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>master
Alex Black
GitHub
parent
948646b32d
commit
ce6848c9fe
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@ -16,25 +16,19 @@
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package org.deeplearning4j.models;
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import org.junit.rules.Timeout;
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import org.nd4j.shade.guava.io.Files;
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import org.nd4j.shade.guava.primitives.Doubles;
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import lombok.val;
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import org.apache.commons.io.FileUtils;
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import org.apache.commons.lang.ArrayUtils;
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import org.apache.commons.lang3.RandomUtils;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.models.sequencevectors.SequenceVectors;
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import org.deeplearning4j.models.sequencevectors.serialization.VocabWordFactory;
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import org.junit.Rule;
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import org.junit.rules.TemporaryFolder;
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import org.nd4j.linalg.io.ClassPathResource;
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import org.deeplearning4j.models.embeddings.WeightLookupTable;
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import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
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import org.deeplearning4j.models.embeddings.loader.VectorsConfiguration;
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import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
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import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
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import org.deeplearning4j.models.paragraphvectors.ParagraphVectors;
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import org.deeplearning4j.models.sequencevectors.SequenceVectors;
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import org.deeplearning4j.models.sequencevectors.serialization.VocabWordFactory;
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import org.deeplearning4j.models.word2vec.VocabWord;
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import org.deeplearning4j.models.word2vec.Word2Vec;
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import org.deeplearning4j.models.word2vec.wordstore.VocabCache;
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@ -48,11 +42,16 @@ import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFac
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import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
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import org.junit.Before;
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import org.junit.Ignore;
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import org.junit.Rule;
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import org.junit.Test;
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import org.junit.rules.TemporaryFolder;
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import org.junit.rules.Timeout;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.factory.Nd4j;
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import org.nd4j.linalg.io.ClassPathResource;
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import org.nd4j.linalg.ops.transforms.Transforms;
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import org.nd4j.resources.Resources;
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import org.nd4j.shade.guava.primitives.Doubles;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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@ -272,7 +271,14 @@ public class WordVectorSerializerTest extends BaseDL4JTest {
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@Test
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public void testFullModelSerialization() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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File inputFile = Resources.asFile("big/raw_sentences.txt");
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SentenceIterator iter = UimaSentenceIterator.createWithPath(inputFile.getAbsolutePath());
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// Split on white spaces in the line to get words
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TokenizerFactory t = new DefaultTokenizerFactory();
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@ -892,5 +898,4 @@ public class WordVectorSerializerTest extends BaseDL4JTest {
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fail(e.getMessage());
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}
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}
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}
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@ -159,6 +159,11 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testWord2VecCBOW() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
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TokenizerFactory t = new DefaultTokenizerFactory();
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@ -188,6 +193,11 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testWord2VecMultiEpoch() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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SentenceIterator iter;
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if(isIntegrationTests()){
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iter = new BasicLineIterator(inputFile.getAbsolutePath());
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@ -220,6 +230,11 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void reproducibleResults_ForMultipleRuns() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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log.info("reproducibleResults_ForMultipleRuns");
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val shakespear = new ClassPathResource("big/rnj.txt");
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val basic = new ClassPathResource("big/rnj.txt");
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@ -274,6 +289,11 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testRunWord2Vec() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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// Strip white space before and after for each line
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/*val shakespear = new ClassPathResource("big/rnj.txt");
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SentenceIterator iter = new BasicLineIterator(shakespear.getFile());*/
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@ -363,6 +383,11 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testLoadingWordVectors() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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File modelFile = new File(pathToWriteto);
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if (!modelFile.exists()) {
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testRunWord2Vec();
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@ -396,6 +421,11 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testW2VnegativeOnRestore() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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// Strip white space before and after for each line
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SentenceIterator iter;
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if(isIntegrationTests()){
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@ -453,6 +483,11 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testUnknown1() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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// Strip white space before and after for each line
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SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
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// Split on white spaces in the line to get words
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@ -688,6 +723,10 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testWordVectorsPartiallyAbsentLabels() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
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// Split on white spaces in the line to get words
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@ -720,6 +759,10 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testWordVectorsAbsentLabels() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
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// Split on white spaces in the line to get words
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@ -745,6 +788,10 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testWordVectorsAbsentLabels_WithUnknown() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
