samples: ucaip samples batch 6 of 6 (#17)

aiplatform/snippets/pom.xml

@@ -22,15 +22,24 @@
     <maven.compiler.source>1.8</maven.compiler.source>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
   </properties>
-
-
   <!-- [START aiplatform_install_with_bom] -->
   <dependencies>
     <dependency>
       <groupId>com.google.cloud</groupId>
       <artifactId>google-cloud-aiplatform</artifactId>
       <version>0.0.1-SNAPSHOT</version>
     </dependency>
+    <!-- [END aiplatform_install_with_bom] -->
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java-util</artifactId>
+      <version>4.0.0-rc-1</version>
+    </dependency>
+    <dependency>
+      <groupId>com.google.cloud</groupId>
+      <artifactId>google-cloud-storage</artifactId>
+      <version>1.111.0</version>
+    </dependency>
     <dependency>
       <groupId>com.google.cloud</groupId>
       <artifactId>google-cloud-storage</artifactId>

aiplatform/snippets/src/main/java/aiplatform/CreateDatasetTextSample.java

@@ -0,0 +1,84 @@
+/*
+ * Copyright 2020 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package aiplatform;
+
+// [START aiplatform_create_dataset_text_sample]
+
+import com.google.api.gax.longrunning.OperationFuture;
+import com.google.cloud.aiplatform.v1beta1.CreateDatasetOperationMetadata;
+import com.google.cloud.aiplatform.v1beta1.Dataset;
+import com.google.cloud.aiplatform.v1beta1.DatasetServiceClient;
+import com.google.cloud.aiplatform.v1beta1.DatasetServiceSettings;
+import com.google.cloud.aiplatform.v1beta1.LocationName;
+import java.io.IOException;
+import java.util.concurrent.ExecutionException;
+import java.util.concurrent.TimeUnit;
+import java.util.concurrent.TimeoutException;
+
+public class CreateDatasetTextSample {
+
+  public static void main(String[] args)
+      throws IOException, InterruptedException, ExecutionException, TimeoutException {
+    // TODO(developer): Replace these variables before running the sample.
+    String project = "YOUR_PROJECT_ID";
+    String datasetDisplayName = "YOUR_DATASET_DISPLAY_NAME";
+
+    createDatasetTextSample(project, datasetDisplayName);
+  }
+
+  static void createDatasetTextSample(String project, String datasetDisplayName)
+      throws IOException, InterruptedException, ExecutionException, TimeoutException {
+    DatasetServiceSettings datasetServiceSettings =
+        DatasetServiceSettings.newBuilder()
+            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
+            .build();
+
+    // Initialize client that will be used to send requests. This client only needs to be created
+    // once, and can be reused for multiple requests. After completing all of your requests, call
+    // the "close" method on the client to safely clean up any remaining background resources.
+    try (DatasetServiceClient datasetServiceClient =
+        DatasetServiceClient.create(datasetServiceSettings)) {
+      String location = "us-central1";
+      String metadataSchemaUri =
+          "gs://google-cloud-aiplatform/schema/dataset/metadata/text_1.0.0.yaml";
+
+      LocationName locationName = LocationName.of(project, location);
+      Dataset dataset =
+          Dataset.newBuilder()
+              .setDisplayName(datasetDisplayName)
+              .setMetadataSchemaUri(metadataSchemaUri)
+              .build();
+
+      OperationFuture<Dataset, CreateDatasetOperationMetadata> datasetFuture =
+          datasetServiceClient.createDatasetAsync(locationName, dataset);
+      System.out.format("Operation name: %s\n", datasetFuture.getInitialFuture().get().getName());
+
+      System.out.println("Waiting for operation to finish...");
+      Dataset datasetResponse = datasetFuture.get(120, TimeUnit.SECONDS);
