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From sboi...@apache.org
Subject [05/21] ignite git commit: IGNITE-9711: [ML] Remove IgniteThread wrapper from ml examples
Date Mon, 01 Oct 2018 05:55:15 GMT
http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java
index b7ca448..520b8cc 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/svm/multiclass/SVMMultiClassClassificationExample.java
@@ -32,7 +32,6 @@ import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
 import org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer;
 import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationModel;
 import org.apache.ignite.ml.svm.SVMLinearMultiClassClassificationTrainer;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Run SVM multi-class classification trainer ({@link SVMLinearMultiClassClassificationModel}) over distributed dataset
@@ -59,106 +58,100 @@ public class SVMMultiClassClassificationExample {
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
 
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                SVMMultiClassClassificationExample.class.getSimpleName(), () -> {
-                IgniteCache<Integer, Vector> dataCache = new TestCache(ignite).getVectors(data);
+            IgniteCache<Integer, Vector> dataCache = new TestCache(ignite).getVectors(data);
 
-                SVMLinearMultiClassClassificationTrainer trainer = new SVMLinearMultiClassClassificationTrainer();
+            SVMLinearMultiClassClassificationTrainer trainer = new SVMLinearMultiClassClassificationTrainer();
 
-                SVMLinearMultiClassClassificationModel mdl = trainer.fit(
-                    ignite,
-                    dataCache,
-                    (k, v) -> {
-                        double[] arr = v.asArray();
-                        return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
-                    },
-                    (k, v) -> v.get(0)
-                );
+            SVMLinearMultiClassClassificationModel mdl = trainer.fit(
+                ignite,
+                dataCache,
+                (k, v) -> {
+                    double[] arr = v.asArray();
+                    return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
+                },
+                (k, v) -> v.get(0)
+            );
 
-                System.out.println(">>> SVM Multi-class model");
-                System.out.println(mdl.toString());
+            System.out.println(">>> SVM Multi-class model");
+            System.out.println(mdl.toString());
 
-                MinMaxScalerTrainer<Integer, Vector> normalizationTrainer = new MinMaxScalerTrainer<>();
+            MinMaxScalerTrainer<Integer, Vector> normalizationTrainer = new MinMaxScalerTrainer<>();
 
-                IgniteBiFunction<Integer, Vector, Vector> preprocessor = normalizationTrainer.fit(
-                    ignite,
-                    dataCache,
-                    (k, v) -> {
-                        double[] arr = v.asArray();
-                        return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
-                    }
-                );
-
-                SVMLinearMultiClassClassificationModel mdlWithNormalization = trainer.fit(
-                    ignite,
-                    dataCache,
-                    preprocessor,
-                    (k, v) -> v.get(0)
-                );
+            IgniteBiFunction<Integer, Vector, Vector> preprocessor = normalizationTrainer.fit(
+                ignite,
+                dataCache,
+                (k, v) -> {
+                    double[] arr = v.asArray();
+                    return VectorUtils.of(Arrays.copyOfRange(arr, 1, arr.length));
+                }
+            );
 
-                System.out.println(">>> SVM Multi-class model with minmaxscaling");
-                System.out.println(mdlWithNormalization.toString());
+            SVMLinearMultiClassClassificationModel mdlWithNormalization = trainer.fit(
+                ignite,
+                dataCache,
+                preprocessor,
+                (k, v) -> v.get(0)
+            );
 
-                System.out.println(">>> ----------------------------------------------------------------");
-                System.out.println(">>> | Prediction\t| Prediction with Normalization\t| Ground Truth\t|");
-                System.out.println(">>> ----------------------------------------------------------------");
+            System.out.println(">>> SVM Multi-class model with minmaxscaling");
+            System.out.println(mdlWithNormalization.toString());
 
-                int amountOfErrors = 0;
-                int amountOfErrorsWithNormalization = 0;
-                int totalAmount = 0;
+            System.out.println(">>> ----------------------------------------------------------------");
+            System.out.println(">>> | Prediction\t| Prediction with Normalization\t| Ground Truth\t|");
+            System.out.println(">>> ----------------------------------------------------------------");
 
-                // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
-                int[][] confusionMtx = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
-                int[][] confusionMtxWithNormalization = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
+            int amountOfErrors = 0;
+            int amountOfErrorsWithNormalization = 0;
+            int totalAmount = 0;
 
-                try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
-                    for (Cache.Entry<Integer, Vector> observation : observations) {
-                        double[] val = observation.getValue().asArray();
-                        double[] inputs = Arrays.copyOfRange(val, 1, val.length);
-                        double groundTruth = val[0];
+            // Build confusion matrix. See https://en.wikipedia.org/wiki/Confusion_matrix
+            int[][] confusionMtx = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
+            int[][] confusionMtxWithNormalization = {{0, 0, 0}, {0, 0, 0}, {0, 0, 0}};
 
-                        double prediction = mdl.apply(new DenseVector(inputs));
-                        double predictionWithNormalization = mdlWithNormalization.apply(new DenseVector(inputs));
+            try (QueryCursor<Cache.Entry<Integer, Vector>> observations = dataCache.query(new ScanQuery<>())) {
+                for (Cache.Entry<Integer, Vector> observation : observations) {
+                    double[] val = observation.getValue().asArray();
+                    double[] inputs = Arrays.copyOfRange(val, 1, val.length);
+                    double groundTruth = val[0];
 
-                        totalAmount++;
+                    double prediction = mdl.apply(new DenseVector(inputs));
+                    double predictionWithNormalization = mdlWithNormalization.apply(new DenseVector(inputs));
 
-                        // Collect data for model
-                        if(groundTruth != prediction)
-                            amountOfErrors++;
+                    totalAmount++;
 
-                        int idx1 = (int)prediction == 1 ? 0 : ((int)prediction == 3 ? 1 : 2);
-                        int idx2 = (int)groundTruth == 1 ? 0 : ((int)groundTruth == 3 ? 1 : 2);
+                    // Collect data for model
+                    if(groundTruth != prediction)
+                        amountOfErrors++;
 
-                        confusionMtx[idx1][idx2]++;
+                    int idx1 = (int)prediction == 1 ? 0 : ((int)prediction == 3 ? 1 : 2);
+                    int idx2 = (int)groundTruth == 1 ? 0 : ((int)groundTruth == 3 ? 1 : 2);
 
-                        // Collect data for model with minmaxscaling
-                        if(groundTruth != predictionWithNormalization)
-                            amountOfErrorsWithNormalization++;
+                    confusionMtx[idx1][idx2]++;
 
-                        idx1 = (int)predictionWithNormalization == 1 ? 0 : ((int)predictionWithNormalization == 3 ? 1 : 2);
-                        idx2 = (int)groundTruth == 1 ? 0 : ((int)groundTruth == 3 ? 1 : 2);
+                    // Collect data for model with minmaxscaling
+                    if(groundTruth != predictionWithNormalization)
+                        amountOfErrorsWithNormalization++;
 
-                        confusionMtxWithNormalization[idx1][idx2]++;
+                    idx1 = (int)predictionWithNormalization == 1 ? 0 : ((int)predictionWithNormalization == 3 ? 1 : 2);
+                    idx2 = (int)groundTruth == 1 ? 0 : ((int)groundTruth == 3 ? 1 : 2);
 
-                        System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithNormalization, groundTruth);
-                    }
-                    System.out.println(">>> ----------------------------------------------------------------");
-                    System.out.println("\n>>> -----------------SVM model-------------");
-                    System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
-                    System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount));
-                    System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx));
+                    confusionMtxWithNormalization[idx1][idx2]++;
 