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// Split on white spaces in the line to get words
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@ -814,6 +861,10 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void weightsNotUpdated_WhenLocked_CBOW() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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SentenceIterator iter = new BasicLineIterator(inputFile.getAbsolutePath());
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@ -851,6 +902,11 @@ public class Word2VecTests extends BaseDL4JTest {
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@Test
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public void testWordsNearestSum() throws IOException {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //AB 2020/02/06 Skip CUDA except for integration tests due to very slow test speed - > 5 minutes on Titan X
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}
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log.info("Load & Vectorize Sentences....");
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SentenceIterator iter = new BasicLineIterator(inputFile);
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TokenizerFactory t = new DefaultTokenizerFactory();
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@ -48,12 +48,22 @@ public class TsneTest extends BaseDL4JTest {
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@Override
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public long getTimeoutMilliseconds() {
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return 60000L;
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return 180000L;
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}
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@Rule
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public TemporaryFolder testDir = new TemporaryFolder();
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@Override
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public DataType getDataType() {
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return DataType.FLOAT;
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}
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@Override
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public DataType getDefaultFPDataType() {
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return DataType.FLOAT;
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}
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@Test
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public void testSimple() throws Exception {
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//Simple sanity check
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@ -32,6 +32,7 @@ import org.deeplearning4j.models.sequencevectors.transformers.impl.iterables.Par
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import org.deeplearning4j.text.sentenceiterator.*;
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import org.junit.Rule;
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import org.junit.rules.TemporaryFolder;
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import org.nd4j.linalg.api.buffer.DataType;
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import org.nd4j.linalg.io.ClassPathResource;
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import org.deeplearning4j.models.embeddings.inmemory.InMemoryLookupTable;
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import org.deeplearning4j.models.embeddings.learning.impl.elements.SkipGram;
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@ -80,12 +81,21 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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@Override
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public long getTimeoutMilliseconds() {
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return 240000;
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return isIntegrationTests() ? 600_000 : 240_000;
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}
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@Rule
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public TemporaryFolder testDir = new TemporaryFolder();
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@Override
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public DataType getDataType() {
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return DataType.FLOAT;
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}
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@Override
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public DataType getDefaultFPDataType() {
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return DataType.FLOAT;
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}
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/*
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@Test
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@ -359,8 +369,13 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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}
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@Test(timeout = 300000)
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@Test
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public void testParagraphVectorsDM() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //Skip CUDA except for integration tests due to very slow test speed
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}
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File file = Resources.asFile("/big/raw_sentences.txt");
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SentenceIterator iter = new BasicLineIterator(file);
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@ -372,10 +387,10 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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LabelsSource source = new LabelsSource("DOC_");
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ParagraphVectors vec = new ParagraphVectors.Builder().minWordFrequency(1).iterations(2).seed(119).epochs(1)
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.layerSize(100).learningRate(0.025).labelsSource(source).windowSize(5).iterate(iter)
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.trainWordVectors(true).vocabCache(cache).tokenizerFactory(t).negativeSample(0)
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.useHierarchicSoftmax(true).sampling(0).workers(1).usePreciseWeightInit(true)
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.sequenceLearningAlgorithm(new DM<VocabWord>()).build();
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.layerSize(100).learningRate(0.025).labelsSource(source).windowSize(5).iterate(iter)
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.trainWordVectors(true).vocabCache(cache).tokenizerFactory(t).negativeSample(0)
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.useHierarchicSoftmax(true).sampling(0).workers(1).usePreciseWeightInit(true)
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.sequenceLearningAlgorithm(new DM<VocabWord>()).build();
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vec.fit();
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@ -404,7 +419,9 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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double similarityX = vec.similarity("DOC_3720", "DOC_9852");
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log.info("3720/9852 similarity: " + similarityX);
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assertTrue(similarityX < 0.5d);
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if(isIntegrationTests()) {
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assertTrue(similarityX < 0.5d);
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}
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// testing DM inference now
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@ -418,7 +435,6 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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log.info("Cos O/A: {}", cosAO1);
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log.info("Cos A/B: {}", cosAB1);
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}
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@ -501,6 +517,11 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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@Test(timeout = 300000)
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public void testParagraphVectorsWithWordVectorsModelling1() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //Skip CUDA except for integration tests due to very slow test speed
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}
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File file = Resources.asFile("/big/raw_sentences.txt");
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SentenceIterator iter = new BasicLineIterator(file);
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@ -705,8 +726,12 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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In this test we'll build w2v model, and will use it's vocab and weights for ParagraphVectors.
