Test fixes (#218)

* Test speedups / integration test run only for CUDA - NLP

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

* nlp-uima CUDA slow tests

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

* Spark CUDA timeout fixes

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

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

@@ -16,25 +16,19 @@
 
 package org.deeplearning4j.models;
 
-import org.junit.rules.Timeout;
-import org.nd4j.shade.guava.io.Files;
-import org.nd4j.shade.guava.primitives.Doubles;
 import lombok.val;
 import org.apache.commons.io.FileUtils;
 import org.apache.commons.lang.ArrayUtils;
 import org.apache.commons.lang3.RandomUtils;
 import org.deeplearning4j.BaseDL4JTest;
-import org.deeplearning4j.models.sequencevectors.SequenceVectors;
-import org.deeplearning4j.models.sequencevectors.serialization.VocabWordFactory;
-import org.junit.Rule;
-import org.junit.rules.TemporaryFolder;
-import org.nd4j.linalg.io.ClassPathResource;
 import org.deeplearning4j.models.embeddings.WeightLookupTable;
 import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
 import org.deeplearning4j.models.embeddings.loader.VectorsConfiguration;
 import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
 import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
 import org.deeplearning4j.models.paragraphvectors.ParagraphVectors;
+import org.deeplearning4j.models.sequencevectors.SequenceVectors;
+import org.deeplearning4j.models.sequencevectors.serialization.VocabWordFactory;
 import org.deeplearning4j.models.word2vec.VocabWord;
 import org.deeplearning4j.models.word2vec.Word2Vec;
 import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
@@ -48,11 +42,16 @@ import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFac
 import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
 import org.junit.Before;
 import org.junit.Ignore;
+import org.junit.Rule;
 import org.junit.Test;
+import org.junit.rules.TemporaryFolder;
+import org.junit.rules.Timeout;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.io.ClassPathResource;
 import org.nd4j.linalg.ops.transforms.Transforms;
 import org.nd4j.resources.Resources;
+import org.nd4j.shade.guava.primitives.Doubles;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
@@ -272,7 +271,14 @@ public class WordVectorSerializerTest extends BaseDL4JTest {
 
     @Test
     public void testFullModelSerialization() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         File inputFile = Resources.asFile("big/raw_sentences.txt");
+
+
         SentenceIterator iter = UimaSentenceIterator.createWithPath(inputFile.getAbsolutePath());
         // Split on white spaces in the line to get words
         TokenizerFactory t = new DefaultTokenizerFactory();
@@ -892,5 +898,4 @@ public class WordVectorSerializerTest extends BaseDL4JTest {
             fail(e.getMessage());
         }
     }
-
 }

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

@@ -159,6 +159,11 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testWord2VecCBOW() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
 
         TokenizerFactory t = new DefaultTokenizerFactory();
@@ -188,6 +193,11 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testWord2VecMultiEpoch() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         SentenceIterator iter;
         if(isIntegrationTests()){
             iter = new BasicLineIterator(inputFile.getAbsolutePath());
@@ -220,6 +230,11 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void reproducibleResults_ForMultipleRuns() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         log.info("reproducibleResults_ForMultipleRuns");
         val shakespear = new ClassPathResource("big/rnj.txt");
         val basic = new ClassPathResource("big/rnj.txt");
@@ -274,6 +289,11 @@ public class Word2VecTests extends BaseDL4JTest {
     
     @Test
     public void testRunWord2Vec() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         // Strip white space before and after for each line
         /*val shakespear = new ClassPathResource("big/rnj.txt");
         SentenceIterator iter = new BasicLineIterator(shakespear.getFile());*/
@@ -363,6 +383,11 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testLoadingWordVectors() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         File modelFile = new File(pathToWriteto);
         if (!modelFile.exists()) {
             testRunWord2Vec();
@@ -396,6 +421,11 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testW2VnegativeOnRestore() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         // Strip white space before and after for each line
         SentenceIterator iter;
         if(isIntegrationTests()){
@@ -453,6 +483,11 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testUnknown1() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         // Strip white space before and after for each line
         SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
         // Split on white spaces in the line to get words
@@ -688,6 +723,10 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testWordVectorsPartiallyAbsentLabels() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
 
         SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
         // Split on white spaces in the line to get words
@@ -720,6 +759,10 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testWordVectorsAbsentLabels() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
 
         SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
         // Split on white spaces in the line to get words
@@ -745,6 +788,10 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testWordVectorsAbsentLabels_WithUnknown() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
 
         SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
         // Split on white spaces in the line to get words
@@ -814,6 +861,10 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void weightsNotUpdated_WhenLocked_CBOW() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
 
         SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
 
@@ -851,6 +902,11 @@ public class Word2VecTests extends BaseDL4JTest {
 
     @Test
     public void testWordsNearestSum() throws IOException {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
+        }
+
         log.info("Load & Vectorize Sentences....");
         SentenceIterator iter = new BasicLineIterator(inputFile);
         TokenizerFactory t = new DefaultTokenizerFactory();

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

@@ -48,12 +48,22 @@ public class TsneTest extends BaseDL4JTest {
 
     @Override
     public long getTimeoutMilliseconds() {
-        return 60000L;
+        return 180000L;
     }
 
     @Rule
     public TemporaryFolder testDir = new TemporaryFolder();
 
+    @Override
+    public DataType getDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Override
+    public DataType getDefaultFPDataType() {
+        return DataType.FLOAT;
+    }
+
     @Test
     public void testSimple() throws Exception {
         //Simple sanity check

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

@@ -32,6 +32,7 @@ import org.deeplearning4j.models.sequencevectors.transformers.impl.iterables.Par
 import org.deeplearning4j.text.sentenceiterator.*;
 import org.junit.Rule;
 import org.junit.rules.TemporaryFolder;
+import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.io.ClassPathResource;
 import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
 import org.deeplearning4j.models.embeddings.learning.impl.elements.SkipGram;
@@ -80,12 +81,21 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
 
     @Override
     public long getTimeoutMilliseconds() {
-        return 240000;
+        return isIntegrationTests() ? 600_000 : 240_000;
     }
 
     @Rule
     public TemporaryFolder testDir = new TemporaryFolder();
 
+    @Override
+    public DataType getDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Override
+    public DataType getDefaultFPDataType() {
+        return DataType.FLOAT;
+    }
 
     /*
     @Test
@@ -359,8 +369,13 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
     }
 
 
-    @Test(timeout = 300000)
+    @Test
     public void testParagraphVectorsDM() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //Skip CUDA except for integration tests due to very slow test speed
+        }
+
         File file = Resources.asFile("/big/raw_sentences.txt");
         SentenceIterator iter = new BasicLineIterator(file);
 
@@ -372,10 +387,10 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
         LabelsSource source = new LabelsSource("DOC_");
 
         ParagraphVectors vec = new ParagraphVectors.Builder().minWordFrequency(1).iterations(2).seed(119).epochs(1)
-                        .layerSize(100).learningRate(0.025).labelsSource(source).windowSize(5).iterate(iter)
+                .layerSize(100).learningRate(0.025).labelsSource(source).windowSize(5).iterate(iter)
-                        .trainWordVectors(true).vocabCache(cache).tokenizerFactory(t).negativeSample(0)
+                .trainWordVectors(true).vocabCache(cache).tokenizerFactory(t).negativeSample(0)
-                        .useHierarchicSoftmax(true).sampling(0).workers(1).usePreciseWeightInit(true)
+                .useHierarchicSoftmax(true).sampling(0).workers(1).usePreciseWeightInit(true)
-                        .sequenceLearningAlgorithm(new DM<VocabWord>()).build();
+                .sequenceLearningAlgorithm(new DM<VocabWord>()).build();
 
         vec.fit();
 
