diff --git a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/WordVectorSerializerTest.java b/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/WordVectorSerializerTest.java index 7807ff711..0154bc732 100755 --- a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/WordVectorSerializerTest.java +++ b/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()); } } - } diff --git a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/word2vec/Word2VecTests.java b/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/word2vec/Word2VecTests.java index e50a95443..7dcfb160a 100755 --- a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/test/java/org/deeplearning4j/models/word2vec/Word2VecTests.java +++ b/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(); diff --git a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/TsneTest.java b/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/TsneTest.java index c99cb3b9a..cf0e7c7a3 100644 --- a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/TsneTest.java +++ b/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 diff --git a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/paragraphvectors/ParagraphVectorsTest.java b/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/paragraphvectors/ParagraphVectorsTest.java index 95cd4e9a6..14495ffaf 100644 --- a/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/models/paragraphvectors/ParagraphVectorsTest.java +++ b/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) - .trainWordVectors(true).vocabCache(cache).tokenizerFactory(t).negativeSample(0) - .useHierarchicSoftmax(true).sampling(0).workers(1).usePreciseWeightInit(true) - .sequenceLearningAlgorithm(new DM()).build(); + .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) + .sequenceLearningAlgorithm(new DM()).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; - if(isIntegration){ - iter = new BasicLineIterator(resource); - } else { - List 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); - } + SentenceIterator iter = getIterator(isIntegration, resource); 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 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 data = MLUtils .loadLibSVMFile(sc.sc(), @@ -125,7 +142,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest { } - @Test(timeout = 120000L) + @Test public void testFromSvmLight() throws Exception { JavaRDD 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;