayerNetwork(conf);
+            assertEquals(ConvolutionMode.Truncate,
-                    net.init();
+                    ((ConvolutionLayer) conf.getConf(0).getLayer()).getConvolutionMode());
 
-                    for (int i = 0; i < 4; i++) {
+            MultiLayerNetwork net = new MultiLayerNetwork(conf);
-                        System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
+            net.init();
-                    }
 
-                    String msg = "Minibatch=" + minibatchSize + ", activationFn="
+            String msg = "Minibatch=" + minibatchSize + ", activationFn="
-                            + afn;
+                    + afn;
-                    System.out.println(msg);
+            System.out.println(msg);
 
-                    boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
+            boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
-                            DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
+                    DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
 
-                    assertTrue(msg, gradOK);
+            assertTrue(msg, gradOK);
 
-                    TestUtils.testModelSerialization(net);
+            TestUtils.testModelSerialization(net);
-                }
-
-            }
         }
     }
 
@@ -1106,71 +1098,64 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
     public void testCropping2DLayer() {
         Nd4j.getRandom().setSeed(12345);
         int nOut = 2;
-
-        int[] minibatchSizes = {1, 3};
         int width = 12;
         int height = 11;
-        int[] inputDepths = {1, 3};
 
         int[] kernel = {2, 2};
         int[] stride = {1, 1};
         int[] padding = {0, 0};
 
         int[][] cropTestCases = new int[][]{{0, 0, 0, 0}, {1, 1, 0, 0}, {2, 2, 2, 2}, {1, 2, 3, 4}};
+        int[] inputDepths = {1, 2, 3, 2};
+        int[] minibatchSizes = {2, 1, 3, 2};
 
-        for (int inputDepth : inputDepths) {
+        for (int i = 0; i < cropTestCases.length; i++) {
-            for (int minibatchSize : minibatchSizes) {
+            int inputDepth = inputDepths[i];
-                INDArray input = Nd4j.rand(new int[]{minibatchSize, inputDepth, height, width});
+            int minibatchSize = minibatchSizes[i];
-                INDArray labels = Nd4j.zeros(minibatchSize, nOut);
+            int[] crop = cropTestCases[i];
-                for (int i = 0; i < minibatchSize; i++) {
+            INDArray input = Nd4j.rand(new int[]{minibatchSize, inputDepth, height, width});
-                    labels.putScalar(new int[]{i, i % nOut}, 1.0);
+            INDArray labels = TestUtils.randomOneHot(minibatchSize, nOut);
-                }
-                for (int[] crop : cropTestCases) {
 
-                    MultiLayerConfiguration conf =
+            MultiLayerConfiguration conf =
-                            new NeuralNetConfiguration.Builder()
+                    new NeuralNetConfiguration.Builder()
-                                    .dataType(DataType.DOUBLE)
+                            .dataType(DataType.DOUBLE)
-                                    .updater(new NoOp())
+                            .updater(new NoOp())
-                                    .convolutionMode(ConvolutionMode.Same)
+                            .convolutionMode(ConvolutionMode.Same)
-                                    .weightInit(new NormalDistribution(0, 1)).list()
+                            .weightInit(new NormalDistribution(0, 1)).list()
-                                    .layer(new ConvolutionLayer.Builder(kernel, stride, padding)
+                            .layer(new ConvolutionLayer.Builder(kernel, stride, padding)
-                                            .nIn(inputDepth).nOut(2).build())//output: (6-2+0)/1+1 = 5
+                                    .nIn(inputDepth).nOut(2).build())//output: (6-2+0)/1+1 = 5
-                                    .layer(new Cropping2D(crop))
+                            .layer(new Cropping2D(crop))
-                                    .layer(new ConvolutionLayer.Builder(kernel, stride, padding).nIn(2).nOut(2).build())
+                            .layer(new ConvolutionLayer.Builder(kernel, stride, padding).nIn(2).nOut(2).build())
-                                    .layer(new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG).kernelSize(3, 3).stride(3, 3).build())
+                            .layer(new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG).kernelSize(3, 3).stride(3, 3).build())
-                                    .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
+                            .layer(3, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
-                                            .activation(Activation.SOFTMAX).nOut(nOut).build())
+                                    .activation(Activation.SOFTMAX).nOut(nOut).build())
-                                    .setInputType(InputType.convolutional(height, width, inputDepth))
+                            .setInputType(InputType.convolutional(height, width, inputDepth))
-                                    .build();
+                            .build();
 
-                    MultiLayerNetwork net = new MultiLayerNetwork(conf);
+            MultiLayerNetwork net = new MultiLayerNetwork(conf);
-                    net.init();
+            net.init();
 
-                    //Check cropping activation shape
+            //Check cropping activation shape
-                    org.deeplearning4j.nn.layers.convolution.Cropping2DLayer cl =
+            org.deeplearning4j.nn.layers.convolution.Cropping2DLayer cl =
-                            (org.deeplearning4j.nn.layers.convolution.Cropping2DLayer) net.getLayer(1);
+                    (org.deeplearning4j.nn.layers.convolution.Cropping2DLayer) net.getLayer(1);
-                    val expShape = new long[]{minibatchSize, inputDepth, height - crop[0] - crop[1],
+            val expShape = new long[]{minibatchSize, inputDepth, height - crop[0] - crop[1],
-                            width - crop[2] - crop[3]};
+                    width - crop[2] - crop[3]};
-                    INDArray out = cl.activate(input, false, LayerWorkspaceMgr.noWorkspaces());
+            INDArray out = cl.activate(input, false, LayerWorkspaceMgr.noWorkspaces());
-                    assertArrayEquals(expShape, out.shape());
+            assertArrayEquals(expShape, out.shape());
 
-                    String msg = "minibatch=" + minibatchSize + ", channels=" + inputDepth + ", zeroPad = "
+            String msg = "minibatch=" + minibatchSize + ", channels=" + inputDepth + ", zeroPad = "
-                            + Arrays.toString(crop);
+                    + Arrays.toString(crop);
 
-                    if (PRINT_RESULTS) {
+            if (PRINT_RESULTS) {
-                        System.out.println(msg);
+                System.out.println(msg);
-//                        for (int j = 0; j < net.getnLayers(); j++)
-//                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
-                    }
-
-                    boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
-                            .labels(labels).subset(true).maxPerParam(160));
-
-                    assertTrue(msg, gradOK);
-
-                    TestUtils.testModelSerialization(net);
-                }
             }
+
+            boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(net).input(input)
+                    .labels(labels).subset(true).maxPerParam(160));
+
+            assertTrue(msg, gradOK);
+
+            TestUtils.testModelSerialization(net);
         }
     }
 

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTestsMasking.java

@@ -142,9 +142,9 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
         Nd4j.getRandom().setSeed(12345L);
 
         int timeSeriesLength = 5;
-        int nIn = 5;
+        int nIn = 3;
         int layerSize = 3;
-        int nOut = 3;
+        int nOut = 2;
 
         int miniBatchSize = 2;
 
@@ -170,24 +170,16 @@ public class GradientCheckTestsMasking extends BaseDL4JTest {
             MultiLayerNetwork mln = new MultiLayerNetwork(conf);
             mln.init();
 
-            Random r = new Random(12345L);
             INDArray input = Nd4j.rand(new int[]{miniBatchSize, nIn, timeSeriesLength}, 'f').subi(0.5);
 
-            INDArray labels = Nd4j.zeros(miniBatchSize, nOut, timeSeriesLength);
+            INDArray labels = TestUtils.randomOneHotTimeSeries(miniBatchSize, nOut, timeSeriesLength);
-            for (int i = 0; i < miniBatchSize; i++) {
-                for (int j = 0; j < nIn; j++) {
-                    labels.putScalar(i, r.nextInt(nOut), j, 1.0);
-                }
-            }
 
             if (PRINT_RESULTS) {
                 System.out.println("testBidirectionalLSTMMasking() - testNum = " + testNum++);
-//                for (int j = 0; j < mln.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
-                    .labels(labels).inputMask(mask).labelMask(mask).subset(true).maxPerParam(16));
+                    .labels(labels).inputMask(mask).labelMask(mask).subset(true).maxPerParam(12));
 
             assertTrue(gradOK);
             TestUtils.testModelSerialization(mln);

deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java

@@ -123,8 +123,6 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                     if (PRINT_RESULTS) {
                         System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation="
                                 + outputActivation + ", doLearningFirst=" + doLearningFirst);
-                        for (int j = 0; j < mln.getnLayers(); j++)
-                            System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -214,8 +212,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                 System.out.println(testName + "- activationFn=" + afn + ", lossFn=" + lf
                         + ", outputActivation=" + outputActivation + ", doLearningFirst="
                         + doLearningFirst);
-                for (int j = 0; j < mln.getnLayers(); j++)
+//                for (int j = 0; j < mln.getnLayers(); j++)
-                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -277,8 +275,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
 
                 if (PRINT_RESULTS) {
                     System.out.println(msg);
-                    for (int j = 0; j < net.getnLayers(); j++)
+//                    for (int j = 0; j < net.getnLayers(); j++)
-                        System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                        System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
                 }
                 boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
                         DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
@@ -340,8 +338,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
 
                     if (PRINT_RESULTS) {
                         System.out.println(msg);
-                        for (int j = 0; j < net.getnLayers(); j++)
+//                        for (int j = 0; j < net.getnLayers(); j++)
-                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
                     }
                     boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
                             DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
@@ -397,8 +395,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
 
             if (PRINT_RESULTS) {
                 System.out.println(msg);
-                for (int j = 0; j < net.getnLayers(); j++)
+//                for (int j = 0; j < net.getnLayers(); j++)
-                    System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                    System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -468,8 +466,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
 
                     if (PRINT_RESULTS) {
                         System.out.println(msg);
-                        for (int j = 0; j < net.getnLayers(); j++)
+//                        for (int j = 0; j < net.getnLayers(); j++)
-                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -602,9 +600,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                         MultiLayerNetwork net = new MultiLayerNetwork(conf);
                         net.init();
 
-                        for (int i = 0; i < 4; i++) {
+//                        for (int i = 0; i < 4; i++) {
-                            System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
+//                            System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
-                        }
+//                        }
 
                         String msg = "PoolingType=" + poolingType + ", minibatch=" + minibatchSize + ", activationFn="
                                 + afn;
@@ -663,9 +661,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
             MultiLayerNetwork net = new MultiLayerNetwork(conf);
             net.init();
 
-            for (int j = 0; j < net.getLayers().length; j++) {
+//            for (int j = 0; j < net.getLayers().length; j++) {
-                System.out.println("nParams, layer " + j + ": " + net.getLayer(j).numParams());
+//                System.out.println("nParams, layer " + j + ": " + net.getLayer(j).numParams());
-            }
+//            }
 
             String msg = "Minibatch=" + minibatchSize + ", inDepth=" + inputDepth + ", height=" + height
                     + ", kernelSize=" + k;
@@ -726,9 +724,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                             MultiLayerNetwork net = new MultiLayerNetwork(conf);
                             net.init();
 
