@@ -0,0 +1,1606 @@
+package hex.deeplearning;%0A%0Aimport org.junit.BeforeClass;%0Aimport org.junit.Ignore;%0Aimport org.junit.Test;%0Aimport water.DKV;%0Aimport water.H2O;%0Aimport water.Key;%0Aimport water.TestUtil;%0Aimport water.fvec.Frame;%0Aimport water.fvec.NFSFileVec;%0Aimport water.fvec.RebalanceDataSet;%0Aimport water.parser.ParseDataset;%0A%0A/**%0A * This test simulates environment%0A * produced by Spark - dataset divided into%0A * many small chunks,  some of theam are empty.%0A */%0Apublic class DeepLearningScoreTest extends TestUtil %7B%0A  @BeforeClass%0A  public static void setup() %7B stall_till_cloudsize(1); %7D%0A%0A  /** Load simple dataset, rebalance to a number of chunks %3E number of rows, and run deep learning */%0A  @Test @Ignore public void testPubDev928() %7B%0A    // Create rebalanced dataset%0A    Key rebalancedKey = Key.make(%22rebalanced%22);%0A    NFSFileVec nfs = NFSFileVec.make(find_test_file(%22smalldata/logreg/prostate.csv%22));%0A    Frame fr = ParseDataset.parse(Key.make(), nfs._key);%0A    RebalanceDataSet rb = new RebalanceDataSet(fr, rebalancedKey, (int)(fr.numRows()+1));%0A    H2O.submitTask(rb);%0A    rb.join();%0A    Frame rebalanced = DKV.get(rebalancedKey).get();%0A%0A    DeepLearningModel dlModel = null;%0A    try %7B%0A      // Launch Deep Learning%0A      DeepLearningModel.DeepLearningParameters dlParams = new DeepLearningModel.DeepLearningParameters();%0A      dlParams._train = rebalancedKey;%0A      dlParams._epochs = 5;%0A      dlParams._response_column = %22CAPSULE%22;%0A%0A      dlModel = new DeepLearning(dlParams).trainModel().get();%0A    %7D finally %7B%0A      fr.delete();%0A      rebalanced.delete();%0A      if (dlModel != null) dlModel.delete();%0A    %7D%0A  %7D%0A%7D%0A

Test (ignored) - deep learing is failing on empty chunks.