-                    System.out.println("\n>>> -----------------SVM model with Normalization-------------");
-                    System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithNormalization);
-                    System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithNormalization / (double)totalAmount));
-                    System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithNormalization));
-
-                    System.out.println(">>> Linear regression model over cache based dataset usage example completed.");
+                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t\t\t\t\t| %.4f\t\t|\n", prediction, predictionWithNormalization, groundTruth);
                 }
-            });
+                System.out.println(">>> ----------------------------------------------------------------");
+                System.out.println("\n>>> -----------------SVM model-------------");
+                System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
+                System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double)totalAmount));
+                System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtx));
+
+                System.out.println("\n>>> -----------------SVM model with Normalization-------------");
+                System.out.println("\n>>> Absolute amount of errors " + amountOfErrorsWithNormalization);
+                System.out.println("\n>>> Accuracy " + (1 - amountOfErrorsWithNormalization / (double)totalAmount));
+                System.out.println("\n>>> Confusion matrix is " + Arrays.deepToString(confusionMtxWithNormalization));
 
-            igniteThread.start();
-            igniteThread.join();
+                System.out.println(">>> Linear regression model over cache based dataset usage example completed.");
+            }
         }
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java
index 28a5fbc..652b293 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeClassificationTrainerExample.java
@@ -26,7 +26,6 @@ import org.apache.ignite.configuration.CacheConfiguration;
 import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
 import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
 import org.apache.ignite.ml.tree.DecisionTreeNode;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Example of using distributed {@link DecisionTreeClassificationTrainer}.
@@ -53,58 +52,49 @@ public class DecisionTreeClassificationTrainerExample {
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
 
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                DecisionTreeClassificationTrainerExample.class.getSimpleName(), () -> {
+            // Create cache with training data.
+            CacheConfiguration<Integer, LabeledPoint> trainingSetCfg = new CacheConfiguration<>();
+            trainingSetCfg.setName("TRAINING_SET");
+            trainingSetCfg.setAffinity(new RendezvousAffinityFunction(false, 10));
 
-                // Create cache with training data.
-                CacheConfiguration<Integer, LabeledPoint> trainingSetCfg = new CacheConfiguration<>();
-                trainingSetCfg.setName("TRAINING_SET");
-                trainingSetCfg.setAffinity(new RendezvousAffinityFunction(false, 10));
+            IgniteCache<Integer, LabeledPoint> trainingSet = ignite.createCache(trainingSetCfg);
 
-                IgniteCache<Integer, LabeledPoint> trainingSet = ignite.createCache(trainingSetCfg);
+            Random rnd = new Random(0);
 
-                Random rnd = new Random(0);
+            // Fill training data.
+            for (int i = 0; i < 1000; i++)
+                trainingSet.put(i, generatePoint(rnd));
 
-                // Fill training data.
-                for (int i = 0; i < 1000; i++)
-                    trainingSet.put(i, generatePoint(rnd));
+            // Create classification trainer.
+            DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(4, 0);
 
-                // Create classification trainer.
-                DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(4, 0);
+            // Train decision tree model.
+            DecisionTreeNode mdl = trainer.fit(
+                ignite,
+                trainingSet,
+                (k, v) -> VectorUtils.of(v.x, v.y),
+                (k, v) -> v.lb
+            );
 
-                // Train decision tree model.
-                DecisionTreeNode mdl = trainer.fit(
-                    ignite,
-                    trainingSet,
-                    (k, v) -> VectorUtils.of(v.x, v.y),
-                    (k, v) -> v.lb
-                );
+            System.out.println(">>> Decision tree classification model: " + mdl);
 
-                System.out.println(">>> Decision tree classification model: " + mdl);
+            // Calculate score.
+            int correctPredictions = 0;
+            for (int i = 0; i < 1000; i++) {
+                LabeledPoint pnt = generatePoint(rnd);
 
-                // Calculate score.
-                int correctPredictions = 0;
-                for (int i = 0; i < 1000; i++) {
-                    LabeledPoint pnt = generatePoint(rnd);
+                double prediction = mdl.apply(VectorUtils.of(pnt.x, pnt.y));
+                double lbl = pnt.lb;
 
-                    double prediction = mdl.apply(VectorUtils.of(pnt.x, pnt.y));
-                    double lbl = pnt.lb;
+                if (i %50 == 1)
+                    System.out.printf(">>> test #: %d\t\t predicted: %.4f\t\tlabel: %.4f\n", i, prediction, lbl);
 
-                    if (i %50 == 1)
-                        System.out.printf(">>> test #: %d\t\t predicted: %.4f\t\tlabel: %.4f\n", i, prediction, lbl);
+                if (prediction == lbl)
+                    correctPredictions++;
+            }
 
-                    if (prediction == lbl)
-                        correctPredictions++;
-                }
-
-                System.out.println(">>> Accuracy: " + correctPredictions / 10.0 + "%");
-
-                System.out.println(">>> Decision tree classification trainer example completed.");
-            });
-
-            igniteThread.start();
-
-            igniteThread.join();
+            System.out.println(">>> Accuracy: " + correctPredictions / 10.0 + "%");
+            System.out.println(">>> Decision tree classification trainer example completed.");
         }
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java
index 301df10..2a89c7e 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/DecisionTreeRegressionTrainerExample.java
@@ -25,7 +25,6 @@ import org.apache.ignite.configuration.CacheConfiguration;
 import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
 import org.apache.ignite.ml.tree.DecisionTreeNode;
 import org.apache.ignite.ml.tree.DecisionTreeRegressionTrainer;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Example of using distributed {@link DecisionTreeRegressionTrainer}.
@@ -53,51 +52,43 @@ public class DecisionTreeRegressionTrainerExample {
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
 
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                DecisionTreeRegressionTrainerExample.class.getSimpleName(), () -> {
+            // Create cache with training data.
+            CacheConfiguration<Integer, Point> trainingSetCfg = new CacheConfiguration<>();
+            trainingSetCfg.setName("TRAINING_SET");
+            trainingSetCfg.setAffinity(new RendezvousAffinityFunction(false, 10));
 
-                // Create cache with training data.
-                CacheConfiguration<Integer, Point> trainingSetCfg = new CacheConfiguration<>();
-                trainingSetCfg.setName("TRAINING_SET");
-                trainingSetCfg.setAffinity(new RendezvousAffinityFunction(false, 10));
+            IgniteCache<Integer, Point> trainingSet = ignite.createCache(trainingSetCfg);
 
-                IgniteCache<Integer, Point> trainingSet = ignite.createCache(trainingSetCfg);
+            // Fill training data.
+            generatePoints(trainingSet);
 
-                // Fill training data.
-                generatePoints(trainingSet);
+            // Create regression trainer.
+            DecisionTreeRegressionTrainer trainer = new DecisionTreeRegressionTrainer(10, 0);
 
-                // Create regression trainer.
-                DecisionTreeRegressionTrainer trainer = new DecisionTreeRegressionTrainer(10, 0);
+            // Train decision tree model.
+            DecisionTreeNode mdl = trainer.fit(
+                ignite,
+                trainingSet,
+                (k, v) -> VectorUtils.of(v.x),
+                (k, v) -> v.y
+            );
 
-                // Train decision tree model.
-                DecisionTreeNode mdl = trainer.fit(
-                    ignite,
-                    trainingSet,
-                    (k, v) -> VectorUtils.of(v.x),
-                    (k, v) -> v.y
-                );
+            System.out.println(">>> Decision tree regression model: " + mdl);
 