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there's no need in this test within travis, use it manually only for problems detection
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*/
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@Test(timeout = 300000)
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@Test
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public void testParagraphVectorsOverExistingWordVectorsModel() throws Exception {
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String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
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if(!isIntegrationTests() && "CUDA".equalsIgnoreCase(backend)) {
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skipUnlessIntegrationTests(); //Skip CUDA except for integration tests due to very slow test speed
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}
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// we build w2v from multiple sources, to cover everything
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File resource_sentences = Resources.asFile("/big/raw_sentences.txt");
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@ -997,14 +1022,18 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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log.info("SimilarityB: {}", simB);
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}
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@Test(timeout = 300000)
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@Test
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@Ignore //AB 2020/02/06 - https://github.com/eclipse/deeplearning4j/issues/8677
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public void testDirectInference() throws Exception {
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File resource_sentences = Resources.asFile("/big/raw_sentences.txt");
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boolean isIntegration = isIntegrationTests();
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File resource = Resources.asFile("/big/raw_sentences.txt");
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SentenceIterator sentencesIter = getIterator(isIntegration, resource);
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ClassPathResource resource_mixed = new ClassPathResource("paravec/");
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File local_resource_mixed = testDir.newFolder();
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resource_mixed.copyDirectory(local_resource_mixed);
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SentenceIterator iter = new AggregatingSentenceIterator.Builder()
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.addSentenceIterator(new BasicLineIterator(resource_sentences))
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.addSentenceIterator(sentencesIter)
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.addSentenceIterator(new FileSentenceIterator(local_resource_mixed)).build();
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TokenizerFactory t = new DefaultTokenizerFactory();
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@ -1154,24 +1183,7 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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public void testDoubleFit() throws Exception {
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boolean isIntegration = isIntegrationTests();
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File resource = Resources.asFile("/big/raw_sentences.txt");
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SentenceIterator iter;
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if(isIntegration){
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iter = new BasicLineIterator(resource);
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} else {
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List<String> lines = new ArrayList<>();
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try(InputStream is = new BufferedInputStream(new FileInputStream(resource))){
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LineIterator lineIter = IOUtils.lineIterator(is, StandardCharsets.UTF_8);
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try{
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for( int i=0; i<500 && lineIter.hasNext(); i++ ){
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lines.add(lineIter.next());
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}
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} finally {
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lineIter.close();
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}
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}
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iter = new CollectionSentenceIterator(lines);
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}
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SentenceIterator iter = getIterator(isIntegration, resource);
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TokenizerFactory t = new DefaultTokenizerFactory();
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@ -1197,6 +1209,30 @@ public class ParagraphVectorsTest extends BaseDL4JTest {
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assertEquals(num1, num2);
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}
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public static SentenceIterator getIterator(boolean isIntegration, File file) throws IOException {
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return getIterator(isIntegration, file, 500);
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}
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public static SentenceIterator getIterator(boolean isIntegration, File file, int linesForUnitTest) throws IOException {
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if(isIntegration){
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return new BasicLineIterator(file);
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} else {
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||||
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);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -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 = new BasicLineIterator(inputFile);
|
||||
val iter = ParagraphVectorsTest.getIterator(isIntegrationTests(), inputFile);
|
||||
// val iter = new BasicLineIterator(inputFile);
|
||||
val t = new DefaultTokenizerFactory();
|
||||
t.setTokenPreProcessor(new CommonPreprocessor());
|
||||
|
||||
|
|
|
|||
|
|
@ -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());
|
||||
|
|
|
|||
|
|
@ -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;
|
||||
|
|
|
|||
Loading…
Reference in New Issue