+
+      System.out.println("Create Text Dataset Response");
+      System.out.format("\tName: %s\n", datasetResponse.getName());
+      System.out.format("\tDisplay Name: %s\n", datasetResponse.getDisplayName());
+      System.out.format("\tMetadata Schema Uri: %s\n", datasetResponse.getMetadataSchemaUri());
+      System.out.format("\tMetadata: %s\n", datasetResponse.getMetadata());
+      System.out.format("\tCreate Time: %s\n", datasetResponse.getCreateTime());
+      System.out.format("\tUpdate Time: %s\n", datasetResponse.getUpdateTime());
+      System.out.format("\tLabels: %s\n", datasetResponse.getLabelsMap());
+    }
+  }
+}
+// [END aiplatform_create_dataset_text_sample]

aiplatform/snippets/src/main/java/aiplatform/CreateTrainingPipelineTextClassificationSample.java

@@ -0,0 +1,235 @@
+/*
+ * Copyright 2020 Google LLC
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package aiplatform;
+
+// [START aiplatform_create_training_pipeline_text_classification_sample]
+
+import com.google.cloud.aiplatform.v1beta1.DeployedModelRef;
+import com.google.cloud.aiplatform.v1beta1.EnvVar;
+import com.google.cloud.aiplatform.v1beta1.ExplanationMetadata;
+import com.google.cloud.aiplatform.v1beta1.ExplanationParameters;
+import com.google.cloud.aiplatform.v1beta1.ExplanationSpec;
+import com.google.cloud.aiplatform.v1beta1.FilterSplit;
+import com.google.cloud.aiplatform.v1beta1.FractionSplit;
+import com.google.cloud.aiplatform.v1beta1.InputDataConfig;
+import com.google.cloud.aiplatform.v1beta1.LocationName;
+import com.google.cloud.aiplatform.v1beta1.Model;
+import com.google.cloud.aiplatform.v1beta1.Model.ExportFormat;
+import com.google.cloud.aiplatform.v1beta1.ModelContainerSpec;
+import com.google.cloud.aiplatform.v1beta1.PipelineServiceClient;
+import com.google.cloud.aiplatform.v1beta1.PipelineServiceSettings;
+import com.google.cloud.aiplatform.v1beta1.Port;
+import com.google.cloud.aiplatform.v1beta1.PredefinedSplit;
+import com.google.cloud.aiplatform.v1beta1.PredictSchemata;
+import com.google.cloud.aiplatform.v1beta1.SampledShapleyAttribution;
+import com.google.cloud.aiplatform.v1beta1.TimestampSplit;
+import com.google.cloud.aiplatform.v1beta1.TrainingPipeline;
+import com.google.protobuf.Any;
+import com.google.protobuf.Value;
+import com.google.protobuf.util.JsonFormat;
+import com.google.rpc.Status;
+import java.io.IOException;
+import java.util.List;
+
+public class CreateTrainingPipelineTextClassificationSample {
+
+  public static void main(String[] args) throws IOException {
+    // TODO(developer): Replace these variables before running the sample.
+    String trainingPipelineDisplayName = "YOUR_TRAINING_PIPELINE_DISPLAY_NAME";
+    String project = "YOUR_PROJECT_ID";
+    String datasetId = "YOUR_DATASET_ID";
+    String modelDisplayName = "YOUR_MODEL_DISPLAY_NAME";
+
+    createTrainingPipelineTextClassificationSample(
+        project, trainingPipelineDisplayName, datasetId, modelDisplayName);
+  }
+
+  static void createTrainingPipelineTextClassificationSample(
+      String project, String trainingPipelineDisplayName, String datasetId, String modelDisplayName)
+      throws IOException {
+    PipelineServiceSettings pipelineServiceSettings =
+        PipelineServiceSettings.newBuilder()
+            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
+            .build();
+
+    // Initialize client that will be used to send requests. This client only needs to be created
+    // once, and can be reused for multiple requests. After completing all of your requests, call
+    // the "close" method on the client to safely clean up any remaining background resources.