-                            for (int i = 0; i < net.getLayers().length; i++) {
+//                            for (int i = 0; i < net.getLayers().length; i++) {
-                                System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
+//                                System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
-                            }
+//                            }
 
                             String msg = "Minibatch=" + minibatchSize + ", inDepth=" + inputDepth + ", height=" + height
                                     + ", kernelSize=" + k + ", stride = " + stride + ", convLayer first = "
@@ -806,8 +804,8 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
 
             if (PRINT_RESULTS) {
                 System.out.println(msg);
-                for (int j = 0; j < net.getnLayers(); j++)
+//                for (int j = 0; j < net.getnLayers(); j++)
-                    System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                    System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -872,9 +870,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
             MultiLayerNetwork net = new MultiLayerNetwork(conf);
             net.init();
 
-            for (int j = 0; j < net.getLayers().length; j++) {
+//            for (int j = 0; j < net.getLayers().length; j++) {
-                System.out.println("nParams, layer " + j + ": " + net.getLayer(j).numParams());
+//                System.out.println("nParams, layer " + j + ": " + net.getLayer(j).numParams());
-            }
+//            }
 
             String msg = " - mb=" + minibatchSize + ", k="
                     + k + ", s=" + s + ", d=" + d + ", cm=" + cm;
@@ -943,9 +941,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
             MultiLayerNetwork net = new MultiLayerNetwork(conf);
             net.init();
 
-            for (int i = 0; i < net.getLayers().length; i++) {
+//            for (int i = 0; i < net.getLayers().length; i++) {
-                System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
+//                System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
-            }
+//            }
 
             String msg = " - mb=" + minibatchSize + ", k="
                     + k + ", nIn=" + nIn + ", depthMul=" + depthMultiplier + ", s=" + s + ", cm=" + cm;
@@ -1018,9 +1016,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
             MultiLayerNetwork net = new MultiLayerNetwork(conf);
             net.init();
 
-            for (int i = 0; i < net.getLayers().length; i++) {
+//            for (int i = 0; i < net.getLayers().length; i++) {
-                System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
+//                System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
-            }
+//            }
 
             String msg = " - mb=" + minibatchSize + ", k="
                     + k + ", s=" + s + ", d=" + d + ", cm=" + cm;
@@ -1104,9 +1102,9 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
             MultiLayerNetwork net = new MultiLayerNetwork(conf);
             net.init();
 
-            for (int i = 0; i < net.getLayers().length; i++) {
+//            for (int i = 0; i < net.getLayers().length; i++) {
-                System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
+//                System.out.println("nParams, layer " + i + ": " + net.getLayer(i).numParams());
-            }
+//            }
 
             String msg = (subsampling ? "subsampling" : "conv") + " - mb=" + minibatchSize + ", k="
                     + k + ", s=" + s + ", d=" + d + ", cm=" + cm;
@@ -1179,8 +1177,6 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
 
                     if (PRINT_RESULTS) {
                         System.out.println(msg);
-                        for (int j = 0; j < net.getnLayers(); j++)
-                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(

deeplearning4j/deeplearning4j-nearestneighbors-parent/nearestneighbor-core/src/test/java/org/deeplearning4j/clustering/kdtree/KDTreeTest.java

@@ -177,7 +177,7 @@ public class KDTreeTest extends BaseDL4JTest {
     @Test
     public void testKNN() {
         int dimensions = 512;
-        int vectorsNo = 50000;
+        int vectorsNo = isIntegrationTests() ? 50000 : 1000;
         // make a KD-tree of dimension {#dimensions}
         Stopwatch stopwatch = Stopwatch.createStarted();
         KDTree kdTree = new KDTree(dimensions);

deeplearning4j/deeplearning4j-nearestneighbors-parent/nearestneighbor-core/src/test/java/org/deeplearning4j/clustering/sptree/SPTreeTest.java

@@ -92,13 +92,13 @@ public class SPTreeTest extends BaseDL4JTest {
     @Test
     //@Ignore
     public void testLargeTree() {
-        int num = 100000;
+        int num = isIntegrationTests() ? 100000 : 1000;
         StopWatch watch = new StopWatch();
         watch.start();
         INDArray arr = Nd4j.linspace(1, num, num, Nd4j.dataType()).reshape(num, 1);
         SpTree tree = new SpTree(arr);
         watch.stop();
-        System.out.println("Tree created in " + watch);
+        System.out.println("Tree of size " + num + " created in " + watch);
     }
 
 }

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/test/java/com/atilika/kuromoji/ipadic/RandomizedInputTest.java

@@ -45,19 +45,19 @@ public class RandomizedInputTest extends RandomizedTest {
     private Tokenizer tokenizer = new Tokenizer();
 
     @Test
-    @Repeat(iterations = 50)
+    @Repeat(iterations = 10)
     public void testRandomizedUnicodeInput() {
         assertCanTokenizeString(randomUnicodeOfLength(LENGTH), tokenizer);
     }
 
     @Test
-    @Repeat(iterations = 50)
+    @Repeat(iterations = 10)
     public void testRandomizedRealisticUnicodeInput() {
         assertCanTokenizeString(randomRealisticUnicodeOfLength(LENGTH), tokenizer);
     }
 
     @Test
-    @Repeat(iterations = 50)
+    @Repeat(iterations = 10)
     public void testRandomizedAsciiInput() {
         assertCanTokenizeString(randomAsciiOfLength(LENGTH), tokenizer);
     }

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/WordVectorSerializerTest.java

@@ -406,11 +406,11 @@ public class WordVectorSerializerTest extends BaseDL4JTest {
         double simD = arraysSimilarity(day1, day2);
         double simN = arraysSimilarity(night1, night2);
 
-        logger.info("Vec1 day: " + day1);
+//        logger.info("Vec1 day: " + day1);
-        logger.info("Vec2 day: " + day2);
+//        logger.info("Vec2 day: " + day2);
 
-        logger.info("Vec1 night: " + night1);
+//        logger.info("Vec1 night: " + night1);
-        logger.info("Vec2 night: " + night2);
+//        logger.info("Vec2 night: " + night2);
 
         logger.info("Day/day cross-model similarity: " + simD);
         logger.info("Night/night cross-model similarity: " + simN);

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/word2vec/Word2VecTests.java

@@ -16,6 +16,9 @@
 
 package org.deeplearning4j.models.word2vec;
 
+import org.apache.commons.io.IOUtils;
+import org.apache.commons.io.LineIterator;
+import org.deeplearning4j.text.sentenceiterator.CollectionSentenceIterator;
 import org.junit.Rule;
 import org.junit.rules.Timeout;
 import org.nd4j.shade.guava.primitives.Doubles;
@@ -51,8 +54,8 @@ import org.nd4j.resources.Resources;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import java.io.File;
+import java.io.*;
-import java.io.IOException;
+import java.nio.charset.StandardCharsets;
 import java.util.*;
 
 import static org.junit.Assert.*;
@@ -185,7 +188,12 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testWord2VecMultiEpoch() throws Exception {
-        SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
+        SentenceIterator iter;
+        if(isIntegrationTests()){
+            iter = new BasicLineIterator(inputFile.getAbsolutePath());
+        } else {
+            iter = new CollectionSentenceIterator(firstNLines(inputFile, 50000));
+        }
 
         TokenizerFactory t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());
@@ -389,7 +397,12 @@ public class Word2VecTests extends BaseDL4JTest {
     @Test
     public void testW2VnegativeOnRestore() throws Exception {
         // Strip white space before and after for each line
-        SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
+        SentenceIterator iter;
+        if(isIntegrationTests()){
+            iter = new BasicLineIterator(inputFile.getAbsolutePath());
+        } else {
+            iter = new CollectionSentenceIterator(firstNLines(inputFile, 300));
+        }
         // Split on white spaces in the line to get words
         TokenizerFactory t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());
@@ -491,7 +504,12 @@ public class Word2VecTests extends BaseDL4JTest {
     @Test
     public void orderIsCorrect_WhenParallelized() throws Exception {
         // Strip white space before and after for each line
-        SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
+        SentenceIterator iter;
+        if(isIntegrationTests()){
+            iter = new BasicLineIterator(inputFile.getAbsolutePath());
+        } else {
+            iter = new CollectionSentenceIterator(firstNLines(inputFile, 300));
+        }
         // Split on white spaces in the line to get words
         TokenizerFactory t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());
@@ -510,9 +528,10 @@ public class Word2VecTests extends BaseDL4JTest {
         System.out.println(vec.getVocab().numWords());
 
         val words = vec.getVocab().words();
-        for (val word : words) {
+        assertTrue(words.size() > 0);
-            System.out.println(word);
+//        for (val word : words) {
-        }
+//            System.out.println(word);
+//        }
     }
 
     @Test
@@ -755,7 +774,16 @@ public class Word2VecTests extends BaseDL4JTest {
     @Test
     public void weightsNotUpdated_WhenLocked() throws Exception {
 
-        SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
+        boolean isIntegration = isIntegrationTests();
+        SentenceIterator iter;
+        SentenceIterator iter2;
+        if(isIntegration){
+            iter = new BasicLineIterator(inputFile);
+            iter2 = new BasicLineIterator(inputFile2.getAbsolutePath());
+        } else {
+            iter = new CollectionSentenceIterator(firstNLines(inputFile, 300));
+            iter2 = new CollectionSentenceIterator(firstNLines(inputFile2, 300));
+        }
 
         Word2Vec vec1 = new Word2Vec.Builder().minWordFrequency(1).iterations(3).batchSize(64).layerSize(100)
                 .stopWords(new ArrayList<String>()).seed(42).learningRate(0.025).minLearningRate(0.001)
@@ -767,13 +795,12 @@ public class Word2VecTests extends BaseDL4JTest {
 
         vec1.fit();
 
-        iter = new BasicLineIterator(inputFile2.getAbsolutePath());
         Word2Vec vec2 = new Word2Vec.Builder().minWordFrequency(1).iterations(3).batchSize(32).layerSize(100)
                 .stopWords(new ArrayList<String>()).seed(32).learningRate(0.021).minLearningRate(0.001)
                 .sampling(0).elementsLearningAlgorithm(new SkipGram<VocabWord>())
                 .epochs(1).windowSize(5).allowParallelTokenization(true)
                 .workers(1)
-                .iterate(iter)
+                .iterate(iter2)
                 .intersectModel(vec1, true)
                 .modelUtils(new BasicModelUtils<VocabWord>()).build();
 
@@ -861,6 +888,22 @@ public class Word2VecTests extends BaseDL4JTest {
         }
         System.out.print("\n");
     }
-    //
+
+    public static List<String> firstNLines(File f, int n){
+        List<String> lines = new ArrayList<>();
+        try(InputStream is = new BufferedInputStream(new FileInputStream(f))){
+            LineIterator lineIter = IOUtils.lineIterator(is, StandardCharsets.UTF_8);
+            try{
+                for( int i=0; i<n && lineIter.hasNext(); i++ ){
+                    lines.add(lineIter.next());
+                }
+            } finally {
+                lineIter.close();
+            }
+            return lines;
+        } catch (IOException e){
+            throw new RuntimeException(e);
+        }
+    }
 }
 

