tests for auto-attach and manual attach; improve JavaDoc

Signed-off-by: Tamás Fenyvesi <tamas.fenyvesi@doknet.hu>

arbiter/arbiter-ui/src/test/java/org/deeplearning4j/arbiter/optimize/TestBasic.java

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.arbiter.optimize;
 
+import io.netty.handler.codec.http.HttpResponseStatus;
 import lombok.NonNull;
 import lombok.extern.slf4j.Slf4j;
 import org.deeplearning4j.BaseDL4JTest;
@@ -65,12 +66,17 @@ import org.nd4j.linalg.function.Function;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
 import java.io.File;
-import java.util.Collections;
+import java.io.IOException;
-import java.util.HashMap;
+import java.io.UnsupportedEncodingException;
-import java.util.Map;
+import java.net.HttpURLConnection;
-import java.util.Properties;
+import java.net.URL;
+import java.net.URLEncoder;
+import java.util.*;
 import java.util.concurrent.TimeUnit;
 
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
 /**
  * Created by Alex on 19/07/2017.
  */
@@ -88,112 +94,15 @@ public class TestBasic extends BaseDL4JTest {
 
         UIServer.getInstance();
 
-        Thread.sleep(1000000);
+        Thread.sleep(1000_000);
     }
 
-    @Test
-    @Ignore
-    public void testBasicUiMultiSession() throws Exception {
-
-        Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
-
-        MultiLayerSpace mls = new MultiLayerSpace.Builder()
-                .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2)))
-                .l2(new ContinuousParameterSpace(0.0001, 0.05))
-                .addLayer(
-                        new ConvolutionLayerSpace.Builder().nIn(1)
-                                .nOut(new IntegerParameterSpace(5, 30))
-                                .kernelSize(new DiscreteParameterSpace<>(new int[]{3, 3},
-                                        new int[]{4, 4}, new int[]{5, 5}))
-                                .stride(new DiscreteParameterSpace<>(new int[]{1, 1},
-                                        new int[]{2, 2}))
-                                .activation(new DiscreteParameterSpace<>(Activation.RELU,
-                                        Activation.SOFTPLUS, Activation.LEAKYRELU))
-                                .build())
-                .addLayer(new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(32, 128))
-                        .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
-                        .build(), new IntegerParameterSpace(0, 1), true) //0 to 1 layers
-                .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
-                        .lossFunction(LossFunctions.LossFunction.MCXENT).build())
-                .setInputType(InputType.convolutionalFlat(28, 28, 1))
-                .build();
-        Map<String, Object> commands = new HashMap<>();
-//        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());
-
-        //Define configuration:
-        CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, commands);
-        DataProvider dataProvider = new MnistDataSetProvider();
-
-
-        String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterUiTestBasicMnist\\").getAbsolutePath();
-
-        File f = new File(modelSavePath);
-        if (f.exists())
-            f.delete();
-        f.mkdir();
-        if (!f.exists())
-            throw new RuntimeException();
-
-        OptimizationConfiguration configuration =
-                new OptimizationConfiguration.Builder()
-                        .candidateGenerator(candidateGenerator).dataProvider(dataProvider)
-                        .modelSaver(new FileModelSaver(modelSavePath))
-                        .scoreFunction(new TestSetLossScoreFunction(true))
-                        .terminationConditions(new MaxTimeCondition(120, TimeUnit.MINUTES),
-                                new MaxCandidatesCondition(100))
-                        .build();
-
-        IOptimizationRunner runner =
-                new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator());
-
-        // add 3 different sessions to the same execution
-        HashMap<String, StatsStorage> statsStorageForSession = new HashMap<>();
-        for (int i = 0; i < 3; i++) {
-            StatsStorage ss = new InMemoryStatsStorage();
-            @NonNull String sessionId = "sid" + i;
-            statsStorageForSession.put(sessionId, ss);
-            StatusListener sl = new ArbiterStatusListener(sessionId, ss);
-            runner.addListeners(sl);
-        }
-
-        Function<String, StatsStorage> statsStorageProvider = statsStorageForSession::get;
-        UIServer uIServer = UIServer.getInstance(true, statsStorageProvider);
-        String serverAddress = uIServer.getAddress();
-        for (String sessionId : statsStorageForSession.keySet()) {
-            log.info("Arbiter session can be attached at {}/arbiter/{}", serverAddress, sessionId);
-        }
-
-        runner.execute();
-
-        Thread.sleep(1000000);
-    }
-
-
     @Test
     @Ignore
     public void testBasicMnist() throws Exception {
         Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
 
