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samples: ucaip samples batch 6 of 6 (#17)
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‎aiplatform/snippets/pom.xml

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

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

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

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

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

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