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/TsneTest.java

@@ -48,7 +48,7 @@ public class TsneTest extends BaseDL4JTest {
 
     @Override
     public long getTimeoutMilliseconds() {
-        return 120000L;
+        return 60000L;
     }
 
     @Rule
@@ -58,103 +58,102 @@ public class TsneTest extends BaseDL4JTest {
     public void testSimple() throws Exception {
         //Simple sanity check
 
-        for (boolean syntheticData : new boolean[]{false, true}) {
+        for( int test=0; test <=1; test++){
-            for (WorkspaceMode wsm : new WorkspaceMode[]{WorkspaceMode.NONE, WorkspaceMode.ENABLED}) {
+            boolean syntheticData = test == 1;
-                log.info("Starting test: WSM={}, syntheticData={}", wsm, syntheticData);
+            WorkspaceMode wsm = test == 0 ? WorkspaceMode.NONE : WorkspaceMode.ENABLED;
+            log.info("Starting test: WSM={}, syntheticData={}", wsm, syntheticData);
 
-                //STEP 1: Initialization
+            //STEP 1: Initialization
-                int iterations = 50;
+            int iterations = 50;
-                //create an n-dimensional array of doubles
+            //create an n-dimensional array of doubles
-                Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
+            Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
-                List<String> cacheList = new ArrayList<>(); //cacheList is a dynamic array of strings used to hold all words
+            List<String> cacheList = new ArrayList<>(); //cacheList is a dynamic array of strings used to hold all words
 
-                //STEP 2: Turn text input into a list of words
+            //STEP 2: Turn text input into a list of words
-                INDArray weights;
+            INDArray weights;
-                if(syntheticData){
+            if(syntheticData){
-                    weights = Nd4j.rand(1000, 200);
+                weights = Nd4j.rand(250, 200);
-                } else {
+            } else {
-                    log.info("Load & Vectorize data....");
+                log.info("Load & Vectorize data....");
-                    File wordFile = new ClassPathResource("deeplearning4j-tsne/words.txt").getFile();   //Open the file
+                File wordFile = new ClassPathResource("deeplearning4j-tsne/words.txt").getFile();   //Open the file
-                    //Get the data of all unique word vectors
+                //Get the data of all unique word vectors
-                    Pair<InMemoryLookupTable, VocabCache> vectors = WordVectorSerializer.loadTxt(wordFile);
+                Pair<InMemoryLookupTable, VocabCache> vectors = WordVectorSerializer.loadTxt(wordFile);
-                    VocabCache cache = vectors.getSecond();
+                VocabCache cache = vectors.getSecond();
-                    weights = vectors.getFirst().getSyn0();    //seperate weights of unique words into their own list
+                weights = vectors.getFirst().getSyn0();    //seperate weights of unique words into their own list
 
-                    for (int i = 0; i < cache.numWords(); i++)   //seperate strings of words into their own list
+                for (int i = 0; i < cache.numWords(); i++)   //seperate strings of words into their own list
-                        cacheList.add(cache.wordAtIndex(i));
+                    cacheList.add(cache.wordAtIndex(i));
-                }
-
-                //STEP 3: build a dual-tree tsne to use later
-                log.info("Build model....");
-                BarnesHutTsne tsne = new BarnesHutTsne.Builder()
-                        .setMaxIter(iterations)
-                        .theta(0.5)
-                        .normalize(false)
-                        .learningRate(500)
-                        .useAdaGrad(false)
-                        .workspaceMode(wsm)
-                        .build();
-
-
-                //STEP 4: establish the tsne values and save them to a file
-                log.info("Store TSNE Coordinates for Plotting....");
-                File outDir = testDir.newFolder();
-                tsne.fit(weights);
-                tsne.saveAsFile(cacheList, new File(outDir, "out.txt").getAbsolutePath());
             }
+
+            //STEP 3: build a dual-tree tsne to use later
+            log.info("Build model....");
+            BarnesHutTsne tsne = new BarnesHutTsne.Builder()
+                    .setMaxIter(iterations)
+                    .theta(0.5)
+                    .normalize(false)
+                    .learningRate(500)
+                    .useAdaGrad(false)
+                    .workspaceMode(wsm)
+                    .build();
+
+
+            //STEP 4: establish the tsne values and save them to a file
+            log.info("Store TSNE Coordinates for Plotting....");
+            File outDir = testDir.newFolder();
+            tsne.fit(weights);
+            tsne.saveAsFile(cacheList, new File(outDir, "out.txt").getAbsolutePath());
         }
     }
 
-    //Elapsed time : 01:01:57.988
     @Test
     public void testPerformance() throws Exception {
 
         StopWatch watch = new StopWatch();
         watch.start();
-        for (boolean syntheticData : new boolean[]{false, true}) {
+        for( int test=0; test <=1; test++){
-            for (WorkspaceMode wsm : new WorkspaceMode[]{WorkspaceMode.NONE, WorkspaceMode.ENABLED}) {
+            boolean syntheticData = test == 1;
-                log.info("Starting test: WSM={}, syntheticData={}", wsm, syntheticData);
+            WorkspaceMode wsm = test == 0 ? WorkspaceMode.NONE : WorkspaceMode.ENABLED;
+            log.info("Starting test: WSM={}, syntheticData={}", wsm, syntheticData);
 
-                //STEP 1: Initialization
+            //STEP 1: Initialization
-                int iterations = 100;
+            int iterations = 50;
-                //create an n-dimensional array of doubles
+            //create an n-dimensional array of doubles
-                Nd4j.setDataType(DataType.DOUBLE);
+            Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
-                List<String> cacheList = new ArrayList<>(); //cacheList is a dynamic array of strings used to hold all words
+            List<String> cacheList = new ArrayList<>(); //cacheList is a dynamic array of strings used to hold all words
 
-                //STEP 2: Turn text input into a list of words
+            //STEP 2: Turn text input into a list of words
-                INDArray weights;
+            INDArray weights;
-                if(syntheticData){
+            if(syntheticData){
-                    weights = Nd4j.rand(5000, 20);
+                weights = Nd4j.rand(DataType.FLOAT, 250, 20);
-                } else {
+            } else {
-                    log.info("Load & Vectorize data....");
+                log.info("Load & Vectorize data....");
-                    File wordFile = new ClassPathResource("deeplearning4j-tsne/words.txt").getFile();   //Open the file
+                File wordFile = new ClassPathResource("deeplearning4j-tsne/words.txt").getFile();   //Open the file
-                    //Get the data of all unique word vectors
+                //Get the data of all unique word vectors
-                    Pair<InMemoryLookupTable, VocabCache> vectors = WordVectorSerializer.loadTxt(wordFile);
+                Pair<InMemoryLookupTable, VocabCache> vectors = WordVectorSerializer.loadTxt(wordFile);
-                    VocabCache cache = vectors.getSecond();
+                VocabCache cache = vectors.getSecond();
-                    weights = vectors.getFirst().getSyn0();    //seperate weights of unique words into their own list
+                weights = vectors.getFirst().getSyn0();    //seperate weights of unique words into their own list
 
-                    for (int i = 0; i < cache.numWords(); i++)   //seperate strings of words into their own list
+                for (int i = 0; i < cache.numWords(); i++)   //seperate strings of words into their own list
-                        cacheList.add(cache.wordAtIndex(i));
+                    cacheList.add(cache.wordAtIndex(i));
-                }
-
-                //STEP 3: build a dual-tree tsne to use later
-                log.info("Build model....");
-                BarnesHutTsne tsne = new BarnesHutTsne.Builder()
-                        .setMaxIter(iterations)
-                        .theta(0.5)
-                        .normalize(false)
-                        .learningRate(500)
-                        .useAdaGrad(false)
-                        .workspaceMode(wsm)
-                        .build();
-
-
-                //STEP 4: establish the tsne values and save them to a file
-                log.info("Store TSNE Coordinates for Plotting....");
-                File outDir = testDir.newFolder();
-                tsne.fit(weights);
-                tsne.saveAsFile(cacheList, new File(outDir, "out.txt").getAbsolutePath());
             }
+
+            //STEP 3: build a dual-tree tsne to use later
+            log.info("Build model....");
+            BarnesHutTsne tsne = new BarnesHutTsne.Builder()
+                    .setMaxIter(iterations)
+                    .theta(0.5)
+                    .normalize(false)
+                    .learningRate(500)
+                    .useAdaGrad(false)
+                    .workspaceMode(wsm)
+                    .build();
+
+
+            //STEP 4: establish the tsne values and save them to a file
+            log.info("Store TSNE Coordinates for Plotting....");
+            File outDir = testDir.newFolder();
+            tsne.fit(weights);
+            tsne.saveAsFile(cacheList, new File(outDir, "out.txt").getAbsolutePath());
         }
         watch.stop();
         System.out.println("Elapsed time : " + watch);

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/paragraphvectors/ParagraphVectorsTest.java

@@ -20,6 +20,8 @@ package org.deeplearning4j.models.paragraphvectors;
 import lombok.NonNull;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
+import org.apache.commons.io.IOUtils;
+import org.apache.commons.io.LineIterator;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.models.embeddings.learning.impl.elements.CBOW;
 import org.deeplearning4j.models.embeddings.reader.impl.FlatModelUtils;
@@ -27,6 +29,7 @@ import org.deeplearning4j.models.sequencevectors.sequence.Sequence;
 import org.deeplearning4j.models.sequencevectors.transformers.impl.SentenceTransformer;
 import org.deeplearning4j.models.sequencevectors.transformers.impl.iterables.BasicTransformerIterator;
 import org.deeplearning4j.models.sequencevectors.transformers.impl.iterables.ParallelTransformerIterator;
+import org.deeplearning4j.text.sentenceiterator.*;
 import org.junit.Rule;
 import org.junit.rules.TemporaryFolder;
 import org.nd4j.linalg.io.ClassPathResource;
@@ -46,10 +49,6 @@ import org.deeplearning4j.text.documentiterator.FileLabelAwareIterator;
 import org.deeplearning4j.text.documentiterator.LabelAwareIterator;
 import org.deeplearning4j.text.documentiterator.LabelledDocument;
 import org.deeplearning4j.text.documentiterator.LabelsSource;
-import org.deeplearning4j.text.sentenceiterator.AggregatingSentenceIterator;
-import org.deeplearning4j.text.sentenceiterator.BasicLineIterator;
-import org.deeplearning4j.text.sentenceiterator.FileSentenceIterator;
-import org.deeplearning4j.text.sentenceiterator.SentenceIterator;
 import org.deeplearning4j.text.sentenceiterator.interoperability.SentenceIteratorConverter;
 import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor;
 import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
@@ -66,8 +65,8 @@ import org.nd4j.resources.Resources;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import java.io.File;
+import java.io.*;
-import java.io.IOException;
+import java.nio.charset.StandardCharsets;
 import java.util.*;
 import java.util.concurrent.atomic.AtomicLong;
 
@@ -372,7 +371,7 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
 
         LabelsSource source = new LabelsSource("DOC_");
 