@@ -404,7 +419,9 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
 
         double similarityX = vec.similarity("DOC_3720", "DOC_9852");
         log.info("3720/9852 similarity: " + similarityX);
-        assertTrue(similarityX < 0.5d);
+        if(isIntegrationTests()) {
+            assertTrue(similarityX < 0.5d);
+        }
 
 
         // testing DM inference now
@@ -418,7 +435,6 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
 
         log.info("Cos O/A: {}", cosAO1);
         log.info("Cos A/B: {}", cosAB1);
-
     }
 
 
@@ -501,6 +517,11 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
 
     @Test(timeout = 300000)
     public void testParagraphVectorsWithWordVectorsModelling1() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //Skip CUDA except for integration tests due to very slow test speed
+        }
+
         File file = Resources.asFile("/big/raw_sentences.txt");
         SentenceIterator iter = new BasicLineIterator(file);
 
@@ -705,8 +726,12 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
         In this test we'll build w2v model, and will use it's vocab and weights for ParagraphVectors.
         there's no need in this test within travis, use it manually only for problems detection
     */
-    @Test(timeout = 300000)
+    @Test
     public void testParagraphVectorsOverExistingWordVectorsModel() throws Exception {
+        String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
+        if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
+            skipUnlessIntegrationTests(); //Skip CUDA except for integration tests due to very slow test speed
+        }
 
         // we build w2v from multiple sources, to cover everything
         File resource_sentences = Resources.asFile("/big/raw_sentences.txt");
@@ -997,14 +1022,18 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
         log.info("SimilarityB: {}", simB);
     }
 
-    @Test(timeout = 300000)
+    @Test
+    @Ignore //AB 2020/02/06 - https://github.com/eclipse/deeplearning4j/issues/8677
     public void testDirectInference() throws Exception {
-        File resource_sentences = Resources.asFile("/big/raw_sentences.txt");
+        boolean isIntegration = isIntegrationTests();
+        File resource = Resources.asFile("/big/raw_sentences.txt");
+        SentenceIterator sentencesIter = getIterator(isIntegration, resource);
+
         ClassPathResource resource_mixed = new ClassPathResource("paravec/");
         File local_resource_mixed = testDir.newFolder();
         resource_mixed.copyDirectory(local_resource_mixed);
         SentenceIterator iter = new AggregatingSentenceIterator.Builder()
-                        .addSentenceIterator(new BasicLineIterator(resource_sentences))
+                        .addSentenceIterator(sentencesIter)
                         .addSentenceIterator(new FileSentenceIterator(local_resource_mixed)).build();
 
         TokenizerFactory t = new DefaultTokenizerFactory();
@@ -1154,24 +1183,7 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
     public void testDoubleFit() throws Exception {
         boolean isIntegration = isIntegrationTests();
         File resource = Resources.asFile("/big/raw_sentences.txt");
-        SentenceIterator iter;
+        SentenceIterator iter = getIterator(isIntegration, resource);
-        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();
@@ -1197,6 +1209,30 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
 
         assertEquals(num1, num2);
     }
+
+    public static SentenceIterator getIterator(boolean isIntegration, File file) throws IOException {
+        return getIterator(isIntegration, file, 500);
+    }
+
+    public static SentenceIterator getIterator(boolean isIntegration, File file, int linesForUnitTest) throws IOException {
+        if(isIntegration){
+            return new BasicLineIterator(file);
+        } else {
+            List<String> lines = new ArrayList<>();
+            try(InputStream is = new BufferedInputStream(new FileInputStream(file))){
+                LineIterator lineIter = IOUtils.lineIterator(is, StandardCharsets.UTF_8);
+                try{
+                    for( int i=0; i<linesForUnitTest && lineIter.hasNext(); i++ ){
+                        lines.add(lineIter.next());
+                    }
+                } finally {
+                    lineIter.close();
+                }
+            }
+
+            return new CollectionSentenceIterator(lines);
+        }
+    }
 }
 
