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samples: ucaip batch samples 4 of 6 (#19)
* samples: ucaip batch samples 4 of 6 * made requested changes * fixed prediction test * added missing resources
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‎aiplatform/snippets/pom.xml

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<version>1.1</version>
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<scope>test</scope>
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</dependency>
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</dependencies>
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</project>

‎aiplatform/snippets/resources/daisy.jpg

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

‎aiplatform/snippets/src/main/java/aiplatform/DeleteModelSample.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_delete_model_sample]
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import com.google.api.gax.longrunning.OperationFuture;
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import com.google.cloud.aiplatform.v1beta1.DeleteOperationMetadata;
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import com.google.cloud.aiplatform.v1beta1.ModelName;
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import com.google.cloud.aiplatform.v1beta1.ModelServiceClient;
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import com.google.cloud.aiplatform.v1beta1.ModelServiceSettings;
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import com.google.protobuf.Empty;
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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 DeleteModelSample {
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public static void main(String[] args)
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throws IOException, ExecutionException, InterruptedException, 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 modelId = "YOUR_MODEL_ID";
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deleteModel(project, modelId);
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}
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static void deleteModel(String project, String modelId)
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throws IOException, ExecutionException, InterruptedException, TimeoutException {
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ModelServiceSettings modelServiceSettings =
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ModelServiceSettings.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 (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
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String location = "us-central1";
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ModelName modelName = ModelName.of(project, location, modelId);
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OperationFuture<Empty, DeleteOperationMetadata> operationFuture =
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modelServiceClient.deleteModelAsync(modelName);
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System.out.format("Operation name: %s\n", operationFuture.getInitialFuture().get().getName());
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System.out.println("Waiting for operation to finish...");
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operationFuture.get(300, TimeUnit.SECONDS);
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System.out.format("Deleted Model.");
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}
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}
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}
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// [END aiplatform_delete_model_sample]

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