-        MultiLayerSpace mls = new MultiLayerSpace.Builder()
+        MultiLayerSpace mls = getMultiLayerSpaceMnist();
-                .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2)))
-                .l2(new ContinuousParameterSpace(0.0001, 0.05))
-                .addLayer(
-                        new ConvolutionLayerSpace.Builder().nIn(1)
-                                .nOut(new IntegerParameterSpace(5, 30))
-                                .kernelSize(new DiscreteParameterSpace<>(new int[]{3, 3},
-                                        new int[]{4, 4}, new int[]{5, 5}))
-                                .stride(new DiscreteParameterSpace<>(new int[]{1, 1},
-                                        new int[]{2, 2}))
-                                .activation(new DiscreteParameterSpace<>(Activation.RELU,
-                                        Activation.SOFTPLUS, Activation.LEAKYRELU))
-                                .build())
-                .addLayer(new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(32, 128))
-                        .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
-                        .build(), new IntegerParameterSpace(0, 1), true) //0 to 1 layers
-                .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
-                        .lossFunction(LossFunctions.LossFunction.MCXENT).build())
-                .setInputType(InputType.convolutionalFlat(28, 28, 1))
-                .build();
         Map<String, Object> commands = new HashMap<>();
 //        commands.put(DataSetIteratorFactoryProvider.FACTORY_KEY, TestDataFactoryProviderMnist.class.getCanonicalName());
 
@@ -230,7 +139,30 @@ public class TestBasic extends BaseDL4JTest {
         UIServer.getInstance().attach(ss);
 
         runner.execute();
-        Thread.sleep(100000);
+        Thread.sleep(1000_000);
+    }
+
+    private static MultiLayerSpace getMultiLayerSpaceMnist() {
+        return new MultiLayerSpace.Builder()
+                .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2)))
+                .l2(new ContinuousParameterSpace(0.0001, 0.05))
+                .addLayer(
+                        new ConvolutionLayerSpace.Builder().nIn(1)
+                                .nOut(new IntegerParameterSpace(5, 30))
+                                .kernelSize(new DiscreteParameterSpace<>(new int[]{3, 3},
+                                        new int[]{4, 4}, new int[]{5, 5}))
+                                .stride(new DiscreteParameterSpace<>(new int[]{1, 1},
+                                        new int[]{2, 2}))
+                                .activation(new DiscreteParameterSpace<>(Activation.RELU,
+                                        Activation.SOFTPLUS, Activation.LEAKYRELU))
+                                .build())
+                .addLayer(new DenseLayerSpace.Builder().nOut(new IntegerParameterSpace(32, 128))
+                        .activation(new DiscreteParameterSpace<>(Activation.RELU, Activation.TANH))
+                        .build(), new IntegerParameterSpace(0, 1), true) //0 to 1 layers
+                .addLayer(new OutputLayerSpace.Builder().nOut(10).activation(Activation.SOFTMAX)
+                        .lossFunction(LossFunctions.LossFunction.MCXENT).build())
+                .setInputType(InputType.convolutionalFlat(28, 28, 1))
+                .build();
     }
 
     @Test
@@ -319,7 +251,7 @@ public class TestBasic extends BaseDL4JTest {
                 .build();
 
         //Define configuration:
-        CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs, Collections.EMPTY_MAP);
+        CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs);
         DataProvider dataProvider = new MnistDataSetProvider();
 
 
@@ -417,7 +349,7 @@ public class TestBasic extends BaseDL4JTest {
         UIServer.getInstance().attach(ss);
 
         runner.execute();
-        Thread.sleep(100000);
+        Thread.sleep(1000_000);
     }
 
 
@@ -482,7 +414,7 @@ public class TestBasic extends BaseDL4JTest {
         UIServer.getInstance().attach(ss);
 
         runner.execute();
-        Thread.sleep(100000);
+        Thread.sleep(1000_000);
     }
 
 
@@ -519,7 +451,7 @@ public class TestBasic extends BaseDL4JTest {
                 .build();
 
         //Define configuration:
-        CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs, Collections.EMPTY_MAP);
+        CandidateGenerator candidateGenerator = new RandomSearchGenerator(cgs);
         DataProvider dataProvider = new MnistDataSetProvider();
 
 
@@ -551,13 +483,17 @@ public class TestBasic extends BaseDL4JTest {
         UIServer.getInstance().attach(ss);
 
         runner.execute();
-        Thread.sleep(100000);
+        Thread.sleep(1000_000);
     }
 