-        ParagraphVectors vec = new ParagraphVectors.Builder().minWordFrequency(1).iterations(2).seed(119).epochs(3)
+        ParagraphVectors vec = new ParagraphVectors.Builder().minWordFrequency(1).iterations(2).seed(119).epochs(1)
                         .layerSize(100).learningRate(0.025).labelsSource(source).windowSize(5).iterate(iter)
                         .trainWordVectors(true).vocabCache(cache).tokenizerFactory(t).negativeSample(0)
                         .useHierarchicSoftmax(true).sampling(0).workers(1).usePreciseWeightInit(true)
@@ -425,6 +424,8 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
 
     @Test(timeout = 300000)
     public void testParagraphVectorsDBOW() throws Exception {
+        skipUnlessIntegrationTests();
+
         File file = Resources.asFile("/big/raw_sentences.txt");
         SentenceIterator iter = new BasicLineIterator(file);
 
@@ -657,7 +658,7 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
         }
     }
 
-    @Test(timeout = 300000)
+    @Test
     public void testIterator() throws IOException {
         val folder_labeled = testDir.newFolder();
         val folder_unlabeled = testDir.newFolder();
@@ -672,7 +673,7 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
         SentenceIterator iter = new BasicLineIterator(resource_sentences);
 
         int i = 0;
-        for (; i < 10000; ++i) {
+        for (; i < 10; ++i) {
             int j = 0;
             int labels = 0;
             int words = 0;
@@ -721,7 +722,7 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
         TokenizerFactory t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());
 
-        Word2Vec wordVectors = new Word2Vec.Builder().seed(119).minWordFrequency(1).batchSize(250).iterations(1).epochs(3)
+        Word2Vec wordVectors = new Word2Vec.Builder().seed(119).minWordFrequency(1).batchSize(250).iterations(1).epochs(1)
                         .learningRate(0.025).layerSize(150).minLearningRate(0.001)
                         .elementsLearningAlgorithm(new SkipGram<VocabWord>()).useHierarchicSoftmax(true).windowSize(5)
                         .allowParallelTokenization(true)
@@ -1009,7 +1010,7 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
         TokenizerFactory t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());
 
-        Word2Vec wordVectors = new Word2Vec.Builder().minWordFrequency(1).batchSize(250).iterations(1).epochs(3)
+        Word2Vec wordVectors = new Word2Vec.Builder().minWordFrequency(1).batchSize(250).iterations(1).epochs(1)
                         .learningRate(0.025).layerSize(150).minLearningRate(0.001)
                         .elementsLearningAlgorithm(new SkipGram<VocabWord>()).useHierarchicSoftmax(true).windowSize(5)
                         .iterate(iter).tokenizerFactory(t).build();
@@ -1151,8 +1152,27 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
 
     @Test(timeout = 300000)
     public void testDoubleFit() throws Exception {
+        boolean isIntegration = isIntegrationTests();
         File resource = Resources.asFile("/big/raw_sentences.txt");
-        SentenceIterator iter = new BasicLineIterator(resource);
+        SentenceIterator iter;
+        if(isIntegration){
+            iter = new BasicLineIterator(resource);
+        } else {
+            List<String> lines = new ArrayList<>();
+            try(InputStream is = new BufferedInputStream(new FileInputStream(resource))){
+                LineIterator lineIter = IOUtils.lineIterator(is, StandardCharsets.UTF_8);
+                try{
+                    for( int i=0; i<500 && lineIter.hasNext(); i++ ){
+                        lines.add(lineIter.next());
+                    }
+                } finally {
+                    lineIter.close();
+                }
+            }
+
+            iter = new CollectionSentenceIterator(lines);
+        }
+
 
         TokenizerFactory t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/word2vec/iterator/Word2VecDataSetIteratorTest.java

@@ -49,7 +49,7 @@ public class Word2VecDataSetIteratorTest extends BaseDL4JTest {
 
     @Override
     public long getTimeoutMilliseconds() {
-        return 240000L;
+        return 60000L;
     }
 
     /**
@@ -57,6 +57,7 @@ public class Word2VecDataSetIteratorTest extends BaseDL4JTest {
      */
     @Test
     public void testIterator1() throws Exception {
+
         File inputFile = Resources.asFile("big/raw_sentences.txt");
         SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
 
@@ -77,10 +78,14 @@ public class Word2VecDataSetIteratorTest extends BaseDL4JTest {
 
         Word2VecDataSetIterator iterator = new Word2VecDataSetIterator(vec, getLASI(iter, labels), labels, 1);
         INDArray array = iterator.next().getFeatures();
+        int count = 0;
         while (iterator.hasNext()) {
             DataSet ds = iterator.next();
 
             assertArrayEquals(array.shape(), ds.getFeatures().shape());
+
+            if(!isIntegrationTests() && count++ > 20)
+                break;  //raw_sentences.txt is 2.81 MB, takes quite some time to process. We'll only first 20 minibatches when doing unit tests
         }
     }
 

deeplearning4j/deeplearning4j-nn/src/test/java/org/deeplearning4j/optimize/solvers/accumulation/EncodedGradientsAccumulatorTest.java

@@ -45,9 +45,15 @@ public class EncodedGradientsAccumulatorTest extends BaseDL4JTest {
      */
     @Test
     public void testStore1() throws Exception {
-        int numParams = 100000;
+        int numParams;
-
+        int[] workers;
-        int workers[] = new int[] {2, 4, 8};
+        if(isIntegrationTests()){
+            numParams = 100000;
+            workers = new int[] {2, 4, 8};
+        } else {
+            numParams = 10000;
+            workers = new int[] {2, 3};
+        }
 
         for (int numWorkers : workers) {
             EncodingHandler handler = new EncodingHandler(new FixedThresholdAlgorithm(1e-3),null, null, false);
@@ -77,7 +83,13 @@ public class EncodedGradientsAccumulatorTest extends BaseDL4JTest {
      */
     @Test
     public void testEncodingLimits1() throws Exception {
-        int numParams = 100000;
+        int numParams;
+        if(isIntegrationTests()){
+            numParams = 100000;
+        } else {
+            numParams = 10000;
+        }
+
 
         EncodingHandler handler = new EncodingHandler(new FixedThresholdAlgorithm(1e-3), null, null, false);
         for (int e = 10; e < numParams / 5; e++) {

deeplearning4j/deeplearning4j-nn/src/test/java/org/deeplearning4j/optimize/solvers/accumulation/IndexedTailTest.java

@@ -242,7 +242,7 @@ public class IndexedTailTest extends BaseDL4JTest {
         final long[] sums = new long[numReaders];
         val readers = new ArrayList<Thread>();
         for (int e = 0; e < numReaders; e++) {
-            val f = e;
+            final int f = e;
             val t = new Thread(new Runnable() {
                 @Override
                 public void run() {
@@ -297,7 +297,7 @@ public class IndexedTailTest extends BaseDL4JTest {
         final long[] sums = new long[numReaders];
         val readers = new ArrayList<Thread>();
         for (int e = 0; e < numReaders; e++) {
-            val f = e;
+            final int f = e;
             val t = new Thread(new Runnable() {
                 @Override
                 public void run() {
@@ -371,7 +371,7 @@ public class IndexedTailTest extends BaseDL4JTest {
         final long[] sums = new long[numReaders];
         val readers = new ArrayList<Thread>();
         for (int e = 0; e < numReaders; e++) {
-            val f = e;
+            final int f = e;
             val t = new Thread(new Runnable() {
                 @Override
                 public void run() {

deeplearning4j/deeplearning4j-remote/deeplearning4j-json-server/src/test/java/org/deeplearning4j/remote/JsonModelServerTest.java

@@ -35,6 +35,7 @@ import org.deeplearning4j.remote.helpers.House;
 import org.deeplearning4j.remote.helpers.HouseToPredictedPriceAdapter;
 import org.deeplearning4j.remote.helpers.PredictedPrice;
 import org.junit.After;
+import org.junit.Before;
 import org.junit.Test;
 import org.nd4j.adapters.InferenceAdapter;
 import org.nd4j.autodiff.samediff.SDVariable;
@@ -58,6 +59,7 @@ import java.util.Collections;
 import java.util.concurrent.ExecutionException;
 import java.util.concurrent.Future;
 import java.util.concurrent.TimeUnit;
+import java.util.concurrent.atomic.AtomicInteger;
 
 import static org.deeplearning4j.parallelism.inference.InferenceMode.INPLACE;
 import static org.deeplearning4j.parallelism.inference.InferenceMode.SEQUENTIAL;
@@ -66,7 +68,6 @@ import static org.junit.Assert.*;
 @Slf4j
 public class JsonModelServerTest extends BaseDL4JTest {
     private static final MultiLayerNetwork model;
-    private final int PORT = 18080;
 
     static {
         val conf = new NeuralNetConfiguration.Builder()
@@ -84,10 +85,18 @@ public class JsonModelServerTest extends BaseDL4JTest {
 
     @After
     public void pause() throws Exception {
-        // TODO: the same port was used in previous test and not accessible immediately. Might be better solution.
+        // Need to wait for server shutdown; without sleep, tests will fail if starting immediately after shutdown
         TimeUnit.SECONDS.sleep(2);
     }
 
+    private AtomicInteger portCount = new AtomicInteger(18080);
+    private int PORT;
+
+    @Before
+    public void setPort(){
+        PORT = portCount.getAndIncrement();
+    }
+
 
     @Test
     public void testStartStopParallel() throws Exception {
@@ -343,7 +352,7 @@ public class JsonModelServerTest extends BaseDL4JTest {
         val server = new JsonModelServer.Builder<House, PredictedPrice>(model)
                 .outputSerializer(new PredictedPrice.PredictedPriceSerializer())
                 .inputDeserializer(null)
-                .port(18080)
+                .port(PORT)
                 .build();
     }
 
@@ -382,7 +391,7 @@ public class JsonModelServerTest extends BaseDL4JTest {
                             return null;
                         }
                     })
-                    .endpointAddress("http://localhost:18080/v1/serving")
+                    .endpointAddress("http://localhost:" + PORT + "/v1/serving")
                     .build();
 
             int district = 2;

deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/test/java/org/deeplearning4j/parallelism/ParallelInferenceTest.java

@@ -485,7 +485,8 @@ public class ParallelInferenceTest extends BaseDL4JTest {
                     List<INDArray> exp = new ArrayList<>();
 
                     Random r = new Random();
-                    for (int i = 0; i < 500; i++) {
+                    int runs = isIntegrationTests() ? 500 : 30;
+                    for (int i = 0; i < runs; i++) {
                         int[] shape = defaultSize;
                         if (r.nextDouble() < 0.4) {
                             shape = new int[]{r.nextInt(5) + 1, 10, r.nextInt(10) + 1};
@@ -597,7 +598,8 @@ public class ParallelInferenceTest extends BaseDL4JTest {
                 List<INDArray> arrs = new ArrayList<>();
                 List<INDArray> exp = new ArrayList<>();
                 Random r = new Random();
-                for( int i=0; i<500; i++ ){
+                int runs = isIntegrationTests() ? 500 : 20;
+                for( int i=0; i<runs; i++ ){
                     int[] shape = defaultShape;
                     if(r.nextDouble() < 0.4){
                         shape = new int[]{r.nextInt(5)+1, nIn, 10, r.nextInt(10)+1};
@@ -679,8 +681,7 @@ public class ParallelInferenceTest extends BaseDL4JTest {
         }
     }
 
