samples: ucaip batch samples 4 of 6 (#19)

* samples: ucaip batch samples 4 of 6

* made requested changes

* fixed prediction test

* added missing resources

aiplatform/snippets/pom.xml

@@ -55,5 +55,6 @@
       <version>1.1</version>
       <scope>test</scope>
     </dependency>
+
   </dependencies>
 </project>

aiplatform/snippets/src/main/java/aiplatform/CreateTrainingPipelineSample.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_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 CreateTrainingPipelineSample {
+
+  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 trainingTaskDefinition = "YOUR_TRAINING_TASK_DEFINITION";
+    String modelDisplayName = "YOUR_MODEL_DISPLAY_NAME";
+    createTrainingPipelineSample(
+        project, trainingPipelineDisplayName, datasetId, trainingTaskDefinition, modelDisplayName);
+  }
+
+  static void createTrainingPipelineSample(
+      String project,
+      String trainingPipelineDisplayName,
+      String datasetId,
+      String trainingTaskDefinition,
+      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";
+      LocationName locationName = LocationName.of(project, location);
+
+      String jsonString =
+          "{\"multiLabel\": false, \"modelType\": \"CLOUD\", \"budgetMilliNodeHours\": 8000,"
+              + " \"disableEarlyStopping\": false}";
+      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 Response");
+      System.out.format("Name: %s\n", trainingPipelineResponse.getName());
+      System.out.format("Display Name: %s\n", trainingPipelineResponse.getDisplayName());
+
+      System.out.format(
+          "Training Task Definition %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
+      System.out.format(
+          "Training Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
+      System.out.format(
+          "Training Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());
+      System.out.format("State: %s\n", trainingPipelineResponse.getState());
+
+      System.out.format("Create Time: %s\n", trainingPipelineResponse.getCreateTime());
+      System.out.format("StartTime %s\n", trainingPipelineResponse.getStartTime());
+      System.out.format("End Time: %s\n", trainingPipelineResponse.getEndTime());
+      System.out.format("Update Time: %s\n", trainingPipelineResponse.getUpdateTime());
+      System.out.format("Labels: %s\n", trainingPipelineResponse.getLabelsMap());
+
+      InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();
+      System.out.println("Input Data Config");
+      System.out.format("Dataset Id: %s", inputDataConfig.getDatasetId());
+      System.out.format("Annotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());
+
+      FractionSplit fractionSplit = inputDataConfig.getFractionSplit();
+      System.out.println("Fraction Split");
+      System.out.format("Training Fraction: %s\n", fractionSplit.getTrainingFraction());
+      System.out.format("Validation Fraction: %s\n", fractionSplit.getValidationFraction());
+      System.out.format("Test Fraction: %s\n", fractionSplit.getTestFraction());
+
+      FilterSplit filterSplit = inputDataConfig.getFilterSplit();
+      System.out.println("Filter Split");
+      System.out.format("Training Filter: %s\n", filterSplit.getTrainingFilter());
+      System.out.format("Validation Filter: %s\n", filterSplit.getValidationFilter());
+      System.out.format("Test Filter: %s\n", filterSplit.getTestFilter());
+
+      PredefinedSplit predefinedSplit = inputDataConfig.getPredefinedSplit();
+      System.out.println("Predefined Split");
+      System.out.format("Key: %s\n", predefinedSplit.getKey());
+
+      TimestampSplit timestampSplit = inputDataConfig.getTimestampSplit();
+      System.out.println("Timestamp Split");
+      System.out.format("Training Fraction: %s\n", timestampSplit.getTrainingFraction());
+      System.out.format("Validation Fraction: %s\n", timestampSplit.getValidationFraction());
+      System.out.format("Test Fraction: %s\n", timestampSplit.getTestFraction());
+      System.out.format("Key: %s\n", timestampSplit.getKey());
+
+      Model modelResponse = trainingPipelineResponse.getModelToUpload();
+      System.out.println("Model To Upload");
+      System.out.format("Name: %s\n", modelResponse.getName());
+      System.out.format("Display Name: %s\n", modelResponse.getDisplayName());
+      System.out.format("Description: %s\n", modelResponse.getDescription());
+
+      System.out.format("Metadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
+      System.out.format("Metadata: %s\n", modelResponse.getMetadata());
+      System.out.format("Training Pipeline: %s\n", modelResponse.getTrainingPipeline());
+      System.out.format("Artifact Uri: %s\n", modelResponse.getArtifactUri());
+
+      System.out.format(
+          "Supported Deployment Resources Types: %s\n",
+          modelResponse.getSupportedDeploymentResourcesTypesList());
+      System.out.format(
+          "Supported Input Storage Formats: %s\n",
+          modelResponse.getSupportedInputStorageFormatsList());
+      System.out.format(
+          "Supported Output Storage Formats: %s\n",
+          modelResponse.getSupportedOutputStorageFormatsList());
+
+      System.out.format("Create Time: %s\n", modelResponse.getCreateTime());
+      System.out.format("Update Time: %s\n", modelResponse.getUpdateTime());
+      System.out.format("Labels: %sn\n", modelResponse.getLabelsMap());
+
+      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
+      System.out.println("Predict Schemata");
+      System.out.format("Instance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
+      System.out.format("Parameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
+      System.out.format("Prediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());
+
+      for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {
+        System.out.println("Supported Export Format");
+        System.out.format("Id: %s\n", exportFormat.getId());
+      }
+
+      ModelContainerSpec modelContainerSpec = modelResponse.getContainerSpec();
+      System.out.println("Container Spec");
+      System.out.format("Image Uri: %s\n", modelContainerSpec.getImageUri());
+      System.out.format("Command: %s\n", modelContainerSpec.getCommandList());
+      System.out.format("Args: %s\n", modelContainerSpec.getArgsList());
+      System.out.format("Predict Route: %s\n", modelContainerSpec.getPredictRoute());
+      System.out.format("Health Route: %s\n", modelContainerSpec.getHealthRoute());
+
+      for (EnvVar envVar : modelContainerSpec.getEnvList()) {
+        System.out.println("Env");
+        System.out.format("Name: %s\n", envVar.getName());
+        System.out.format("Value: %s\n", envVar.getValue());
+      }
+
+      for (Port port : modelContainerSpec.getPortsList()) {
+        System.out.println("Port");
+        System.out.format("Container Port: %s\n", port.getContainerPort());
+      }
+
+      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
+        System.out.println("Deployed Model");
+        System.out.format("Endpoint: %s\n", deployedModelRef.getEndpoint());
+        System.out.format("Deployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
+      }
+
+      ExplanationSpec explanationSpec = modelResponse.getExplanationSpec();
+      System.out.println("Explanation Spec");
+
+      ExplanationParameters explanationParameters = explanationSpec.getParameters();
+      System.out.println("Parameters");
+
+      SampledShapleyAttribution sampledShapleyAttribution =
+          explanationParameters.getSampledShapleyAttribution();
+      System.out.println("Sampled Shapley Attribution");
+      System.out.format("Path Count: %s\n", sampledShapleyAttribution.getPathCount());
+
+      ExplanationMetadata explanationMetadata = explanationSpec.getMetadata();
+      System.out.println("Metadata");
+      System.out.format("Inputs: %s\n", explanationMetadata.getInputsMap());
+      System.out.format("Outputs: %s\n", explanationMetadata.getOutputsMap());
+      System.out.format(
+          "Feature Attributions Schema_uri: %s\n",
+          explanationMetadata.getFeatureAttributionsSchemaUri());
+
+      Status status = trainingPipelineResponse.getError();
+      System.out.println("Error");
+      System.out.format("Code: %s\n", status.getCode());
+      System.out.format("Message: %s\n", status.getMessage());
+    }
+  }
+}
+// [END aiplatform_create_training_pipeline_sample]