-                System.out.println(">>> Decision tree regression model: " + mdl);
+            System.out.println(">>> ---------------------------------");
+            System.out.println(">>> | Prediction\t| Ground Truth\t|");
+            System.out.println(">>> ---------------------------------");
 
-                System.out.println(">>> ---------------------------------");
-                System.out.println(">>> | Prediction\t| Ground Truth\t|");
-                System.out.println(">>> ---------------------------------");
+            // Calculate score.
+            for (int x = 0; x < 10; x++) {
+                double predicted = mdl.apply(VectorUtils.of(x));
 
-                // Calculate score.
-                for (int x = 0; x < 10; x++) {
-                    double predicted = mdl.apply(VectorUtils.of(x));
+                System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", predicted, Math.sin(x));
+            }
 
-                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", predicted, Math.sin(x));
-                }
+            System.out.println(">>> ---------------------------------");
 
-                System.out.println(">>> ---------------------------------");
-
-                System.out.println(">>> Decision tree regression trainer example completed.");
-            });
-
-            igniteThread.start();
-
-            igniteThread.join();
+            System.out.println(">>> Decision tree regression trainer example completed.");
         }
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java
index e092e5c..5beb954 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesClassificationTrainerExample.java
@@ -27,7 +27,6 @@ import org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueCo
 import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
 import org.apache.ignite.ml.trainers.DatasetTrainer;
 import org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer;
-import org.apache.ignite.thread.IgniteThread;
 import org.jetbrains.annotations.NotNull;
 
 /**
@@ -50,45 +49,38 @@ public class GDBOnTreesClassificationTrainerExample {
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
 
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                GDBBinaryClassifierOnTreesTrainer.class.getSimpleName(), () -> {
+            // Create cache with training data.
+            CacheConfiguration<Integer, double[]> trainingSetCfg = createCacheConfiguration();
+            IgniteCache<Integer, double[]> trainingSet = fillTrainingData(ignite, trainingSetCfg);
 
-                // Create cache with training data.
-                CacheConfiguration<Integer, double[]> trainingSetCfg = createCacheConfiguration();
-                IgniteCache<Integer, double[]> trainingSet = fillTrainingData(ignite, trainingSetCfg);
+            // Create regression trainer.
+            DatasetTrainer<ModelsComposition, Double> trainer = new GDBBinaryClassifierOnTreesTrainer(1.0, 300, 2, 0.)
+                .withCheckConvergenceStgyFactory(new MeanAbsValueConvergenceCheckerFactory(0.1));
 
-                // Create regression trainer.
-                DatasetTrainer<ModelsComposition, Double> trainer = new GDBBinaryClassifierOnTreesTrainer(1.0, 300, 2, 0.)
-                    .withCheckConvergenceStgyFactory(new MeanAbsValueConvergenceCheckerFactory(0.1));
+            // Train decision tree model.
+            ModelsComposition mdl = trainer.fit(
+                ignite,
+                trainingSet,
+                (k, v) -> VectorUtils.of(v[0]),
+                (k, v) -> v[1]
+            );
 
-                // Train decision tree model.
-                ModelsComposition mdl = trainer.fit(
-                    ignite,
-                    trainingSet,
-                    (k, v) -> VectorUtils.of(v[0]),
-                    (k, v) -> v[1]
-                );
+            System.out.println(">>> ---------------------------------");
+            System.out.println(">>> | Prediction\t| Valid answer\t|");
+            System.out.println(">>> ---------------------------------");
 
-                System.out.println(">>> ---------------------------------");
-                System.out.println(">>> | Prediction\t| Valid answer\t|");
-                System.out.println(">>> ---------------------------------");
+            // Calculate score.
+            for (int x = -5; x < 5; x++) {
+                double predicted = mdl.apply(VectorUtils.of(x));
 
-                // Calculate score.
-                for (int x = -5; x < 5; x++) {
-                    double predicted = mdl.apply(VectorUtils.of(x));
+                System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", predicted, Math.sin(x) < 0 ? 0.0 : 1.0);
+            }
 
-                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", predicted, Math.sin(x) < 0 ? 0.0 : 1.0);
-                }
+            System.out.println(">>> ---------------------------------");
+            System.out.println(">>> Count of trees = " + mdl.getModels().size());
+            System.out.println(">>> ---------------------------------");
 
-                System.out.println(">>> ---------------------------------");
-                System.out.println(">>> Count of trees = " + mdl.getModels().size());
-                System.out.println(">>> ---------------------------------");
-
-                System.out.println(">>> GDB classification trainer example completed.");
-            });
-
-            igniteThread.start();
-            igniteThread.join();
+            System.out.println(">>> GDB classification trainer example completed.");
         }
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesRegressionTrainerExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesRegressionTrainerExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesRegressionTrainerExample.java
index 3662973..482a47c 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesRegressionTrainerExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/boosting/GDBOnTreesRegressionTrainerExample.java
@@ -29,7 +29,6 @@ import org.apache.ignite.ml.math.primitives.vector.Vector;
 import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
 import org.apache.ignite.ml.trainers.DatasetTrainer;
 import org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer;
-import org.apache.ignite.thread.IgniteThread;
 import org.jetbrains.annotations.NotNull;
 
 /**
@@ -52,43 +51,35 @@ public class GDBOnTreesRegressionTrainerExample {
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
 
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                GDBOnTreesRegressionTrainerExample.class.getSimpleName(), () -> {
+            // Create cache with training data.
+            CacheConfiguration<Integer, double[]> trainingSetCfg = createCacheConfiguration();
+            IgniteCache<Integer, double[]> trainingSet = fillTrainingData(ignite, trainingSetCfg);
 
-                // Create cache with training data.
-                CacheConfiguration<Integer, double[]> trainingSetCfg = createCacheConfiguration();
-                IgniteCache<Integer, double[]> trainingSet = fillTrainingData(ignite, trainingSetCfg);
+            // Create regression trainer.
+            DatasetTrainer<ModelsComposition, Double> trainer = new GDBRegressionOnTreesTrainer(1.0, 2000, 1, 0.)
+                .withCheckConvergenceStgyFactory(new MeanAbsValueConvergenceCheckerFactory(0.001));
 
-                // Create regression trainer.
-                DatasetTrainer<ModelsComposition, Double> trainer = new GDBRegressionOnTreesTrainer(1.0, 2000, 1, 0.)
-                    .withCheckConvergenceStgyFactory(new MeanAbsValueConvergenceCheckerFactory(0.001));
+            // Train decision tree model.
+            Model<Vector, Double> mdl = trainer.fit(
+                ignite,
+                trainingSet,
+                (k, v) -> VectorUtils.of(v[0]),
+                (k, v) -> v[1]
+            );
 
-                // Train decision tree model.
-                Model<Vector, Double> mdl = trainer.fit(
-                    ignite,
-                    trainingSet,
-                    (k, v) -> VectorUtils.of(v[0]),
-                    (k, v) -> v[1]
-                );
+            System.out.println(">>> ---------------------------------");
+            System.out.println(">>> | Prediction\t| Valid answer \t|");
+            System.out.println(">>> ---------------------------------");
 
-                System.out.println(">>> ---------------------------------");
-                System.out.println(">>> | Prediction\t| Valid answer \t|");
-                System.out.println(">>> ---------------------------------");
+            // Calculate score.
+            for (int x = -5; x < 5; x++) {
+                double predicted = mdl.apply(VectorUtils.of(x));
 