-    @Test(timeout = 120000)
+    private void testInputMasking() throws Exception {
-    public void testInputMasking() throws Exception {
         Nd4j.getRandom().setSeed(12345);
 
         int nIn = 10;
@@ -698,12 +699,15 @@ public class ParallelInferenceTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
+//        InferenceMode[] inferenceModes = new InferenceMode[]{InferenceMode.SEQUENTIAL, InferenceMode.BATCHED, InferenceMode.INPLACE, InferenceMode.SEQUENTIAL};
+//        int[] workers = new int[]{2, 2, 2, 1};
+//        boolean[] randomTS = new boolean[]{true, false, true, false};
+
         Random r = new Random();
         for( InferenceMode m : InferenceMode.values()) {
             log.info("Testing inference mode: [{}]", m);
             for( int w : new int[]{1,2}) {
                 for (boolean randomTSLength : new boolean[]{false, true}) {
-
                     final ParallelInference inf =
                             new ParallelInference.Builder(net)
                                     .inferenceMode(m)
@@ -714,7 +718,8 @@ public class ParallelInferenceTest extends BaseDL4JTest {
                     List<INDArray> in = new ArrayList<>();
                     List<INDArray> inMasks = new ArrayList<>();
                     List<INDArray> exp = new ArrayList<>();
-                    for (int i = 0; i < 100; i++) {
+                    int nRuns = isIntegrationTests() ? 100 : 10;
+                    for (int i = 0; i < nRuns; i++) {
                         int currTSLength = (randomTSLength ? 1 + r.nextInt(tsLength) : tsLength);
                         int currNumEx = 1 + r.nextInt(3);
                         INDArray inArr = Nd4j.rand(new int[]{currNumEx, nIn, currTSLength});
@@ -847,6 +852,7 @@ public class ParallelInferenceTest extends BaseDL4JTest {
 
                 List<INDArray[]> in = new ArrayList<>();
                 List<INDArray[]> exp = new ArrayList<>();
+                int runs = isIntegrationTests() ? 100 : 20;
                 for (int i = 0; i < 100; i++) {
                     int currNumEx = 1 + r.nextInt(3);
                     INDArray inArr = Nd4j.rand(new int[]{currNumEx, nIn});

deeplearning4j/deeplearning4j-scaleout/deeplearning4j-scaleout-parallelwrapper/src/test/java/org/deeplearning4j/parallelism/ParallelWrapperTest.java

@@ -62,8 +62,8 @@ public class ParallelWrapperTest extends BaseDL4JTest {
         int seed = 123;
 
         log.info("Load data....");
-        DataSetIterator mnistTrain = new EarlyTerminationDataSetIterator(new MnistDataSetIterator(batchSize, true, 12345), 100);
+        DataSetIterator mnistTrain = new EarlyTerminationDataSetIterator(new MnistDataSetIterator(batchSize, true, 12345), 15);
-        DataSetIterator mnistTest = new EarlyTerminationDataSetIterator(new MnistDataSetIterator(batchSize, false, 12345), 10);
+        DataSetIterator mnistTest = new EarlyTerminationDataSetIterator(new MnistDataSetIterator(batchSize, false, 12345), 4);
 
         assertTrue(mnistTrain.hasNext());
         val t0 = mnistTrain.next();

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp-java8/pom.xml

@@ -47,6 +47,12 @@
             <groupId>org.nd4j</groupId>
             <artifactId>nd4j-parameter-server-node_2.11</artifactId>
             <version>${nd4j.version}</version>
+            <exclusions>
+                <exclusion>
+                    <groupId>net.jpountz.lz4</groupId>
+                    <artifactId>lz4</artifactId>
+                </exclusion>
+            </exclusions>
         </dependency>
         <dependency>
             <groupId>junit</groupId>

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp-java8/src/test/resources/log4j.properties

@@ -0,0 +1,31 @@
+################################################################################
+# Copyright (c) 2015-2019 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
+################################################################################
+
+log4j.rootLogger=ERROR, Console
+log4j.appender.Console=org.apache.log4j.ConsoleAppender
+log4j.appender.Console.layout=org.apache.log4j.PatternLayout
+log4j.appender.Console.layout.ConversionPattern=%d{ABSOLUTE} %-5p ~ %m%n
+
+log4j.appender.org.springframework=DEBUG
+log4j.appender.org.deeplearning4j=DEBUG
+log4j.appender.org.nd4j=DEBUG
+
+log4j.logger.org.springframework=INFO
+log4j.logger.org.deeplearning4j=DEBUG
+log4j.logger.org.nd4j=DEBUG
+log4j.logger.org.apache.spark=WARN
+
+

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp-java8/src/test/resources/logback.xml

@@ -0,0 +1,53 @@
+<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  ~ 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
+  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->
+
+<configuration>
+
+
+
+    <appender name="FILE" class="ch.qos.logback.core.FileAppender">
+        <file>logs/application.log</file>
+        <encoder>
+            <pattern>%date - [%level] - from %logger in %thread
+                %n%message%n%xException%n</pattern>
+        </encoder>
+    </appender>
+
+    <appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
+        <encoder>
+            <pattern> %logger{15} - %message%n%xException{5}
+            </pattern>
+        </encoder>
+    </appender>
+
+    <logger name="org.apache.catalina.core" level="DEBUG" />
+    <logger name="org.springframework" level="DEBUG" />
+    <logger name="org.deeplearning4j" level="DEBUG" />
+    <logger name="org.datavec" level="INFO" />
+    <logger name="org.nd4j" level="INFO" />
+    <logger name="opennlp.uima.util" level="OFF" />
+    <logger name="org.apache.uima" level="OFF" />
+    <logger name="org.cleartk" level="OFF" />
+    <logger name="org.apache.spark" level="WARN" />
+
+
+
+    <root level="ERROR">
+        <appender-ref ref="STDOUT" />
+        <appender-ref ref="FILE" />
+    </root>
+
+</configuration>

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp/src/test/resources/log4j.properties

@@ -0,0 +1,31 @@
+################################################################################
+# Copyright (c) 2015-2019 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
+################################################################################
+
+log4j.rootLogger=ERROR, Console
+log4j.appender.Console=org.apache.log4j.ConsoleAppender
+log4j.appender.Console.layout=org.apache.log4j.PatternLayout
+log4j.appender.Console.layout.ConversionPattern=%d{ABSOLUTE} %-5p ~ %m%n
+
+log4j.appender.org.springframework=DEBUG
+log4j.appender.org.deeplearning4j=DEBUG
+log4j.appender.org.nd4j=DEBUG
+
+log4j.logger.org.springframework=INFO
+log4j.logger.org.deeplearning4j=DEBUG
+log4j.logger.org.nd4j=DEBUG
+log4j.logger.org.apache.spark=WARN
+
+

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp/src/test/resources/logback.xml

@@ -0,0 +1,53 @@
+<!--~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  ~ 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
+  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~-->
+
+<configuration>
+
+
+
+    <appender name="FILE" class="ch.qos.logback.core.FileAppender">
+        <file>logs/application.log</file>
+        <encoder>
+            <pattern>%date - [%level] - from %logger in %thread
+                %n%message%n%xException%n</pattern>
+        </encoder>
+    </appender>
+
+    <appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
+        <encoder>
+            <pattern> %logger{15} - %message%n%xException{5}
+            </pattern>
+        </encoder>
+    </appender>
+
+    <logger name="org.apache.catalina.core" level="DEBUG" />
+    <logger name="org.springframework" level="DEBUG" />
+    <logger name="org.deeplearning4j" level="DEBUG" />
+    <logger name="org.datavec" level="INFO" />
+    <logger name="org.nd4j" level="INFO" />
+    <logger name="opennlp.uima.util" level="OFF" />
+    <logger name="org.apache.uima" level="OFF" />
+    <logger name="org.cleartk" level="OFF" />
+    <logger name="org.apache.spark" level="WARN" />
+
+
+
+    <root level="ERROR">
+        <appender-ref ref="STDOUT" />
+        <appender-ref ref="FILE" />
+    </root>
+
+</configuration>

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

@@ -25,6 +25,7 @@ import org.datavec.spark.transform.misc.StringToWritablesFunction;
 import org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator;
 import org.deeplearning4j.spark.BaseSparkTest;
 import org.junit.Test;
+import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.dataset.api.MultiDataSet;
 import org.nd4j.linalg.io.ClassPathResource;
 
@@ -35,6 +36,15 @@ import static org.junit.Assert.assertEquals;
 
 public class TestIteratorUtils extends BaseSparkTest {
 
+    @Override
+    public DataType getDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Override
+    public DataType getDefaultFPDataType() {
+        return DataType.FLOAT;
+    }
 
     @Test
     public void testIrisRRMDSI() throws Exception {

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

@@ -453,8 +453,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         tempDirF.deleteOnExit();
 
         int dataSetObjSize = 1;
-        int batchSizePerExecutor = 16;
+        int batchSizePerExecutor = 4;
-        int numSplits = 5;
+        int numSplits = 3;
         int averagingFrequency = 3;
         int totalExamples = numExecutors() * batchSizePerExecutor * numSplits * averagingFrequency;
         DataSetIterator iter = new MnistDataSetIterator(dataSetObjSize, totalExamples, false);
@@ -506,7 +506,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         INDArray paramsAfter = sparkNet.getNetwork().params().dup();
         assertNotEquals(paramsBefore, paramsAfter);
 
-        Thread.sleep(2000);
+        Thread.sleep(200);
         SparkTrainingStats stats = sparkNet.getSparkTrainingStats();
 
         //Expect
@@ -517,7 +517,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         assertEquals(numSplits * numExecutors() * averagingFrequency, list.size());
         for (EventStats es : list) {
             ExampleCountEventStats e = (ExampleCountEventStats) es;
-            assertTrue(batchSizePerExecutor * averagingFrequency - 10 >= e.getTotalExampleCount());
+            assertTrue(batchSizePerExecutor * averagingFrequency >= e.getTotalExampleCount());
         }
 
 
@@ -535,9 +535,9 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         tempDirF.deleteOnExit();
         tempDirF2.deleteOnExit();
 
-        int dataSetObjSize = 5;
+        int dataSetObjSize = 4;
-        int batchSizePerExecutor = 25;
+        int batchSizePerExecutor = 8;
-        DataSetIterator iter = new MnistDataSetIterator(dataSetObjSize, 1000, false);
+        DataSetIterator iter = new MnistDataSetIterator(dataSetObjSize, 128, false);
         int i = 0;
         while (iter.hasNext()) {
             File nextFile = new File(tempDirF, i + ".bin");
@@ -981,7 +981,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
                 .setOutputs("out")
                 .build();
 