aiplatform/snippets/src/main/java/aiplatform/DeleteModelSample.java

@@ -0,0 +1,63 @@
+/*
+ * 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_delete_model_sample]
+
+import com.google.api.gax.longrunning.OperationFuture;
+import com.google.cloud.aiplatform.v1beta1.DeleteOperationMetadata;
+import com.google.cloud.aiplatform.v1beta1.ModelName;
+import com.google.cloud.aiplatform.v1beta1.ModelServiceClient;
+import com.google.cloud.aiplatform.v1beta1.ModelServiceSettings;
+import com.google.protobuf.Empty;
+import java.io.IOException;
+import java.util.concurrent.ExecutionException;
+import java.util.concurrent.TimeUnit;
+import java.util.concurrent.TimeoutException;
+
+public class DeleteModelSample {
+  public static void main(String[] args)
+      throws IOException, ExecutionException, InterruptedException, TimeoutException {
+    // TODO(developer): Replace these variables before running the sample.
+    String project = "YOUR_PROJECT_ID";
+    String modelId = "YOUR_MODEL_ID";
+    deleteModel(project, modelId);
+  }
+
+  static void deleteModel(String project, String modelId)
+      throws IOException, ExecutionException, InterruptedException, TimeoutException {
+    ModelServiceSettings modelServiceSettings =
+        ModelServiceSettings.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 (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
+      String location = "us-central1";
+      ModelName modelName = ModelName.of(project, location, modelId);
+      OperationFuture<Empty, DeleteOperationMetadata> operationFuture =
+          modelServiceClient.deleteModelAsync(modelName);
+      System.out.format("Operation name: %s\n", operationFuture.getInitialFuture().get().getName());
+      System.out.println("Waiting for operation to finish...");
+      operationFuture.get(300, TimeUnit.SECONDS);
+      System.out.format("Deleted Model.");
+    }
+  }
+}
+// [END aiplatform_delete_model_sample]