+    try (PipelineServiceClient pipelineServiceClient =
+        PipelineServiceClient.create(pipelineServiceSettings)) {
+      String location = "us-central1";
+      String trainingTaskDefinition =
+          "gs://google-cloud-aiplatform/schema/trainingjob/definition/"
+                  + "automl_text_classification_1.0.0.yaml";
+      String jsonString = "{\"multiLabel\": false}";
+
+      LocationName locationName = LocationName.of(project, location);
+
+      Value.Builder trainingTaskInputs = Value.newBuilder();
+      JsonFormat.parser().merge(jsonString, trainingTaskInputs);
+
+      InputDataConfig trainingInputDataConfig =
+          InputDataConfig.newBuilder().setDatasetId(datasetId).build();
+      Model model = Model.newBuilder().setDisplayName(modelDisplayName).build();
+      TrainingPipeline trainingPipeline =
+          TrainingPipeline.newBuilder()
+              .setDisplayName(trainingPipelineDisplayName)
+              .setTrainingTaskDefinition(trainingTaskDefinition)
+              .setTrainingTaskInputs(trainingTaskInputs)
+              .setInputDataConfig(trainingInputDataConfig)
+              .setModelToUpload(model)
+              .build();
+
+      TrainingPipeline trainingPipelineResponse =
+          pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);
+
+      System.out.println("Create Training Pipeline Text Classification Response");
+      System.out.format("\tName: %s\n", trainingPipelineResponse.getName());
+      System.out.format("\tDisplay Name: %s\n", trainingPipelineResponse.getDisplayName());
+
+      System.out.format(
+          "\tTraining Task Definition %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
+      System.out.format(
+          "\tTraining Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
+      System.out.format(
+          "\tTraining Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());
+      System.out.format("State: %s\n", trainingPipelineResponse.getState());
+
+      System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());
+      System.out.format("\tStartTime %s\n", trainingPipelineResponse.getStartTime());
+      System.out.format("\tEnd Time: %s\n", trainingPipelineResponse.getEndTime());
+      System.out.format("\tUpdate Time: %s\n", trainingPipelineResponse.getUpdateTime());
+      System.out.format("\tLabels: %s\n", trainingPipelineResponse.getLabelsMap());
+
+      InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();
+      System.out.println("\tInput Data Config");
+      System.out.format("\t\tDataset Id: %s", inputDataConfig.getDatasetId());
+      System.out.format("\t\tAnnotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());
+
+      FractionSplit fractionSplit = inputDataConfig.getFractionSplit();
+      System.out.println("\t\tFraction Split");
+      System.out.format("\t\t\tTraining Fraction: %s\n", fractionSplit.getTrainingFraction());
+      System.out.format("\t\t\tValidation Fraction: %s\n", fractionSplit.getValidationFraction());
+      System.out.format("\t\t\tTest Fraction: %s\n", fractionSplit.getTestFraction());
+
+      FilterSplit filterSplit = inputDataConfig.getFilterSplit();
+      System.out.println("\t\tFilter Split");
+      System.out.format("\t\t\tTraining Filter: %s\n", filterSplit.getTrainingFilter());
+      System.out.format("\t\t\tValidation Filter: %s\n", filterSplit.getValidationFilter());
+      System.out.format("\t\t\tTest Filter: %s\n", filterSplit.getTestFilter());
+
+      PredefinedSplit predefinedSplit = inputDataConfig.getPredefinedSplit();
+      System.out.println("\t\tPredefined Split");
+      System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());
+
+      TimestampSplit timestampSplit = inputDataConfig.getTimestampSplit();
+      System.out.println("\t\tTimestamp Split");
+      System.out.format("\t\t\tTraining Fraction: %s\n", timestampSplit.getTrainingFraction());
+      System.out.format("\t\t\tValidation Fraction: %s\n", timestampSplit.getValidationFraction());
+      System.out.format("\t\t\tTest Fraction: %s\n", timestampSplit.getTestFraction());
+      System.out.format("\t\t\tKey: %s\n", timestampSplit.getKey());
+
+      Model modelResponse = trainingPipelineResponse.getModelToUpload();
+      System.out.println("\tModel To Upload");
+      System.out.format("\t\tName: %s\n", modelResponse.getName());