-        DataSetIterator iter = new IrisDataSetIterator(1, 150);
+        DataSetIterator iter = new IrisDataSetIterator(1, 50);
 
         List<DataSet> l = new ArrayList<>();
         while(iter.hasNext()){
@@ -992,9 +992,10 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
 
 
         int rddDataSetNumExamples = 1;
-        int averagingFrequency = 3;
+        int averagingFrequency = 2;
+        int batch = 2;
         ParameterAveragingTrainingMaster tm = new ParameterAveragingTrainingMaster.Builder(rddDataSetNumExamples)
-                .averagingFrequency(averagingFrequency).batchSizePerWorker(rddDataSetNumExamples)
+                .averagingFrequency(averagingFrequency).batchSizePerWorker(batch)
                 .saveUpdater(true).workerPrefetchNumBatches(0).build();
         Nd4j.getRandom().setSeed(12345);
 
@@ -1003,7 +1004,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         SparkComputationGraph sn2 = new SparkComputationGraph(sc, conf2.clone(), tm);
 
 
-        for(int i=0; i<4; i++ ){
+        for(int i=0; i<3; i++ ){
             assertEquals(i, sn1.getNetwork().getLayerWiseConfigurations().getEpochCount());
             assertEquals(i, sn2.getNetwork().getConfiguration().getEpochCount());
             sn1.fit(rdd);

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

@@ -42,6 +42,11 @@ import static org.junit.Assert.assertTrue;
  */
 public class TestRepartitioning extends BaseSparkTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return isIntegrationTests() ? 240000 : 60000;
+    }
+
     @Test
     public void testRepartitioning() {
         List<String> list = new ArrayList<>();
@@ -66,7 +71,12 @@ public class TestRepartitioning extends BaseSparkTest {
     @Test
     public void testRepartitioning2() throws Exception {
 
-        int[] ns = {320, 321, 25600, 25601, 25615};
+        int[] ns;
+        if(isIntegrationTests()){
+            ns = new int[]{320, 321, 25600, 25601, 25615};
+        } else {
+            ns = new int[]{320, 2561};
+        }
 
         for (int n : ns) {
 

deeplearning4j/deeplearning4j-zoo/src/test/java/org/deeplearning4j/zoo/MiscTests.java

@@ -32,6 +32,11 @@ import java.io.File;
 
 public class MiscTests extends BaseDL4JTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 120000L;
+    }
+
     @Test
     public void testTransferVGG() throws Exception {
         //https://github.com/deeplearning4j/deeplearning4j/issues/5167

deeplearning4j/deeplearning4j-zoo/src/test/java/org/deeplearning4j/zoo/TestDownload.java

@@ -48,6 +48,11 @@ import static org.junit.Assert.assertEquals;
 @Slf4j
 public class TestDownload extends BaseDL4JTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return isIntegrationTests() ? 480000L : 60000L;
+    }
+
     @ClassRule
     public static TemporaryFolder testDir = new TemporaryFolder();
     private static File f;
@@ -67,12 +72,20 @@ public class TestDownload extends BaseDL4JTest {
     public void testDownloadAllModels() throws Exception {
 
         // iterate through each available model
-        ZooModel[] models = new ZooModel[]{
+        ZooModel[] models;
-                LeNet.builder().build(),
+
-                SimpleCNN.builder().build(),
+        if(isIntegrationTests()){
-                UNet.builder().build(),
+            models = new ZooModel[]{
-                NASNet.builder().build()
+                    LeNet.builder().build(),
-        };
+                    SimpleCNN.builder().build(),
+                    UNet.builder().build(),
+                    NASNet.builder().build()};
+        } else {
+            models = new ZooModel[]{
+                    LeNet.builder().build(),
+                    SimpleCNN.builder().build()};
+        }
+
 
 
         for (int i = 0; i < models.length; i++) {

deeplearning4j/deeplearning4j-zoo/src/test/java/org/deeplearning4j/zoo/TestImageNet.java

@@ -57,6 +57,11 @@ import static org.junit.Assert.assertTrue;
 @Slf4j
 public class TestImageNet extends BaseDL4JTest {
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 90000L;
+    }
+
     @Override
     public DataType getDataType(){
         return DataType.FLOAT;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/random/custom/DistributionUniform.java

@@ -63,10 +63,15 @@ public class DistributionUniform extends DynamicCustomOp {
         addArgs();
     }
 
-    public DistributionUniform(INDArray shape, INDArray out, double min, double max){
+    public DistributionUniform(INDArray shape, INDArray out, double min, double max) {
+        this(shape, out, min, max, null);
+    }
+
+    public DistributionUniform(INDArray shape, INDArray out, double min, double max, DataType dataType){
         super(null, new INDArray[]{shape}, new INDArray[]{out}, Arrays.asList(min, max), (List<Integer>)null);
         this.min = min;
         this.max = max;
+        this.dataType = dataType;
     }
 
 

nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/pom.xml

@@ -310,6 +310,13 @@
                 </exclusion>
             </exclusions>
         </dependency>
+
+        <dependency>
+            <groupId>org.nd4j</groupId>
+            <artifactId>nd4j-common-tests</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
     </dependencies>
 
     <profiles>

nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/test/java/org/nd4j/jita/allocator/DeviceLocalNDArrayTests.java

@@ -19,6 +19,7 @@ package org.nd4j.jita.allocator;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
 import org.junit.Test;
+import org.nd4j.BaseND4JTest;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.util.DeviceLocalNDArray;
@@ -29,7 +30,7 @@ import static org.junit.Assert.assertArrayEquals;
 import static org.junit.Assert.assertEquals;
 
 @Slf4j
-public class DeviceLocalNDArrayTests {
+public class DeviceLocalNDArrayTests extends BaseND4JTest {
 
     @Test
     public void testDeviceLocalArray_1() throws Exception{

nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/test/java/org/nd4j/jita/allocator/impl/MemoryTrackerTest.java

@@ -19,13 +19,14 @@ package org.nd4j.jita.allocator.impl;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
 import org.junit.Test;
+import org.nd4j.BaseND4JTest;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.factory.Nd4j;
 
 import static org.junit.Assert.*;
 
 @Slf4j
-public class MemoryTrackerTest {
+public class MemoryTrackerTest extends BaseND4JTest {
 
     @Test
     public void testAllocatedDelta() {

nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/test/java/org/nd4j/linalg/jcublas/buffer/BaseCudaDataBufferTest.java

@@ -4,6 +4,7 @@ import lombok.extern.slf4j.Slf4j;
 import lombok.val;
 import org.junit.Before;
 import org.junit.Test;
+import org.nd4j.BaseND4JTest;
 import org.nd4j.jita.allocator.impl.AtomicAllocator;
 import org.nd4j.jita.workspace.CudaWorkspace;
 import org.nd4j.linalg.api.buffer.DataType;
@@ -20,7 +21,7 @@ import java.util.concurrent.atomic.AtomicInteger;
 import static org.junit.Assert.*;
 
 @Slf4j
-public class BaseCudaDataBufferTest {
+public class BaseCudaDataBufferTest extends BaseND4JTest {
 
     @Before
     public void setUp() {

nd4j/nd4j-backends/nd4j-tests-tensorflow/pom.xml

@@ -87,6 +87,13 @@
             <version>${logback.version}</version>
             <scope>test</scope>
         </dependency>
+
+        <dependency>
+            <groupId>org.nd4j</groupId>
+            <artifactId>nd4j-common-tests</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
     </dependencies>
 
     <profiles>

nd4j/nd4j-backends/nd4j-tests-tensorflow/src/test/cpujava/org/nd4j/tensorflow/conversion/GraphRunnerTest.java

@@ -20,6 +20,7 @@ package org.nd4j.tensorflow.conversion;
 import junit.framework.TestCase;
 import org.apache.commons.io.FileUtils;
 import org.bytedeco.tensorflow.TF_Tensor;
+import org.nd4j.BaseND4JTest;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.resources.Resources;
 import org.nd4j.shade.protobuf.Descriptors;
@@ -46,7 +47,17 @@ import java.util.Map;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertNotNull;
 
-public class GraphRunnerTest {
+public class GraphRunnerTest extends BaseND4JTest {
+
+    @Override
+    public DataType getDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Override
+    public DataType getDefaultFPDataType() {
+        return DataType.FLOAT;
+    }
 
     public static ConfigProto getConfig(){
         String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");

nd4j/nd4j-backends/nd4j-tests-tensorflow/src/test/cpujava/org/nd4j/tensorflow/conversion/TensorflowConversionTest.java

@@ -18,6 +18,7 @@ package org.nd4j.tensorflow.conversion;
 
 import org.apache.commons.io.IOUtils;
 import org.junit.Test;
+import org.nd4j.BaseND4JTest;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.io.ClassPathResource;
@@ -29,7 +30,7 @@ import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertNotNull;
 import static org.junit.Assert.fail;
 
-public class TensorflowConversionTest {
+public class TensorflowConversionTest extends BaseND4JTest {
 
     @Test
     public void testView() {

nd4j/nd4j-backends/nd4j-tests-tensorflow/src/test/gpujava/org/nd4j/tensorflow/conversion/GpuGraphRunnerTest.java

@@ -17,6 +17,7 @@
 
 package org.nd4j.tensorflow.conversion;
 
+import org.nd4j.BaseND4JTest;
 import org.nd4j.shade.protobuf.util.JsonFormat;
 import org.apache.commons.io.IOUtils;
 import org.junit.Test;
@@ -37,7 +38,7 @@ import java.util.Map;
 import static org.junit.Assert.assertEquals;
 import static org.junit.Assert.assertNotNull;
 
-public class GpuGraphRunnerTest {
+public class GpuGraphRunnerTest extends BaseND4JTest {
 
     @Test
     public void testGraphRunner() throws Exception {

nd4j/nd4j-backends/nd4j-tests/pom.xml

@@ -127,6 +127,13 @@
                 </exclusion>
             </exclusions>
         </dependency>
+
+        <dependency>
+            <groupId>org.nd4j</groupId>
+            <artifactId>nd4j-common-tests</artifactId>
+            <version>${project.version}</version>
+            <scope>test</scope>
+        </dependency>
     </dependencies>
 
     <reporting>

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LayerOpValidation.java

@@ -16,9 +16,6 @@
 
 package org.nd4j.autodiff.opvalidation;
 
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNull;
-
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collections;
@@ -46,6 +43,8 @@ import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.factory.Nd4jBackend;
 import org.nd4j.linalg.ops.transforms.Transforms;
 
+import static org.junit.Assert.*;
+
 @Slf4j
 public class LayerOpValidation extends BaseOpValidation {
     public LayerOpValidation(Nd4jBackend backend) {

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LossOpValidation.java

@@ -45,7 +45,7 @@ public class LossOpValidation extends BaseOpValidation {
     }
 
     @Override
-    public long testTimeoutMilliseconds() {
+    public long getTimeoutMilliseconds() {
         return 90000L;
     }
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ReductionOpValidation.java

@@ -54,8 +54,7 @@ import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertNull;
 