-                // Calculate score.
-                for (int x = -5; x < 5; x++) {
-                    double predicted = mdl.apply(VectorUtils.of(x));
+                System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", predicted, Math.pow(x, 2));
+            }
 
-                    System.out.printf(">>> | %.4f\t\t| %.4f\t\t|\n", predicted, Math.pow(x, 2));
-                }
-
-                System.out.println(">>> ---------------------------------");
-
-                System.out.println(">>> GDB regression trainer example completed.");
-            });
-
-            igniteThread.start();
-            igniteThread.join();
+            System.out.println(">>> ---------------------------------");
+            System.out.println(">>> GDB regression trainer example completed.");
         }
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
index 4693744..ea235ee 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestClassificationExample.java
@@ -33,7 +33,6 @@ import org.apache.ignite.ml.dataset.feature.FeatureMeta;
 import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
 import org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer;
 import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Example represents a solution for the task of wine classification based on a
@@ -62,57 +61,50 @@ public class RandomForestClassificationExample {
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
 
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                RandomForestClassificationExample.class.getSimpleName(), () -> {
-                IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+            IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
 
-                AtomicInteger idx = new AtomicInteger(0);
-                RandomForestClassifierTrainer classifier = new RandomForestClassifierTrainer(
-                    IntStream.range(0, data[0].length - 1).mapToObj(
-                        x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
-                ).withAmountOfTrees(101)
-                    .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
-                    .withMaxDepth(4)
-                    .withMinImpurityDelta(0.)
-                    .withSubSampleSize(0.3)
-                    .withSeed(0);
+            AtomicInteger idx = new AtomicInteger(0);
+            RandomForestClassifierTrainer classifier = new RandomForestClassifierTrainer(
+                IntStream.range(0, data[0].length - 1).mapToObj(
+                    x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
+            ).withAmountOfTrees(101)
+                .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
+                .withMaxDepth(4)
+                .withMinImpurityDelta(0.)
+                .withSubSampleSize(0.3)
+                .withSeed(0);
 
-                System.out.println(">>> Configured trainer: " + classifier.getClass().getSimpleName());
+            System.out.println(">>> Configured trainer: " + classifier.getClass().getSimpleName());
 
-                ModelsComposition randomForest = classifier.fit(ignite, dataCache,
-                    (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
-                    (k, v) -> v[0]
-                );
+            ModelsComposition randomForest = classifier.fit(ignite, dataCache,
+                (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 1, v.length)),
+                (k, v) -> v[0]
+            );
 
-                System.out.println(">>> Trained model: " + randomForest.toString(true));
+            System.out.println(">>> Trained model: " + randomForest.toString(true));
 
-                int amountOfErrors = 0;
-                int totalAmount = 0;
+            int amountOfErrors = 0;
+            int totalAmount = 0;
 
-                try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
-                    for (Cache.Entry<Integer, double[]> observation : observations) {
-                        double[] val = observation.getValue();
-                        double[] inputs = Arrays.copyOfRange(val, 1, val.length);
-                        double groundTruth = val[0];
+            try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
+                for (Cache.Entry<Integer, double[]> observation : observations) {
+                    double[] val = observation.getValue();
+                    double[] inputs = Arrays.copyOfRange(val, 1, val.length);
+                    double groundTruth = val[0];
 
-                        double prediction = randomForest.apply(VectorUtils.of(inputs));
+                    double prediction = randomForest.apply(VectorUtils.of(inputs));
 
-                        totalAmount++;
-                        if (groundTruth != prediction)
-                            amountOfErrors++;
-
-                    }
-
-                    System.out.println("\n>>> Evaluated model on " + totalAmount + " data points.");
-
-                    System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
-                    System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double) totalAmount));
-                    System.out.println(">>> Random Forest multi-class classification algorithm over cached dataset usage example completed.");
+                    totalAmount++;
+                    if (groundTruth != prediction)
+                        amountOfErrors++;
                 }
-            });
 
-            igniteThread.start();
-            igniteThread.join();
+                System.out.println("\n>>> Evaluated model on " + totalAmount + " data points.");
+
+                System.out.println("\n>>> Absolute amount of errors " + amountOfErrors);
+                System.out.println("\n>>> Accuracy " + (1 - amountOfErrors / (double) totalAmount));
+                System.out.println(">>> Random Forest multi-class classification algorithm over cached dataset usage example completed.");
+            }
         }
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
index ee0c1c2..9b4aece 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tree/randomforest/RandomForestRegressionExample.java
@@ -37,7 +37,6 @@ import org.apache.ignite.ml.environment.parallelism.ParallelismStrategy;
 import org.apache.ignite.ml.math.primitives.vector.VectorUtils;
 import org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer;
 import org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Example represents a solution for the task of price predictions for houses in Boston based on a
@@ -66,68 +65,62 @@ public class RandomForestRegressionExample {
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
             System.out.println(">>> Ignite grid started.");
 
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                RandomForestRegressionExample.class.getSimpleName(), () -> {
-                IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
+            IgniteCache<Integer, double[]> dataCache = new TestCache(ignite).fillCacheWith(data);
 
-                AtomicInteger idx = new AtomicInteger(0);
-                RandomForestRegressionTrainer trainer = new RandomForestRegressionTrainer(
-                    IntStream.range(0, data[0].length - 1).mapToObj(
-                        x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
-                ).withAmountOfTrees(101)
-                    .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
-                    .withMaxDepth(4)
-                    .withMinImpurityDelta(0.)
-                    .withSubSampleSize(0.3)
-                    .withSeed(0);
+            AtomicInteger idx = new AtomicInteger(0);
+            RandomForestRegressionTrainer trainer = new RandomForestRegressionTrainer(
+                IntStream.range(0, data[0].length - 1).mapToObj(
+                    x -> new FeatureMeta("", idx.getAndIncrement(), false)).collect(Collectors.toList())
+            ).withAmountOfTrees(101)
+                .withFeaturesCountSelectionStrgy(FeaturesCountSelectionStrategies.ONE_THIRD)
+                .withMaxDepth(4)
+                .withMinImpurityDelta(0.)
+                .withSubSampleSize(0.3)
+                .withSeed(0);
 
-                trainer.setEnvironment(LearningEnvironment.builder()
-                    .withParallelismStrategy(ParallelismStrategy.Type.ON_DEFAULT_POOL)
-                    .withLoggingFactory(ConsoleLogger.factory(MLLogger.VerboseLevel.LOW))
-                    .build()
-                );
+            trainer.setEnvironment(LearningEnvironment.builder()
+                .withParallelismStrategy(ParallelismStrategy.Type.ON_DEFAULT_POOL)
+                .withLoggingFactory(ConsoleLogger.factory(MLLogger.VerboseLevel.LOW))
+                .build()
+            );
 
-                System.out.println(">>> Configured trainer: " + trainer.getClass().getSimpleName());
+            System.out.println(">>> Configured trainer: " + trainer.getClass().getSimpleName());
 
-                ModelsComposition randomForest = trainer.fit(ignite, dataCache,
-                    (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 0, v.length - 1)),
-                    (k, v) -> v[v.length - 1]
-                );
+            ModelsComposition randomForest = trainer.fit(ignite, dataCache,
+                (k, v) -> VectorUtils.of(Arrays.copyOfRange(v, 0, v.length - 1)),
+                (k, v) -> v[v.length - 1]
+            );
 
-                System.out.println(">>> Trained model: " + randomForest.toString(true));
+            System.out.println(">>> Trained model: " + randomForest.toString(true));
 