+      System.out.format("\t\tDisplay Name: %s\n", modelResponse.getDisplayName());
+      System.out.format("\t\tDescription: %s\n", modelResponse.getDescription());
+
+      System.out.format("\t\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
+      System.out.format("\t\tMetadata: %s\n", modelResponse.getMetadata());
+      System.out.format("\t\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
+      System.out.format("\t\tArtifact Uri: %s\n", modelResponse.getArtifactUri());
+
+      System.out.format(
+          "\t\tSupported Deployment Resources Types: %s\n",
+          modelResponse.getSupportedDeploymentResourcesTypesList());
+      System.out.format(
+          "\t\tSupported Input Storage Formats: %s\n",
+          modelResponse.getSupportedInputStorageFormatsList());
+      System.out.format(
+          "\t\tSupported Output Storage Formats: %s\n",
+          modelResponse.getSupportedOutputStorageFormatsList());
+
+      System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());
+      System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());
+      System.out.format("\t\tLabels: %sn\n", modelResponse.getLabelsMap());
+
+      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
+      System.out.println("\t\tPredict Schemata");
+      System.out.format("\t\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
+      System.out.format(
+          "\t\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
+      System.out.format(
+          "\t\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());
+
+      for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {
+        System.out.println("\t\tSupported Export Format");
+        System.out.format("\t\t\tId: %s\n", exportFormat.getId());
+      }
+
+      ModelContainerSpec modelContainerSpec = modelResponse.getContainerSpec();
+      System.out.println("\t\tContainer Spec");
+      System.out.format("\t\t\tImage Uri: %s\n", modelContainerSpec.getImageUri());
+      System.out.format("\t\t\tCommand: %s\n", modelContainerSpec.getCommandList());
+      System.out.format("\t\t\tArgs: %s\n", modelContainerSpec.getArgsList());
+      System.out.format("\t\t\tPredict Route: %s\n", modelContainerSpec.getPredictRoute());
+      System.out.format("\t\t\tHealth Route: %s\n", modelContainerSpec.getHealthRoute());
+
+      for (EnvVar envVar : modelContainerSpec.getEnvList()) {
+        System.out.println("\t\t\tEnv");
+        System.out.format("\t\t\t\tName: %s\n", envVar.getName());
+        System.out.format("\t\t\t\tValue: %s\n", envVar.getValue());
+      }
+
+      for (Port port : modelContainerSpec.getPortsList()) {
+        System.out.println("\t\t\tPort");
+        System.out.format("\t\t\t\tContainer Port: %s\n", port.getContainerPort());
+      }
+
+      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
+        System.out.println("\t\tDeployed Model");
+        System.out.format("\t\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
+        System.out.format("\t\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
+      }
+
+      ExplanationSpec explanationSpec = modelResponse.getExplanationSpec();
+      System.out.println("\t\tExplanation Spec");
+
+      ExplanationParameters explanationParameters = explanationSpec.getParameters();
+      System.out.println("\t\t\tParameters");
+
+      SampledShapleyAttribution sampledShapleyAttribution =
+          explanationParameters.getSampledShapleyAttribution();
+      System.out.println("\t\t\t\tSampled Shapley Attribution");
+      System.out.format("\t\t\t\t\tPath Count: %s\n", sampledShapleyAttribution.getPathCount());
+
+      ExplanationMetadata explanationMetadata = explanationSpec.getMetadata();
+      System.out.println("\t\t\tMetadata");
+      System.out.format("\t\t\t\tInputs: %s\n", explanationMetadata.getInputsMap());
+      System.out.format("\t\t\t\tOutputs: %s\n", explanationMetadata.getOutputsMap());
+      System.out.format(
+          "\t\t\t\tFeature Attributions Schema_uri: %s\n",
+          explanationMetadata.getFeatureAttributionsSchemaUri());
+
+      Status status = trainingPipelineResponse.getError();
+      System.out.println("\tError");
+      System.out.format("\t\tCode: %s\n", status.getCode());
+      System.out.format("\t\tMessage: %s\n", status.getMessage());
+    }
+  }
+}
+// [END aiplatform_create_training_pipeline_text_classification_sample]