 @Slf4j
 @RunWith(Parameterized.class)

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/ShapeOpValidation.java

@@ -2434,8 +2434,6 @@ public class ShapeOpValidation extends BaseOpValidation {
 
     @Test
     public void testPermute4(){
-        Nd4j.getExecutioner().enableDebugMode(true);
-        Nd4j.getExecutioner().enableVerboseMode(true);
         INDArray in = Nd4j.linspace(DataType.FLOAT, 1, 6, 1).reshape(3,2);
         INDArray permute = Nd4j.createFromArray(1,0);
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/listeners/CheckpointListenerTest.java

@@ -63,8 +63,12 @@ public class CheckpointListenerTest extends BaseNd4jTest {
         return sd;
     }
 
-    public static DataSetIterator getIter(){
+    public static DataSetIterator getIter() {
-        return new IrisDataSetIterator(15, 150);
+        return getIter(15, 150);
+    }
+
+    public static DataSetIterator getIter(int batch, int totalExamples){
+        return new IrisDataSetIterator(batch, totalExamples);
     }
 
 
@@ -148,15 +152,15 @@ public class CheckpointListenerTest extends BaseNd4jTest {
 
         CheckpointListener l = new CheckpointListener.Builder(dir)
                 .keepLast(2)
-                .saveEvery(3, TimeUnit.SECONDS)
+                .saveEvery(1, TimeUnit.SECONDS)
                 .build();
         sd.setListeners(l);
 
-        DataSetIterator iter = getIter();
+        DataSetIterator iter = getIter(15, 150);
 
         for(int i=0; i<5; i++ ){   //10 iterations total
             sd.fit(iter, 1);
-            Thread.sleep(4000);
+            Thread.sleep(1000);
         }
 
         //Expect models saved at iterations: 10, 20, 30, 40

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/evaluation/EvalCustomThreshold.java

@@ -29,6 +29,7 @@ import org.nd4j.linalg.ops.transforms.Transforms;
 
 import java.util.Random;
 
+import static org.junit.Assert.assertArrayEquals;
 import static org.junit.Assert.assertEquals;
 
 /**

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/evaluation/EvaluationCalibrationTest.java

@@ -34,8 +34,7 @@ import java.util.ArrayList;
 import java.util.List;
 import java.util.Random;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertTrue;
 
 /**
  * Created by Alex on 05/07/2017.

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/evaluation/ROCTest.java

@@ -33,8 +33,7 @@ import org.nd4j.linalg.indexing.NDArrayIndex;
 
 import java.util.*;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertTrue;
 
 /**
  * Created by Alex on 04/11/2016.

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/ByteOrderTests.java

@@ -33,6 +33,7 @@ import org.nd4j.nativeblas.NativeOpsHolder;
 
 import java.util.Arrays;
 
+import static org.junit.Assert.assertArrayEquals;
 import static org.junit.Assert.assertEquals;
 
 @Slf4j

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java

@@ -121,7 +121,12 @@ public class TFGraphTestAllSameDiff {   //Note: Can't extend BaseNd4jTest here a
             "fused_batch_norm/.*",
 
             // AB 2020/01/04 - https://github.com/eclipse/deeplearning4j/issues/8592
-            "emptyArrayTests/reshape/rank2_shape2-0_2-0--1"
+            "emptyArrayTests/reshape/rank2_shape2-0_2-0--1",
+
+            //AB 2020/01/07 - Known issues
+            "bitcast/from_float64_to_int64",
+            "bitcast/from_rank2_float64_to_int64",
+            "bitcast/from_float64_to_uint64"
     };
 
     /* As per TFGraphTestList.printArraysDebugging - this field defines a set of regexes for test cases that should have

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TensorFlowImportTest.java

@@ -252,7 +252,7 @@ public class TensorFlowImportTest extends BaseNd4jTest {
         System.out.println(Arrays.toString(shape));
 
         // this is NHWC weights. will be changed soon.
-        assertArrayEquals(new int[]{5,5,1,32}, shape);
+        assertArrayEquals(new long[]{5,5,1,32}, shape);
         System.out.println(convNode);
     }
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/BaseNd4jTest.java

@@ -17,6 +17,7 @@
 package org.nd4j.linalg;
 
 
+import lombok.extern.slf4j.Slf4j;
 import lombok.val;
 import org.bytedeco.javacpp.Pointer;
 import org.junit.After;
@@ -26,6 +27,7 @@ import org.junit.rules.TestName;
 import org.junit.rules.Timeout;
 import org.junit.runner.RunWith;
 import org.junit.runners.Parameterized;
+import org.nd4j.BaseND4JTest;
 import org.nd4j.config.ND4JSystemProperties;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.memory.MemoryWorkspace;
@@ -40,30 +42,16 @@ import org.slf4j.LoggerFactory;
 import java.lang.management.ManagementFactory;
 import java.util.*;
 
+import static org.junit.Assume.assumeTrue;
+
 
 /**
  * Base Nd4j test
  * @author Adam Gibson
  */
 @RunWith(Parameterized.class)
-public abstract class BaseNd4jTest {
+@Slf4j
-    private static Logger log = LoggerFactory.getLogger(BaseNd4jTest.class);
+public abstract class BaseNd4jTest extends BaseND4JTest {
-
-    @Rule
-    public TestName testName = new TestName();
-
-    @Rule
-    public Timeout timeout = Timeout.seconds(testTimeoutMilliseconds());
-
-    /**
-     * Override this method to set the default timeout for methods in the class
-     */
-    public long testTimeoutMilliseconds(){
-        return 30000L;
-    }
-
-    protected long startTime;
-    protected int threadCountBefore;
 
     protected Nd4jBackend backend;
     protected String name;
@@ -80,16 +68,10 @@ public abstract class BaseNd4jTest {
     public BaseNd4jTest(String name, Nd4jBackend backend) {
         this.backend = backend;
         this.name = name;
-
-        //Suppress ND4J initialization - don't need this logged for every test...
-        System.setProperty(ND4JSystemProperties.LOG_INITIALIZATION, "false");
-        System.setProperty(ND4JSystemProperties.ND4J_IGNORE_AVX, "true");
-        System.gc();
     }
 
     public BaseNd4jTest(Nd4jBackend backend) {
         this(backend.getClass().getName() + UUID.randomUUID().toString(), backend);
-
     }
 
     private static List<Nd4jBackend> backends;
@@ -104,79 +86,6 @@ public abstract class BaseNd4jTest {
             if (backend.canRun() && backendsToRun.contains(backend.getClass().getName()) || backendsToRun.isEmpty())
                 backends.add(backend);
         }
-
-    }
-    public static void assertArrayEquals(String string, Object[] expecteds, Object[] actuals) {
-        org.junit.Assert.assertArrayEquals(string, expecteds, actuals);
-    }
-
-    public static void assertArrayEquals(Object[] expecteds, Object[] actuals) {
-        org.junit.Assert.assertArrayEquals(expecteds, actuals);
-    }
-
-    public static void assertArrayEquals(String string, long[] shapeA, long[] shapeB) {
-        org.junit.Assert.assertArrayEquals(string, shapeA, shapeB);
-    }
-
-    public static void assertArrayEquals(String string, byte[] shapeA, byte[] shapeB) {
-        org.junit.Assert.assertArrayEquals(string, shapeA, shapeB);
-    }
-
-    public static void assertArrayEquals(byte[] shapeA, byte[] shapeB) {
-        org.junit.Assert.assertArrayEquals(shapeA, shapeB);
-    }
-
-    public static void assertArrayEquals(long[] shapeA, long[] shapeB) {
-        org.junit.Assert.assertArrayEquals(shapeA, shapeB);
-    }
-
-    public static void assertArrayEquals(String string, int[] shapeA, long[] shapeB) {
-        org.junit.Assert.assertArrayEquals(string, ArrayUtil.toLongArray(shapeA), shapeB);
-    }
-
-    public static void assertArrayEquals(int[] shapeA, long[] shapeB) {
-        org.junit.Assert.assertArrayEquals(ArrayUtil.toLongArray(shapeA), shapeB);
-    }
-
-    public static void assertArrayEquals(String string, long[] shapeA, int[] shapeB) {
-        org.junit.Assert.assertArrayEquals(string, shapeA, ArrayUtil.toLongArray(shapeB));
-    }
-
-    public static void assertArrayEquals(long[] shapeA, int[] shapeB) {
-        org.junit.Assert.assertArrayEquals(shapeA, ArrayUtil.toLongArray(shapeB));
-    }
-
-    public static void assertArrayEquals(String string, int[] shapeA, int[] shapeB) {
-        org.junit.Assert.assertArrayEquals(string, shapeA, shapeB);
-    }
-
-    public static void assertArrayEquals(int[] shapeA, int[] shapeB) {
-        org.junit.Assert.assertArrayEquals(shapeA, shapeB);
-    }
-
-    public static void assertArrayEquals(String string, boolean[] shapeA, boolean[] shapeB) {
-        org.junit.Assert.assertArrayEquals(string, shapeA, shapeB);
-    }
-
-    public static void assertArrayEquals(boolean[] shapeA, boolean[] shapeB) {
-        org.junit.Assert.assertArrayEquals(shapeA, shapeB);
-    }
-
-
-    public static void assertArrayEquals(float[] shapeA, float[] shapeB, float delta) {
-        org.junit.Assert.assertArrayEquals(shapeA, shapeB, delta);
-    }
-
-    public static void assertArrayEquals(double[] shapeA, double[] shapeB, double delta) {
-        org.junit.Assert.assertArrayEquals(shapeA, shapeB, delta);
-    }
-
-    public static void assertArrayEquals(String string, float[] shapeA, float[] shapeB, float delta) {
-        org.junit.Assert.assertArrayEquals(string, shapeA, shapeB, delta);
-    }
-
-    public static void assertArrayEquals(String string, double[] shapeA, double[] shapeB, double delta) {
-        org.junit.Assert.assertArrayEquals(string, shapeA, shapeB, delta);
     }
 
     @Parameterized.Parameters(name = "{index}: backend({0})={1}")
@@ -187,6 +96,13 @@ public abstract class BaseNd4jTest {
         return ret;
     }
 
+    @Override
+    @Before
+    public void beforeTest(){
+        super.beforeTest();
+        Nd4j.factory().setOrder(ordering());
+    }
+
     /**
      * Get the default backend (jblas)
      * The default backend can be overridden by also passing:
@@ -207,106 +123,6 @@ public abstract class BaseNd4jTest {
     }
 