 
+    /**
+     * Visualize multiple optimization sessions run one after another on single-session mode UI
+     * @throws InterruptedException if current thread has been interrupted
+     */
     @Test
     @Ignore
-    public void testBasicMnistMultipleSessions() throws Exception {
+    public void testBasicMnistMultipleSessions() throws InterruptedException {
 
         MultiLayerSpace mls = new MultiLayerSpace.Builder()
                 .updater(new SgdSpace(new ContinuousParameterSpace(0.0001, 0.2)))
@@ -585,8 +521,10 @@ public class TestBasic extends BaseDL4JTest {
 
         //Define configuration:
         CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls, commands);
-        DataProvider dataProvider = new MnistDataSetProvider();
 
+        Class<? extends DataSource> ds = MnistDataSource.class;
+        Properties dsp = new Properties();
+        dsp.setProperty("minibatch", "8");
 
         String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterUiTestBasicMnist\\").getAbsolutePath();
 
@@ -599,7 +537,7 @@ public class TestBasic extends BaseDL4JTest {
 
         OptimizationConfiguration configuration =
                 new OptimizationConfiguration.Builder()
-                        .candidateGenerator(candidateGenerator).dataProvider(dataProvider)
+                        .candidateGenerator(candidateGenerator).dataSource(ds, dsp)
                         .modelSaver(new FileModelSaver(modelSavePath))
                         .scoreFunction(new TestSetLossScoreFunction(true))
                         .terminationConditions(new MaxTimeCondition(1, TimeUnit.MINUTES),
@@ -621,7 +559,7 @@ public class TestBasic extends BaseDL4JTest {
 
         candidateGenerator = new RandomSearchGenerator(mls, commands);
         configuration = new OptimizationConfiguration.Builder()
-                .candidateGenerator(candidateGenerator).dataProvider(dataProvider)
+                .candidateGenerator(candidateGenerator).dataSource(ds, dsp)
                 .modelSaver(new FileModelSaver(modelSavePath))
                 .scoreFunction(new TestSetLossScoreFunction(true))
                 .terminationConditions(new MaxTimeCondition(1, TimeUnit.MINUTES),
@@ -636,7 +574,148 @@ public class TestBasic extends BaseDL4JTest {
 
         runner.execute();
 