-                double mse = 0.0;
-                double mae = 0.0;
-                int totalAmount = 0;
+            double mse = 0.0;
+            double mae = 0.0;
+            int totalAmount = 0;
 
-                try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
-                    for (Cache.Entry<Integer, double[]> observation : observations) {
-                        double[] val = observation.getValue();
-                        double[] inputs = Arrays.copyOfRange(val, 0, val.length - 1);
-                        double groundTruth = val[val.length - 1];
+            try (QueryCursor<Cache.Entry<Integer, double[]>> observations = dataCache.query(new ScanQuery<>())) {
+                for (Cache.Entry<Integer, double[]> observation : observations) {
+                    double[] val = observation.getValue();
+                    double[] inputs = Arrays.copyOfRange(val, 0, val.length - 1);
+                    double groundTruth = val[val.length - 1];
 
-                        double prediction = randomForest.apply(VectorUtils.of(inputs));
+                    double prediction = randomForest.apply(VectorUtils.of(inputs));
 
-                        mse += Math.pow(prediction - groundTruth, 2.0);
-                        mae += Math.abs(prediction - groundTruth);
+                    mse += Math.pow(prediction - groundTruth, 2.0);
+                    mae += Math.abs(prediction - groundTruth);
 
-                        totalAmount++;
-                    }
-
-                    System.out.println("\n>>> Evaluated model on " + totalAmount + " data points.");
+                    totalAmount++;
+                }
 
-                    mse = mse / totalAmount;
-                    System.out.println("\n>>> Mean squared error (MSE) " + mse);
+                System.out.println("\n>>> Evaluated model on " + totalAmount + " data points.");
 
-                    mae = mae / totalAmount;
-                    System.out.println("\n>>> Mean absolute error (MAE) " + mae);
+                mse = mse / totalAmount;
+                System.out.println("\n>>> Mean squared error (MSE) " + mse);
 
-                    System.out.println(">>> Random Forest regression algorithm over cached dataset usage example completed.");
-                }
-            });
+                mae = mae / totalAmount;
+                System.out.println("\n>>> Mean absolute error (MAE) " + mae);
 
-            igniteThread.start();
-            igniteThread.join();
+                System.out.println(">>> Random Forest regression algorithm over cached dataset usage example completed.");
+            }
         }
     }
 

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_1_Read_and_Learn.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_1_Read_and_Learn.java b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_1_Read_and_Learn.java
index 78ec9f5..264dbf4 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_1_Read_and_Learn.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_1_Read_and_Learn.java
@@ -28,7 +28,6 @@ import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator;
 import org.apache.ignite.ml.selection.scoring.metric.Accuracy;
 import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
 import org.apache.ignite.ml.tree.DecisionTreeNode;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Usage of {@link DecisionTreeClassificationTrainer} to predict death in the disaster.
@@ -48,47 +47,41 @@ public class Step_1_Read_and_Learn {
         System.out.println(">>> Tutorial step 1 (read and learn) example started.");
 
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                Step_1_Read_and_Learn.class.getSimpleName(), () -> {
-                try {
-                    IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
+            try {
+                IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
 
-                    IgniteBiFunction<Integer, Object[], Vector> featureExtractor
-                        = (k, v) -> VectorUtils.of((double) v[0], (double) v[5], (double) v[6]);
+                IgniteBiFunction<Integer, Object[], Vector> featureExtractor
+                    = (k, v) -> VectorUtils.of((double) v[0], (double) v[5], (double) v[6]);
 
-                    IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
+                IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
 
-                    DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
+                DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
 
-                    DecisionTreeNode mdl = trainer.fit(
-                        ignite,
-                        dataCache,
-                        featureExtractor, // "pclass", "sibsp", "parch"
-                        lbExtractor
-                    );
+                DecisionTreeNode mdl = trainer.fit(
+                    ignite,
+                    dataCache,
+                    featureExtractor, // "pclass", "sibsp", "parch"
+                    lbExtractor
+                );
 
-                    System.out.println("\n>>> Trained model: " + mdl);
+                System.out.println("\n>>> Trained model: " + mdl);
 
-                    double accuracy = Evaluator.evaluate(
-                        dataCache,
-                        mdl,
-                        featureExtractor,
-                        lbExtractor,
-                        new Accuracy<>()
-                    );
+                double accuracy = Evaluator.evaluate(
+                    dataCache,
+                    mdl,
+                    featureExtractor,
+                    lbExtractor,
+                    new Accuracy<>()
+                );
 
-                    System.out.println("\n>>> Accuracy " + accuracy);
-                    System.out.println("\n>>> Test Error " + (1 - accuracy));
+                System.out.println("\n>>> Accuracy " + accuracy);
+                System.out.println("\n>>> Test Error " + (1 - accuracy));
 
-                    System.out.println(">>> Tutorial step 1 (read and learn) example completed.");
-                }
-                catch (FileNotFoundException e) {
-                    e.printStackTrace();
-                }
-            });
-
-            igniteThread.start();
-            igniteThread.join();
+                System.out.println(">>> Tutorial step 1 (read and learn) example completed.");
+            }
+            catch (FileNotFoundException e) {
+                e.printStackTrace();
+            }
         }
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_2_Imputing.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_2_Imputing.java b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_2_Imputing.java
index f86e1b6..df73235 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_2_Imputing.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_2_Imputing.java
@@ -29,7 +29,6 @@ import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator;
 import org.apache.ignite.ml.selection.scoring.metric.Accuracy;
 import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
 import org.apache.ignite.ml.tree.DecisionTreeNode;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Usage of {@link ImputerTrainer} to fill missed data ({@code Double.NaN}) values in the chosen columns.
@@ -50,54 +49,48 @@ public class Step_2_Imputing {
         System.out.println(">>> Tutorial step 2 (imputing) example started.");
 
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                Step_2_Imputing.class.getSimpleName(), () -> {
-                try {
-                    IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
+            try {
+                IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
 
-                    IgniteBiFunction<Integer, Object[], Vector> featureExtractor
-                        = (k, v) -> VectorUtils.of((double) v[0], (double) v[5], (double) v[6]);
+                IgniteBiFunction<Integer, Object[], Vector> featureExtractor
+                    = (k, v) -> VectorUtils.of((double) v[0], (double) v[5], (double) v[6]);
 
-                    IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
+                IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
 
-                    IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
-                        .fit(ignite,
-                            dataCache,
-                            featureExtractor // "pclass", "sibsp", "parch"
-                        );
-
-                    DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
-
-                    // Train decision tree model.
-                    DecisionTreeNode mdl = trainer.fit(
-                        ignite,
+                IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
+                    .fit(ignite,
                         dataCache,
-                        imputingPreprocessor,
-                        lbExtractor
+                        featureExtractor // "pclass", "sibsp", "parch"
                     );
 
-                    System.out.println("\n>>> Trained model: " + mdl);
+                DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
 
-                    double accuracy = Evaluator.evaluate(
-                        dataCache,
-                        mdl,
-                        imputingPreprocessor,
-                        lbExtractor,
-                        new Accuracy<>()
-                    );
+                // Train decision tree model.
+                DecisionTreeNode mdl = trainer.fit(
+                    ignite,
+                    dataCache,
+                    imputingPreprocessor,
+                    lbExtractor
+                );
+
+                System.out.println("\n>>> Trained model: " + mdl);
 
-                    System.out.println("\n>>> Accuracy " + accuracy);
-                    System.out.println("\n>>> Test Error " + (1 - accuracy));
+                double accuracy = Evaluator.evaluate(
+                    dataCache,
+                    mdl,
+                    imputingPreprocessor,
+                    lbExtractor,
+                    new Accuracy<>()
+                );
 