 

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/word2vec/Word2VecTestsSmall.java

@@ -19,6 +19,7 @@ package org.deeplearning4j.models.word2vec;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
 import org.deeplearning4j.BaseDL4JTest;
+import org.deeplearning4j.models.paragraphvectors.ParagraphVectorsTest;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
@@ -56,6 +57,11 @@ import static org.junit.Assert.assertEquals;
 public class Word2VecTestsSmall extends BaseDL4JTest {
     WordVectors word2vec;
 
+    @Override
+    public long getTimeoutMilliseconds() {
+        return isIntegrationTests() ? 240000 : 60000;
+    }
+
     @Before
     public void setUp() throws Exception {
         word2vec = WordVectorSerializer.readWord2VecModel(new ClassPathResource("vec.bin").getFile());
@@ -85,8 +91,8 @@ public class Word2VecTestsSmall extends BaseDL4JTest {
     @Test(timeout = 300000)
     public void testUnkSerialization_1() throws Exception {
         val inputFile = Resources.asFile("big/raw_sentences.txt");
-
+//        val iter = new BasicLineIterator(inputFile);
-        val iter = new BasicLineIterator(inputFile);
+        val iter = ParagraphVectorsTest.getIterator(isIntegrationTests(), inputFile);
         val t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());
 
@@ -147,8 +153,8 @@ public class Word2VecTestsSmall extends BaseDL4JTest {
         Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
 
         val inputFile = Resources.asFile("big/raw_sentences.txt");
-
+        val iter = ParagraphVectorsTest.getIterator(isIntegrationTests(), inputFile);
-        val iter = new BasicLineIterator(inputFile);
+//        val iter = new BasicLineIterator(inputFile);
         val t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());
 

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

@@ -17,6 +17,7 @@
 package org.deeplearning4j.models.word2vec.iterator;
 
 import org.deeplearning4j.BaseDL4JTest;
+import org.deeplearning4j.models.paragraphvectors.ParagraphVectorsTest;
 import org.nd4j.linalg.io.ClassPathResource;
 import org.deeplearning4j.models.embeddings.learning.impl.elements.CBOW;
 import org.deeplearning4j.models.embeddings.reader.impl.BasicModelUtils;
@@ -59,7 +60,8 @@ public class Word2VecDataSetIteratorTest extends BaseDL4JTest {
     public void testIterator1() throws Exception {
 
         File inputFile = Resources.asFile("big/raw_sentences.txt");
-        SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
+        SentenceIterator iter = ParagraphVectorsTest.getIterator(isIntegrationTests(), inputFile);
+//        SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
 
         TokenizerFactory t = new DefaultTokenizerFactory();
         t.setTokenPreProcessor(new CommonPreprocessor());

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

@@ -58,6 +58,7 @@ import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.evaluation.classification.ROC;
 import org.nd4j.evaluation.classification.ROCMultiClass;
 import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.dataset.MultiDataSet;
@@ -93,7 +94,23 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     @Rule
     public TemporaryFolder testDir = new TemporaryFolder();
 
-    @Test(timeout = 120000L)
+
+    @Override
+    public long getTimeoutMilliseconds() {
+        return 120000L;
+    }
+
+    @Override
+    public DataType getDefaultFPDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Override
+    public DataType getDataType() {
+        return DataType.FLOAT;
+    }
+
+    @Test
     public void testFromSvmLightBackprop() throws Exception {
         JavaRDD<LabeledPoint> data = MLUtils
                         .loadLibSVMFile(sc.sc(),
@@ -125,7 +142,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L)
+    @Test
     public void testFromSvmLight() throws Exception {
         JavaRDD<LabeledPoint> data = MLUtils
                         .loadLibSVMFile(sc.sc(),
@@ -155,7 +172,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         master.fitLabeledPoint(data);
     }
 
