Small number of test fixes (#220)

* Workaround for apache solr RNG algorithm enforcement in tests

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

* DeepWalk gradient check spam reduction

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

* Timeout increase

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

deeplearning4j/deeplearning4j-dataimport-solrj/src/test/java/org/deeplearning4j/nn/dataimport/solr/client/solrj/io/stream/TupleStreamDataSetIteratorTest.java

@@ -18,6 +18,8 @@ package org.deeplearning4j.nn.dataimport.solr.client.solrj.io.stream;
 
 import com.carrotsearch.randomizedtesting.annotations.ThreadLeakFilters;
 import com.carrotsearch.randomizedtesting.ThreadFilter;
+
+import java.security.SecureRandom;
 import java.util.ArrayList;
 import java.util.Collections;
 import java.util.List;
@@ -44,6 +46,18 @@ import org.nd4j.rng.deallocator.NativeRandomDeallocator;
 })
 public class TupleStreamDataSetIteratorTest extends SolrCloudTestCase {
 
+  static {
+    /*
+    This is a hack around the backend-dependent nature of secure random implementations
+    though we can set the secure random algorithm in our pom.xml files (via maven surefire and test.solr.allowed.securerandom)
+    there isn't a mechanism that is completely platform independent.
+    By setting it there (for example, to NativePRNG) that makes it pass on some platforms like Linux but fails on some JVMs on Windows
+    For testing purposes, we don't need strict guarantees around RNG, hence we don't want to enforce the RNG algorithm
+     */
+    String algorithm = new SecureRandom().getAlgorithm();
+    System.setProperty("test.solr.allowed.securerandom", algorithm);
+  }
+
   public static class PrivateDeallocatorThreadsFilter implements ThreadFilter {
     /**
      * Reject deallocator threads over whose cleanup this test has no control.

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

@@ -66,7 +66,7 @@ public class DeepWalkGradientCheck extends BaseDL4JTest {
         for (int i = 0; i < 7; i++) {
             INDArray vector = deepWalk.getVertexVector(i);
             assertArrayEquals(new long[] {vectorSize}, vector.shape());
-            System.out.println(Arrays.toString(vector.dup().data().asFloat()));
+//            System.out.println(Arrays.toString(vector.dup().data().asFloat()));
         }
 
         GraphWalkIterator<String> iter = new RandomWalkIterator<>(graph, 8);
@@ -182,10 +182,10 @@ public class DeepWalkGradientCheck extends BaseDL4JTest {
 
                     if (relError > MAX_REL_ERROR && absErr > MIN_ABS_ERROR)
                         fail(msg);
-                    else
+//                    else
-                        System.out.println(msg);
+//                        System.out.println(msg);
                 }
-                System.out.println();
+//                System.out.println();
             }
 
         }
@@ -216,7 +216,7 @@ public class DeepWalkGradientCheck extends BaseDL4JTest {
         for (int i = 0; i < nVertices; i++) {
             INDArray vector = deepWalk.getVertexVector(i);
             assertArrayEquals(new long[] {vectorSize}, vector.shape());
-            System.out.println(Arrays.toString(vector.dup().data().asFloat()));
+//            System.out.println(Arrays.toString(vector.dup().data().asFloat()));
         }
 
         GraphWalkIterator<String> iter = new RandomWalkIterator<>(graph, 10);
@@ -295,8 +295,8 @@ public class DeepWalkGradientCheck extends BaseDL4JTest {
 
                         if (relError > MAX_REL_ERROR && absErr > minAbsError)
                             fail(msg);
-                        else
+//                        else
-                            System.out.println(msg);
+//                            System.out.println(msg);
                     }
                 }
 

deeplearning4j/deeplearning4j-modelexport-solr/src/test/java/org/deeplearning4j/nn/modelexport/solr/handler/ModelTupleStreamIntegrationTest.java

@@ -18,6 +18,7 @@ package org.deeplearning4j.nn.modelexport.solr.handler;
 
 import java.io.File;
 import java.nio.file.Path;
+import java.security.SecureRandom;
 
 import com.carrotsearch.randomizedtesting.ThreadFilter;
 import com.carrotsearch.randomizedtesting.annotations.ThreadLeakFilters;
@@ -49,6 +50,19 @@ import org.nd4j.rng.deallocator.NativeRandomDeallocator;
 })
 public class ModelTupleStreamIntegrationTest extends SolrCloudTestCase {
 
+  static {
+    /*
+    This is a hack around the backend-dependent nature of secure random implementations
+    though we can set the secure random algorithm in our pom.xml files (via maven surefire and test.solr.allowed.securerandom)
+    there isn't a mechanism that is completely platform independent.
+    By setting it there (for example, to NativePRNG) that makes it pass on some platforms like Linux but fails on some JVMs on Windows
+    For testing purposes, we don't need strict guarantees around RNG, hence we don't want to enforce the RNG algorithm
+     */
+    String algorithm = new SecureRandom().getAlgorithm();
+    System.setProperty("test.solr.allowed.securerandom", algorithm);
+  }
+
+
   public static class PrivateDeallocatorThreadsFilter implements ThreadFilter {
     /**
      * Reject deallocator threads over whose cleanup this test has no control.

deeplearning4j/deeplearning4j-modelexport-solr/src/test/java/org/deeplearning4j/nn/modelexport/solr/handler/ModelTupleStreamTest.java

@@ -19,6 +19,7 @@ package org.deeplearning4j.nn.modelexport.solr.handler;
 import java.io.File;
 import java.nio.file.Files;
 import java.nio.file.Path;
+import java.security.SecureRandom;
 import java.util.ArrayList;
 import java.util.List;
 import java.util.Map;
@@ -58,6 +59,18 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 
 public class ModelTupleStreamTest {
 
+  static {
+    /*
+    This is a hack around the backend-dependent nature of secure random implementations
+    though we can set the secure random algorithm in our pom.xml files (via maven surefire and test.solr.allowed.securerandom)
+    there isn't a mechanism that is completely platform independent.
+    By setting it there (for example, to NativePRNG) that makes it pass on some platforms like Linux but fails on some JVMs on Windows
+    For testing purposes, we don't need strict guarantees around RNG, hence we don't want to enforce the RNG algorithm
+     */
+    String algorithm = new SecureRandom().getAlgorithm();
+    System.setProperty("test.solr.allowed.securerandom", algorithm);
+  }
+
   protected List<float[]> floatsList(int numFloats) {
     final List<float[]> floatsList = new ArrayList<float[]>();
     final float[] floats0 = new float[numFloats];

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

@@ -34,7 +34,7 @@ public class MiscTests extends BaseDL4JTest {
 
     @Override
     public long getTimeoutMilliseconds() {
-        return 120000L;
+        return 240000L;
     }
 
     @Test