 
-
-    @Before
-    public void before() throws Exception {
-        //
-        log.info("Running {}.{} on {}", getClass().getName(), testName.getMethodName(), backend.getClass().getSimpleName());
-        Nd4j.getExecutioner().setProfilingConfig(ProfilerConfig.builder().build());
-        Nd4j nd4j = new Nd4j();
-        nd4j.initWithBackend(backend);
-        Nd4j.factory().setOrder(ordering());
-        NativeOpsHolder.getInstance().getDeviceNativeOps().enableDebugMode(false);
-        Nd4j.getExecutioner().enableDebugMode(false);
-        Nd4j.getExecutioner().enableVerboseMode(false);
-        Nd4j.setDefaultDataTypes(DataType.DOUBLE, DataType.DOUBLE);
-        startTime = System.currentTimeMillis();
-        threadCountBefore = ManagementFactory.getThreadMXBean().getThreadCount();
-    }
-
-    @After
-    public void after() throws Exception {
-        long totalTime = System.currentTimeMillis() - startTime;
-        Nd4j.getMemoryManager().purgeCaches();
-
-        logTestCompletion(totalTime);
-        if (System.getProperties().getProperty("backends") != null
-                        && !System.getProperty("backends").contains(backend.getClass().getName()))
-            return;
-        Nd4j nd4j = new Nd4j();
-        nd4j.initWithBackend(backend);
-        Nd4j.factory().setOrder(ordering());
-        NativeOpsHolder.getInstance().getDeviceNativeOps().enableDebugMode(false);
-        Nd4j.getExecutioner().enableDebugMode(false);
-        Nd4j.getExecutioner().enableVerboseMode(false);
-
-        //Attempt to keep workspaces isolated between tests
-        Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();
-        val currWS = Nd4j.getMemoryManager().getCurrentWorkspace();
-        Nd4j.getMemoryManager().setCurrentWorkspace(null);
-        if(currWS != null){
-            //Not really safe to continue testing under this situation... other tests will likely fail with obscure
-            // errors that are hard to track back to this
-            log.error("Open workspace leaked from test! Exiting - {}, isOpen = {} - {}", currWS.getId(), currWS.isScopeActive(), currWS);
-            System.exit(1);
-        }
-    }
-
-    public void logTestCompletion( long totalTime){
-        StringBuilder sb = new StringBuilder();
-        long maxPhys = Pointer.maxPhysicalBytes();
-        long maxBytes = Pointer.maxBytes();
-        long currPhys = Pointer.physicalBytes();
-        long currBytes = Pointer.totalBytes();
-
-        long jvmTotal = Runtime.getRuntime().totalMemory();
-        long jvmMax = Runtime.getRuntime().maxMemory();
-
-        int threadsAfter = ManagementFactory.getThreadMXBean().getThreadCount();
-        sb.append(getClass().getSimpleName()).append(".").append(testName.getMethodName())
-                .append(": ").append(totalTime).append(" ms")
-                .append(", threadCount: (").append(threadCountBefore).append("->").append(threadsAfter).append(")")
-                .append(", jvmTotal=").append(jvmTotal)
-                .append(", jvmMax=").append(jvmMax)
-                .append(", totalBytes=").append(currBytes).append(", maxBytes=").append(maxBytes)
-                .append(", currPhys=").append(currPhys).append(", maxPhys=").append(maxPhys);
-
-        List<MemoryWorkspace> ws = Nd4j.getWorkspaceManager().getAllWorkspacesForCurrentThread();
-        if(ws != null && ws.size() > 0){
-            long currSize = 0;
-            for(MemoryWorkspace w : ws){
-                currSize += w.getCurrentSize();
-            }
-            if(currSize > 0){
-                sb.append(", threadWSSize=").append(currSize)
-                        .append(" (").append(ws.size()).append(" WSs)");
-            }
-        }
-
-
-        Properties p = Nd4j.getExecutioner().getEnvironmentInformation();
-        Object o = p.get("cuda.devicesInformation");
-        if(o instanceof List){
-            List<Map<String,Object>> l = (List<Map<String, Object>>) o;
-            if(l.size() > 0) {
-
-                sb.append(" [").append(l.size())
-                        .append(" GPUs: ");
-
-                for (int i = 0; i < l.size(); i++) {
-                    Map<String,Object> m = l.get(i);
-                    if(i > 0)
-                        sb.append(",");
-                    sb.append("(").append(m.get("cuda.freeMemory")).append(" free, ")
-                            .append(m.get("cuda.totalMemory")).append(" total)");
-                }
-                sb.append("]");
-            }
-        }
-        log.info(sb.toString());
-    }
-
-
     /**
      * The ordering for this test
      * This test will only be invoked for
@@ -315,15 +131,10 @@ public abstract class BaseNd4jTest {
      * @return the ordering for this test
      */
     public char ordering() {
-        return 'a';
+        return 'c';
     }
 
-
-
     public String getFailureMessage() {
         return "Failed with backend " + backend.getClass().getName() + " and ordering " + ordering();
     }
-
-
-
 }

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/NDArrayTestsFortran.java

@@ -65,16 +65,6 @@ public class NDArrayTestsFortran extends BaseNd4jTest {
         super(backend);
     }
 
-    @Before
-    public void before() throws Exception {
-        super.before();
-    }
-
-    @After
-    public void after() throws Exception {
-        super.after();
-    }
-
 
 
     @Test

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java

@@ -133,13 +133,13 @@ public class Nd4jTestsC extends BaseNd4jTest {
     }
 
     @Override
-    public long testTimeoutMilliseconds() {
+    public long getTimeoutMilliseconds() {
         return 90000;
     }
 
     @Before
     public void before() throws Exception {
-        super.before();
+        super.beforeTest();
         Nd4j.setDataType(DataType.DOUBLE);
         Nd4j.getRandom().setSeed(123);
         Nd4j.getExecutioner().enableDebugMode(false);
@@ -148,7 +148,7 @@ public class Nd4jTestsC extends BaseNd4jTest {
 
     @After
     public void after() throws Exception {
-        super.after();
+        super.afterTest();
         Nd4j.setDataType(initialType);
     }
 
@@ -5331,7 +5331,8 @@ public class Nd4jTestsC extends BaseNd4jTest {
 
     @Test
     public void testNativeSort3() {
-        INDArray array = Nd4j.linspace(1, 1048576, 1048576, DataType.DOUBLE).reshape(1, -1);
+        int length = isIntegrationTests() ? 1048576 : 16484;
+        INDArray array = Nd4j.linspace(1, length, length, DataType.DOUBLE).reshape(1, -1);
         INDArray exp = array.dup();
         Nd4j.shuffle(array, 0);
 
@@ -7196,19 +7197,19 @@ public class Nd4jTestsC extends BaseNd4jTest {
 
         for( int i=-3; i<3; i++ ){
             INDArray out = Nd4j.stack(i, in, in2);
-            int[] expShape;
+            long[] expShape;
             switch (i){
                 case -3:
                 case 0:
-                    expShape = new int[]{2,3,4};
+                    expShape = new long[]{2,3,4};
                     break;
                 case -2:
                 case 1:
-                    expShape = new int[]{3,2,4};
+                    expShape = new long[]{3,2,4};
                     break;
                 case -1:
                 case 2:
-                    expShape = new int[]{3,4,2};
+                    expShape = new long[]{3,4,2};
                     break;
                 default:
                     throw new RuntimeException(String.valueOf(i));
@@ -7602,6 +7603,7 @@ public class Nd4jTestsC extends BaseNd4jTest {
         String wsName = "testRollingMeanWs";
         try {
             System.gc();
+            int iterations1 = isIntegrationTests() ? 5 : 2;
             for (int e = 0; e < 5; e++) {
                 try (val ws = Nd4j.getWorkspaceManager().getAndActivateWorkspace(wsconf, wsName)) {
                     val array = Nd4j.create(DataType.FLOAT, 32, 128, 256, 256);
@@ -7609,7 +7611,7 @@ public class Nd4jTestsC extends BaseNd4jTest {
                 }
             }
 
-            int iterations = 20;
+            int iterations = isIntegrationTests() ? 20 : 3;
             val timeStart = System.nanoTime();
             for (int e = 0; e < iterations; e++) {
                 try (val ws = Nd4j.getWorkspaceManager().getAndActivateWorkspace(wsconf, wsName)) {

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsComparisonC.java

@@ -57,13 +57,13 @@ public class Nd4jTestsComparisonC extends BaseNd4jTest {
 
     @Before
     public void before() throws Exception {
-        super.before();
+        super.beforeTest();
         DataTypeUtil.setDTypeForContext(DataType.DOUBLE);
     }
 
     @After
     public void after() throws Exception {
-        super.after();
+        super.afterTest();
         DataTypeUtil.setDTypeForContext(initialType);
     }
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsComparisonFortran.java

@@ -37,8 +37,7 @@ import org.slf4j.LoggerFactory;
 import java.util.List;
 import java.util.Random;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertTrue;
 
 /**
  * Tests comparing Nd4j ops to other libraries
@@ -59,7 +58,7 @@ public class Nd4jTestsComparisonFortran extends BaseNd4jTest {
 
     @Before
     public void before() throws Exception {
-        super.before();
+        super.beforeTest();
         DataTypeUtil.setDTypeForContext(DataType.DOUBLE);
         Nd4j.getRandom().setSeed(SEED);
 
@@ -67,7 +66,7 @@ public class Nd4jTestsComparisonFortran extends BaseNd4jTest {
 
     @After
     public void after() throws Exception {
-        super.after();
+        super.afterTest();
         DataTypeUtil.setDTypeForContext(initialType);
     }
 
@@ -197,7 +196,7 @@ public class Nd4jTestsComparisonFortran extends BaseNd4jTest {
                     INDArray gemv = m.mmul(v);
                     RealMatrix gemv2 = rm.multiply(rv);
 
-                    assertArrayEquals(new int[] {rows, 1}, gemv.shape());
+                    assertArrayEquals(new long[] {rows, 1}, gemv.shape());
                     assertArrayEquals(new int[] {rows, 1},
                                     new int[] {gemv2.getRowDimension(), gemv2.getColumnDimension()});
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/TestNDArrayCreationUtil.java

@@ -25,6 +25,8 @@ import org.nd4j.linalg.factory.Nd4jBackend;
 import org.nd4j.linalg.primitives.Pair;
 import org.nd4j.linalg.util.ArrayUtil;
 
+import static org.junit.Assert.assertArrayEquals;
+
 /**
  * Created by Alex on 30/04/2016.
  */

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/buffer/DoubleDataBufferTest.java

@@ -38,8 +38,7 @@ import org.nd4j.linalg.util.SerializationUtils;
 import java.io.*;
 import java.util.Arrays;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertTrue;
 
 /**
  * Double data buffer tests

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/buffer/FloatDataBufferTest.java

@@ -37,8 +37,7 @@ import org.nd4j.linalg.util.SerializationUtils;
 import java.io.*;
 import java.nio.ByteBuffer;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertTrue;
 
 /**
  * Float data buffer tests

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/buffer/IntDataBufferTests.java

@@ -31,8 +31,7 @@ import org.nd4j.linalg.factory.Nd4jBackend;
 import java.io.*;
 import java.util.Arrays;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertTrue;
 
 /**
  * Tests for INT INDArrays and DataBuffers serialization

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/api/indexing/IndexingTestsC.java

@@ -33,8 +33,7 @@ import org.nd4j.linalg.util.ArrayUtil;
 import java.util.Arrays;
 import java.util.Random;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertTrue;
 import static org.nd4j.linalg.indexing.NDArrayIndex.*;
 
 /**