-    @Test(timeout = 120000L)
+    @Test
     public void testRunIteration() {
 
         DataSet dataSet = new IrisDataSetIterator(5, 5).next();
@@ -175,7 +192,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         assertEquals(expectedParams.size(1), actualParams.size(1));
     }
 
-    @Test(timeout = 120000L)
+    @Test
     public void testUpdaters() {
         SparkDl4jMultiLayer sparkNet = getBasicNetwork();
         MultiLayerNetwork netCopy = sparkNet.getNetwork().clone();
@@ -197,7 +214,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L)
+    @Test
     public void testEvaluation() {
 
         SparkDl4jMultiLayer sparkNet = getBasicNetwork();
@@ -228,7 +245,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         }
     }
 
-    @Test(timeout = 120000L)
+    @Test
     public void testSmallAmountOfData() {
         //Idea: Test spark training where some executors don't get any data
         //in this case: by having fewer examples (2 DataSets) than executors (local[*])
@@ -255,7 +272,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
 
     }
 
-    @Test(timeout = 120000L)
+    @Test
     public void testDistributedScoring() {
 
         MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().l1(0.1).l2(0.1)
@@ -333,7 +350,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
 
 
 
-    @Test(timeout = 120000L)
+    @Test
     public void testParameterAveragingMultipleExamplesPerDataSet() throws Exception {
         int dataSetObjSize = 5;
         int batchSizePerExecutor = 25;
@@ -382,7 +399,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L)
+    @Test
     public void testFitViaStringPaths() throws Exception {
 
         Path tempDir = testDir.newFolder("DL4J-testFitViaStringPaths").toPath();
@@ -445,7 +462,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         sparkNet.getTrainingMaster().deleteTempFiles(sc);
     }
 
-    @Test(timeout = 120000L)
+    @Test
     public void testFitViaStringPathsSize1() throws Exception {
 
         Path tempDir = testDir.newFolder("DL4J-testFitViaStringPathsSize1").toPath();
@@ -525,7 +542,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L)
+    @Test
     public void testFitViaStringPathsCompGraph() throws Exception {
 
         Path tempDir = testDir.newFolder("DL4J-testFitViaStringPathsCG").toPath();
@@ -618,7 +635,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L)
+    @Test
     @Ignore("AB 2019/05/23 - Failing on CI only - passing locally. Possible precision or threading issue")
     public void testSeedRepeatability() throws Exception {
 
@@ -691,7 +708,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L)
+    @Test
     public void testIterationCounts() throws Exception {
         int dataSetObjSize = 5;
         int batchSizePerExecutor = 25;
@@ -737,7 +754,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         }
     }
 
-    @Test(timeout = 120000L)
+    @Test
     public void testIterationCountsGraph() throws Exception {
         int dataSetObjSize = 5;
         int batchSizePerExecutor = 25;
@@ -783,7 +800,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L) @Ignore   //Ignored 2019/04/09 - low priority: https://github.com/deeplearning4j/deeplearning4j/issues/6656
+    @Test
+    @Ignore   //Ignored 2019/04/09 - low priority: https://github.com/deeplearning4j/deeplearning4j/issues/6656
     public void testVaePretrainSimple() {
         //Simple sanity check on pretraining
         int nIn = 8;
@@ -818,7 +836,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         sparkNet.fit(data);
     }
 
-    @Test(timeout = 120000L) @Ignore    //Ignored 2019/04/09 - low priority: https://github.com/deeplearning4j/deeplearning4j/issues/6656
+    @Test
+    @Ignore    //Ignored 2019/04/09 - low priority: https://github.com/deeplearning4j/deeplearning4j/issues/6656
     public void testVaePretrainSimpleCG() {
         //Simple sanity check on pretraining
         int nIn = 8;
@@ -854,7 +873,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L)
+    @Test
     public void testROC() {
 
         int nArrays = 100;
@@ -909,7 +928,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
     }
 
 
-    @Test(timeout = 120000L)
+    @Test
     public void testROCMultiClass() {
 
         int nArrays = 100;