-                    System.out.println(">>> Tutorial step 2 (imputing) example completed.");
-                }
-                catch (FileNotFoundException e) {
-                    e.printStackTrace();
-                }
-            });
+                System.out.println("\n>>> Accuracy " + accuracy);
+                System.out.println("\n>>> Test Error " + (1 - accuracy));
 
-            igniteThread.start();
-            igniteThread.join();
+                System.out.println(">>> Tutorial step 2 (imputing) example completed.");
+            }
+            catch (FileNotFoundException e) {
+                e.printStackTrace();
+            }
         }
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial.java b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial.java
index 03ff527..463a6ba 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial.java
@@ -30,7 +30,6 @@ import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator;
 import org.apache.ignite.ml.selection.scoring.metric.Accuracy;
 import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
 import org.apache.ignite.ml.tree.DecisionTreeNode;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Let's add two categorial features "sex", "embarked" to predict more precisely than in {@link Step_1_Read_and_Learn}.
@@ -53,65 +52,58 @@ public class Step_3_Categorial {
         System.out.println(">>> Tutorial step 3 (categorial) example started.");
 
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                Step_3_Categorial.class.getSimpleName(), () -> {
-                try {
-                    IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
+            try {
+                IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
 
-                    // Defines first preprocessor that extracts features from an upstream data.
-                    IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
-                        = (k, v) -> new Object[]{v[0], v[3], v[5], v[6], v[10]}; // "pclass", "sibsp", "parch", "sex", "embarked"
+                // Defines first preprocessor that extracts features from an upstream data.
+                IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
+                    = (k, v) -> new Object[]{v[0], v[3], v[5], v[6], v[10]}; // "pclass", "sibsp", "parch", "sex", "embarked"
 
-                    IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
+                IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
 
-                    IgniteBiFunction<Integer, Object[], Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
-                        .withEncoderType(EncoderType.STRING_ENCODER)
-                        .withEncodedFeature(1)
-                        .withEncodedFeature(4)
-                        .fit(ignite,
-                            dataCache,
-                            featureExtractor
-                    );
-
-                    IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
-                        .fit(ignite,
-                            dataCache,
-                            strEncoderPreprocessor
-                        );
-
-                    DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
-
-                    // Train decision tree model.
-                    DecisionTreeNode mdl = trainer.fit(
-                        ignite,
+                IgniteBiFunction<Integer, Object[], Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
+                    .withEncoderType(EncoderType.STRING_ENCODER)
+                    .withEncodedFeature(1)
+                    .withEncodedFeature(4)
+                    .fit(ignite,
                         dataCache,
-                        imputingPreprocessor,
-                        lbExtractor
-                    );
+                        featureExtractor
+                );
 
-                    System.out.println("\n>>> Trained model: " + mdl);
-
-                    double accuracy = Evaluator.evaluate(
+                IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
+                    .fit(ignite,
                         dataCache,
-                        mdl,
-                        imputingPreprocessor,
-                        lbExtractor,
-                        new Accuracy<>()
+                        strEncoderPreprocessor
                     );
 
-                    System.out.println("\n>>> Accuracy " + accuracy);
-                    System.out.println("\n>>> Test Error " + (1 - accuracy));
-
-                    System.out.println(">>> Tutorial step 3 (categorial) example completed.");
-                }
-                catch (FileNotFoundException e) {
-                    e.printStackTrace();
-                }
-            });
-
-            igniteThread.start();
-
-            igniteThread.join();
+                DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
+
+                // Train decision tree model.
+                DecisionTreeNode mdl = trainer.fit(
+                    ignite,
+                    dataCache,
+                    imputingPreprocessor,
+                    lbExtractor
+                );
+
+                System.out.println("\n>>> Trained model: " + mdl);
+
+                double accuracy = Evaluator.evaluate(
+                    dataCache,
+                    mdl,
+                    imputingPreprocessor,
+                    lbExtractor,
+                    new Accuracy<>()
+                );
+
+                System.out.println("\n>>> Accuracy " + accuracy);
+                System.out.println("\n>>> Test Error " + (1 - accuracy));
+
+                System.out.println(">>> Tutorial step 3 (categorial) example completed.");
+            }
+            catch (FileNotFoundException e) {
+                e.printStackTrace();
+            }
         }
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial_with_One_Hot_Encoder.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial_with_One_Hot_Encoder.java b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial_with_One_Hot_Encoder.java
index a4535ba..93e7e79 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial_with_One_Hot_Encoder.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_3_Categorial_with_One_Hot_Encoder.java
@@ -30,7 +30,6 @@ import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator;
 import org.apache.ignite.ml.selection.scoring.metric.Accuracy;
 import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
 import org.apache.ignite.ml.tree.DecisionTreeNode;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Let's add two categorial features "sex", "embarked" to predict more precisely than in {@link Step_1_Read_and_Learn}..
@@ -54,68 +53,61 @@ public class Step_3_Categorial_with_One_Hot_Encoder {
         System.out.println(">>> Tutorial step 3 (categorial with One-hot encoder) example started.");
 
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                Step_3_Categorial_with_One_Hot_Encoder.class.getSimpleName(), () -> {
-                try {
-                    IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
-
-                    // Defines first preprocessor that extracts features from an upstream data.
-                    IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
-                        = (k, v) -> new Object[]{v[0], v[3], v[5], v[6], v[10]
-                    }; // "pclass", "sibsp", "parch", "sex", "embarked"
-
-                    IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
-
-                    IgniteBiFunction<Integer, Object[], Vector> oneHotEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
-                        .withEncoderType(EncoderType.ONE_HOT_ENCODER)
-                        .withEncodedFeature(0)
-                        .withEncodedFeature(1)
-                        .withEncodedFeature(4)
-                        .fit(ignite,
-                            dataCache,
-                            featureExtractor
-                    );
-
-                    IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
-                        .fit(ignite,
-                            dataCache,
-                            oneHotEncoderPreprocessor
-                        );
-
-                    DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
+            try {
+                IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
+
+                // Defines first preprocessor that extracts features from an upstream data.
+                IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
+                    = (k, v) -> new Object[]{v[0], v[3], v[5], v[6], v[10]
+                }; // "pclass", "sibsp", "parch", "sex", "embarked"
+
+                IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
+
+                IgniteBiFunction<Integer, Object[], Vector> oneHotEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
+                    .withEncoderType(EncoderType.ONE_HOT_ENCODER)
+                    .withEncodedFeature(0)
+                    .withEncodedFeature(1)
+                    .withEncodedFeature(4)
+                    .fit(ignite,
+                        dataCache,
+                        featureExtractor
+                );
 
-                    // Train decision tree model.
-                    DecisionTreeNode mdl = trainer.fit(
-                        ignite,
+                IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
+                    .fit(ignite,
                         dataCache,
-                        imputingPreprocessor,
-                        lbExtractor
+                        oneHotEncoderPreprocessor
                     );
 
-                    System.out.println("\n>>> Trained model: " + mdl);
+                DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
 
-                    double accuracy = Evaluator.evaluate(
-                        dataCache,
-                        mdl,
-                        imputingPreprocessor,
-                        lbExtractor,
-                        new Accuracy<>()
-                    );
+                // Train decision tree model.
+                DecisionTreeNode mdl = trainer.fit(
+                    ignite,
+                    dataCache,
+                    imputingPreprocessor,
+                    lbExtractor
+                );
 