-        Thread.sleep(100000);
+        Thread.sleep(1000_000);
+    }
+
+    /**
+     * Auto-attach multiple optimization sessions to multi-session mode UI
+     * @throws IOException if could not connect to the server
+     */
+    @Test
+    public void testUiMultiSessionAutoAttach() throws IOException {
+
+        //Define configuration:
+        MultiLayerSpace mls = getMultiLayerSpaceMnist();
+        CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);
+
+        Class<? extends DataSource> ds = MnistDataSource.class;
+        Properties dsp = new Properties();
+        dsp.setProperty("minibatch", "8");
+
+        String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterUiTestMultiSessionAutoAttach\\")
+                .getAbsolutePath();
+
+        File f = new File(modelSavePath);
+        if (f.exists())
+            f.delete();
+        f.mkdir();
+        if (!f.exists())
+            throw new RuntimeException();
+
+        OptimizationConfiguration configuration =
+                new OptimizationConfiguration.Builder()
+                        .candidateGenerator(candidateGenerator).dataSource(ds, dsp)
+                        .modelSaver(new FileModelSaver(modelSavePath))
+                        .scoreFunction(new TestSetLossScoreFunction(true))
+                        .terminationConditions(new MaxTimeCondition(10, TimeUnit.SECONDS),
+                                new MaxCandidatesCondition(1))
+                        .build();
+
+        IOptimizationRunner runner =
+                new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator());
+
+        // add 3 different sessions to the same execution
+        HashMap<String, StatsStorage> statsStorageForSession = new HashMap<>();
+        for (int i = 0; i < 3; i++) {
+            StatsStorage ss = new InMemoryStatsStorage();
+            @NonNull String sessionId = "sid" + i;
+            statsStorageForSession.put(sessionId, ss);
+            StatusListener sl = new ArbiterStatusListener(sessionId, ss);
+            runner.addListeners(sl);
+        }
+
+        Function<String, StatsStorage> statsStorageProvider = statsStorageForSession::get;
+        UIServer uIServer = UIServer.getInstance(true, statsStorageProvider);
+        String serverAddress = uIServer.getAddress();
+
+        runner.execute();
+
+        for (String sessionId : statsStorageForSession.keySet()) {
+            /*
+             * Visiting /arbiter/:sessionId to auto-attach StatsStorage
+             */
+            String sessionUrl = sessionUrl(uIServer.getAddress(), sessionId);
+            HttpURLConnection conn = (HttpURLConnection) new URL(sessionUrl).openConnection();
+            conn.connect();
+
+            log.info("Checking auto-attaching Arbiter session at {}", sessionUrl(serverAddress, sessionId));
+            assertEquals(HttpResponseStatus.OK.code(), conn.getResponseCode());
+            assertTrue(uIServer.isAttached(statsStorageForSession.get(sessionId)));
+        }
+    }
+
+    /**
+     * Attach multiple optimization sessions to multi-session mode UI by manually visiting session URL
+     * @throws Exception if an error occurred
+     */
+    @Test
+    @Ignore
+    public void testUiMultiSessionManualAttach() throws Exception {
+        Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
+
+        //Define configuration:
+        MultiLayerSpace mls = getMultiLayerSpaceMnist();
+        CandidateGenerator candidateGenerator = new RandomSearchGenerator(mls);
+
+        Class<? extends DataSource> ds = MnistDataSource.class;
+        Properties dsp = new Properties();
+        dsp.setProperty("minibatch", "8");
+
+        String modelSavePath = new File(System.getProperty("java.io.tmpdir"), "ArbiterUiTestBasicMnist\\")
+                .getAbsolutePath();
+
+        File f = new File(modelSavePath);
+        if (f.exists())
+            f.delete();
+        f.mkdir();
+        if (!f.exists())
+            throw new RuntimeException();
+
+        OptimizationConfiguration configuration =
+                new OptimizationConfiguration.Builder()
+                        .candidateGenerator(candidateGenerator).dataSource(ds, dsp)
+                        .modelSaver(new FileModelSaver(modelSavePath))
+                        .scoreFunction(new TestSetLossScoreFunction(true))
+                        .terminationConditions(new MaxTimeCondition(10, TimeUnit.MINUTES),
+                                new MaxCandidatesCondition(10))
+                        .build();
+
+
+        // parallel execution of multiple optimization sessions
+        HashMap<String, StatsStorage> statsStorageForSession = new HashMap<>();
+        for (int i = 0; i < 3; i++) {
+            String sessionId = "sid" + i;
+            IOptimizationRunner runner =
+                    new LocalOptimizationRunner(configuration, new MultiLayerNetworkTaskCreator());
+            StatsStorage ss = new InMemoryStatsStorage();
+            statsStorageForSession.put(sessionId, ss);
+            StatusListener sl = new ArbiterStatusListener(sessionId, ss);
+            runner.addListeners(sl);
+            // Asynchronous execution
+            new Thread(runner::execute).start();
+        }
+
+        Function<String, StatsStorage> statsStorageProvider = statsStorageForSession::get;
+        UIServer uIServer = UIServer.getInstance(true, statsStorageProvider);
+        String serverAddress = uIServer.getAddress();
+
+        for (String sessionId : statsStorageForSession.keySet()) {
+            log.info("Arbiter session can be attached at {}", sessionUrl(serverAddress, sessionId));
+        }
+
+        Thread.sleep(1000_000);
+    }
+
+
+    /**
+     * Get URL for arbiter session on given server address
+     * @param serverAddress server address, e.g.: http://localhost:9000
+     * @param sessionId session ID (will be URL-encoded)
+     * @return URL
+     * @throws UnsupportedEncodingException if the character encoding is not supported
+     */
+    private static String sessionUrl(String serverAddress, String sessionId) throws UnsupportedEncodingException {
+        return String.format("%s/arbiter/%s", serverAddress, URLEncoder.encode(sessionId, "UTF-8"));
     }
 
     private static class MnistDataSetProvider implements DataProvider {

deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-vertx/src/test/java/org/deeplearning4j/ui/TestVertxUIMultiSession.java

@@ -197,9 +197,9 @@ public class TestVertxUIMultiSession extends BaseDL4JTest {
     }
 
     /**
-     * Get URL-encoded URL for training session on given server address
+     * Get URL for training session on given server address
      * @param serverAddress server address
-     * @param sessionId session ID
+     * @param sessionId session ID (will be URL-encoded)
      * @return URL
      * @throws UnsupportedEncodingException if the used encoding is not supported
      */