-                    System.out.println("\n>>> Accuracy " + accuracy);
-                    System.out.println("\n>>> Test Error " + (1 - accuracy));
+                System.out.println("\n>>> Trained model: " + mdl);
 
-                    System.out.println(">>> Tutorial step 3 (categorial with One-hot encoder) example started.");
+                double accuracy = Evaluator.evaluate(
+                    dataCache,
+                    mdl,
+                    imputingPreprocessor,
+                    lbExtractor,
+                    new Accuracy<>()
+                );
 
-                }
-                catch (FileNotFoundException e) {
-                    e.printStackTrace();
-                }
-            });
+                System.out.println("\n>>> Accuracy " + accuracy);
+                System.out.println("\n>>> Test Error " + (1 - accuracy));
 
-            igniteThread.start();
+                System.out.println(">>> Tutorial step 3 (categorial with One-hot encoder) example started.");
 
-            igniteThread.join();
+            }
+            catch (FileNotFoundException e) {
+                e.printStackTrace();
+            }
         }
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_4_Add_age_fare.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_4_Add_age_fare.java b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_4_Add_age_fare.java
index 789d7e8..bbeedb6 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_4_Add_age_fare.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_4_Add_age_fare.java
@@ -30,7 +30,6 @@ import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator;
 import org.apache.ignite.ml.selection.scoring.metric.Accuracy;
 import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
 import org.apache.ignite.ml.tree.DecisionTreeNode;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * Add yet two numerical features "age", "fare" to improve our model over {@link Step_3_Categorial}.
@@ -51,66 +50,59 @@ public class Step_4_Add_age_fare {
         System.out.println(">>> Tutorial step 4 (add age and fare) example started.");
 
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                Step_4_Add_age_fare.class.getSimpleName(), () -> {
-                try {
-                    IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
+            try {
+                IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
 
-                    // Defines first preprocessor that extracts features from an upstream data.
-                    // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
-                    IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
-                        = (k, v) -> new Object[]{v[0], v[3], v[4], v[5], v[6], v[8], v[10]};
+                // Defines first preprocessor that extracts features from an upstream data.
+                // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
+                IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
+                    = (k, v) -> new Object[]{v[0], v[3], v[4], v[5], v[6], v[8], v[10]};
 
-                    IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
+                IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
 
-                    IgniteBiFunction<Integer, Object[], Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
-                        .withEncoderType(EncoderType.STRING_ENCODER)
-                        .withEncodedFeature(1)
-                        .withEncodedFeature(6) // <--- Changed index here.
-                        .fit(ignite,
-                            dataCache,
-                            featureExtractor
-                    );
-
-                    IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
-                        .fit(ignite,
-                            dataCache,
-                            strEncoderPreprocessor
-                        );
-
-                    DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
-
-                    // Train decision tree model.
-                    DecisionTreeNode mdl = trainer.fit(
-                        ignite,
+                IgniteBiFunction<Integer, Object[], Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
+                    .withEncoderType(EncoderType.STRING_ENCODER)
+                    .withEncodedFeature(1)
+                    .withEncodedFeature(6) // <--- Changed index here.
+                    .fit(ignite,
                         dataCache,
-                        imputingPreprocessor,
-                        lbExtractor
-                    );
+                        featureExtractor
+                );
 
-                    System.out.println("\n>>> Trained model: " + mdl);
-
-                    double accuracy = Evaluator.evaluate(
+                IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
+                    .fit(ignite,
                         dataCache,
-                        mdl,
-                        imputingPreprocessor,
-                        lbExtractor,
-                        new Accuracy<>()
+                        strEncoderPreprocessor
                     );
 
-                    System.out.println("\n>>> Accuracy " + accuracy);
-                    System.out.println("\n>>> Test Error " + (1 - accuracy));
-
-                    System.out.println(">>> Tutorial step 4 (add age and fare) example completed.");
-                }
-                catch (FileNotFoundException e) {
-                    e.printStackTrace();
-                }
-            });
-
-            igniteThread.start();
-
-            igniteThread.join();
+                DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
+
+                // Train decision tree model.
+                DecisionTreeNode mdl = trainer.fit(
+                    ignite,
+                    dataCache,
+                    imputingPreprocessor,
+                    lbExtractor
+                );
+
+                System.out.println("\n>>> Trained model: " + mdl);
+
+                double accuracy = Evaluator.evaluate(
+                    dataCache,
+                    mdl,
+                    imputingPreprocessor,
+                    lbExtractor,
+                    new Accuracy<>()
+                );
+
+                System.out.println("\n>>> Accuracy " + accuracy);
+                System.out.println("\n>>> Test Error " + (1 - accuracy));
+
+                System.out.println(">>> Tutorial step 4 (add age and fare) example completed.");
+            }
+            catch (FileNotFoundException e) {
+                e.printStackTrace();
+            }
         }
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling.java b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling.java
index e3de585..7d934d7 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling.java
@@ -32,7 +32,6 @@ import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator;
 import org.apache.ignite.ml.selection.scoring.metric.Accuracy;
 import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
 import org.apache.ignite.ml.tree.DecisionTreeNode;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * {@link MinMaxScalerTrainer} and {@link NormalizationTrainer} are used in this example due to different values
@@ -54,80 +53,74 @@ public class Step_5_Scaling {
         System.out.println(">>> Tutorial step 5 (scaling) example started.");
 
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                Step_5_Scaling.class.getSimpleName(), () -> {
-                try {
-                    IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
-
-                    // Defines first preprocessor that extracts features from an upstream data.
-                    // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
-                    IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
-                        = (k, v) -> new Object[]{v[0], v[3], v[4], v[5], v[6], v[8], v[10]};
-
-                    IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
-
-                    IgniteBiFunction<Integer, Object[], Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
-                        .withEncoderType(EncoderType.STRING_ENCODER)
-                        .withEncodedFeature(1)
-                        .withEncodedFeature(6) // <--- Changed index here.
-                        .fit(ignite,
-                            dataCache,
-                            featureExtractor
+            try {
+                IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
+
+                // Defines first preprocessor that extracts features from an upstream data.
+                // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
+                IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
+                    = (k, v) -> new Object[]{v[0], v[3], v[4], v[5], v[6], v[8], v[10]};
+
+                IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
+
+                IgniteBiFunction<Integer, Object[], Vector> strEncoderPreprocessor = new EncoderTrainer<Integer, Object[]>()
+                    .withEncoderType(EncoderType.STRING_ENCODER)
+                    .withEncodedFeature(1)
+                    .withEncodedFeature(6) // <--- Changed index here.
+                    .fit(ignite,
+                        dataCache,
+                        featureExtractor
+                );
+
+                IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
+                    .fit(ignite,
+                        dataCache,
+                        strEncoderPreprocessor
                     );
 
-                    IgniteBiFunction<Integer, Object[], Vector> imputingPreprocessor = new ImputerTrainer<Integer, Object[]>()
-                        .fit(ignite,
-                            dataCache,
-                            strEncoderPreprocessor
-                        );
-
-                    IgniteBiFunction<Integer, Object[], Vector> minMaxScalerPreprocessor = new MinMaxScalerTrainer<Integer, Object[]>()
-                        .fit(
-                            ignite,
-                            dataCache,
-                            imputingPreprocessor
-                        );
-
-                    IgniteBiFunction<Integer, Object[], Vector> normalizationPreprocessor = new NormalizationTrainer<Integer, Object[]>()
-                        .withP(1)
-                        .fit(
-                            ignite,
-                            dataCache,
-                            minMaxScalerPreprocessor
-                        );
-
-                    DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
-
-                    // Train decision tree model.
-                    DecisionTreeNode mdl = trainer.fit(
+                IgniteBiFunction<Integer, Object[], Vector> minMaxScalerPreprocessor = new MinMaxScalerTrainer<Integer, Object[]>()
+                    .fit(
                         ignite,
                         dataCache,
-                        normalizationPreprocessor,
-                        lbExtractor
+                        imputingPreprocessor
                     );
 
-                    System.out.println("\n>>> Trained model: " + mdl);
-
-                    double accuracy = Evaluator.evaluate(
+                IgniteBiFunction<Integer, Object[], Vector> normalizationPreprocessor = new NormalizationTrainer<Integer, Object[]>()
+                    .withP(1)
+                    .fit(
+                        ignite,
                         dataCache,
-                        mdl,
-                        normalizationPreprocessor,
-                        lbExtractor,
-                        new Accuracy<>()
+                        minMaxScalerPreprocessor
                     );
 
-                    System.out.println("\n>>> Accuracy " + accuracy);
-                    System.out.println("\n>>> Test Error " + (1 - accuracy));
-
-                    System.out.println(">>> Tutorial step 5 (scaling) example completed.");
-                }
-                catch (FileNotFoundException e) {
-                    e.printStackTrace();
-                }
-            });
-
-            igniteThread.start();
-            igniteThread.join();
+                DecisionTreeClassificationTrainer trainer = new DecisionTreeClassificationTrainer(5, 0);
+
+                // Train decision tree model.
+                DecisionTreeNode mdl = trainer.fit(
+                    ignite,
+                    dataCache,
+                    normalizationPreprocessor,
+                    lbExtractor
+                );
+
+                System.out.println("\n>>> Trained model: " + mdl);
+
+                double accuracy = Evaluator.evaluate(
+                    dataCache,
+                    mdl,
+                    normalizationPreprocessor,
+                    lbExtractor,
+                    new Accuracy<>()
+                );
+
+                System.out.println("\n>>> Accuracy " + accuracy);
+                System.out.println("\n>>> Test Error " + (1 - accuracy));
+
+                System.out.println(">>> Tutorial step 5 (scaling) example completed.");
+            }
+            catch (FileNotFoundException e) {
+                e.printStackTrace();
+            }
         }
     }
 }

http://git-wip-us.apache.org/repos/asf/ignite/blob/609266fe/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling_with_Pipeline.java
----------------------------------------------------------------------
diff --git a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling_with_Pipeline.java b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling_with_Pipeline.java
index 1d5900f..cc0a278 100644
--- a/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling_with_Pipeline.java
+++ b/examples/src/main/java/org/apache/ignite/examples/ml/tutorial/Step_5_Scaling_with_Pipeline.java
@@ -32,7 +32,6 @@ import org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer;
 import org.apache.ignite.ml.selection.scoring.evaluator.Evaluator;
 import org.apache.ignite.ml.selection.scoring.metric.Accuracy;
 import org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer;
-import org.apache.ignite.thread.IgniteThread;
 
 /**
  * {@link MinMaxScalerTrainer} and {@link NormalizationTrainer} are used in this example due to different values
@@ -54,54 +53,48 @@ public class Step_5_Scaling_with_Pipeline {
         System.out.println(">>> Tutorial step 5 (scaling) via Pipeline example started.");
 
         try (Ignite ignite = Ignition.start("examples/config/example-ignite.xml")) {
-            IgniteThread igniteThread = new IgniteThread(ignite.configuration().getIgniteInstanceName(),
-                Step_5_Scaling_with_Pipeline.class.getSimpleName(), () -> {
-                try {
-                    IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
+            try {
+                IgniteCache<Integer, Object[]> dataCache = TitanicUtils.readPassengers(ignite);
 
-                    // Defines first preprocessor that extracts features from an upstream data.
-                    // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
-                    IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
-                        = (k, v) -> new Object[]{v[0], v[3], v[4], v[5], v[6], v[8], v[10]};
+                // Defines first preprocessor that extracts features from an upstream data.
+                // Extracts "pclass", "sibsp", "parch", "sex", "embarked", "age", "fare".
+                IgniteBiFunction<Integer, Object[], Object[]> featureExtractor
+                    = (k, v) -> new Object[]{v[0], v[3], v[4], v[5], v[6], v[8], v[10]};
 
-                    IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
+                IgniteBiFunction<Integer, Object[], Double> lbExtractor = (k, v) -> (double) v[1];
 
-                    PipelineMdl<Integer, Object[]> mdl = new Pipeline<Integer, Object[], Object[]>()
-                        .addFeatureExtractor(featureExtractor)
-                        .addLabelExtractor(lbExtractor)
-                        .addPreprocessor(new EncoderTrainer<Integer, Object[]>()
-                            .withEncoderType(EncoderType.STRING_ENCODER)
-                            .withEncodedFeature(1)
-                            .withEncodedFeature(6))
-                        .addPreprocessor(new ImputerTrainer<Integer, Object[]>())
-                        .addPreprocessor(new MinMaxScalerTrainer<Integer, Object[]>())
-                        .addPreprocessor(new NormalizationTrainer<Integer, Object[]>()
-                            .withP(1))
-                        .addTrainer(new DecisionTreeClassificationTrainer(5, 0))
-                        .fit(ignite, dataCache);
+                PipelineMdl<Integer, Object[]> mdl = new Pipeline<Integer, Object[], Object[]>()
+                    .addFeatureExtractor(featureExtractor)
+                    .addLabelExtractor(lbExtractor)
+                    .addPreprocessor(new EncoderTrainer<Integer, Object[]>()
+                        .withEncoderType(EncoderType.STRING_ENCODER)
+                        .withEncodedFeature(1)
+                        .withEncodedFeature(6))
+                    .addPreprocessor(new ImputerTrainer<Integer, Object[]>())
+                    .addPreprocessor(new MinMaxScalerTrainer<Integer, Object[]>())
+                    .addPreprocessor(new NormalizationTrainer<Integer, Object[]>()
+                        .withP(1))
+                    .addTrainer(new DecisionTreeClassificationTrainer(5, 0))
+                    .fit(ignite, dataCache);
 
-                    System.out.println("\n>>> Trained model: " + mdl);
+                System.out.println("\n>>> Trained model: " + mdl);
 
-                    double accuracy = Evaluator.evaluate(
-                        dataCache,
-                        mdl,
-                        mdl.getFeatureExtractor(),
-                        mdl.getLabelExtractor(),
-                        new Accuracy<>()
-                    );
+                double accuracy = Evaluator.evaluate(
+                    dataCache,
+                    mdl,
+                    mdl.getFeatureExtractor(),
+                    mdl.getLabelExtractor(),
+                    new Accuracy<>()
+                );
 
-                    System.out.println("\n>>> Accuracy " + accuracy);
-                    System.out.println("\n>>> Test Error " + (1 - accuracy));
+                System.out.println("\n>>> Accuracy " + accuracy);
+                System.out.println("\n>>> Test Error " + (1 - accuracy));
 
-                    System.out.println(">>> Tutorial step 5 (scaling) via Pipeline example completed.");
-                }
-                catch (FileNotFoundException e) {
-                    e.printStackTrace();
-                }
-            });
-
-            igniteThread.start();
-            igniteThread.join();
+                System.out.println(">>> Tutorial step 5 (scaling) via Pipeline example completed.");
+            }
+            catch (FileNotFoundException e) {
+                e.printStackTrace();
+            }
         }
     }
 }


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