Examples with Model com.google.cloud.automl.v1.Model used on opensource projects

Example 11 with Model

use of com.google.cloud.automl.v1.Model in project java-aiplatform by googleapis.

the class CreateTrainingPipelineTabularClassificationSample method createTrainingPipelineTableClassification.

static void createTrainingPipelineTableClassification(String project, String modelDisplayName, String datasetId, String targetColumn) throws IOException {
    PipelineServiceSettings pipelineServiceSettings = PipelineServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // 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 trainingTaskDefinition = "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml";
        // Set the columns used for training and their data types
        Transformation transformation1 = Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("sepal_width").build()).build();
        Transformation transformation2 = Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("sepal_length").build()).build();
        Transformation transformation3 = Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("petal_length").build()).build();
        Transformation transformation4 = Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("petal_width").build()).build();
        ArrayList<Transformation> transformationArrayList = new ArrayList<>();
        transformationArrayList.add(transformation1);
        transformationArrayList.add(transformation2);
        transformationArrayList.add(transformation3);
        transformationArrayList.add(transformation4);
        AutoMlTablesInputs autoMlTablesInputs = AutoMlTablesInputs.newBuilder().setTargetColumn(targetColumn).setPredictionType("classification").addAllTransformations(transformationArrayList).setTrainBudgetMilliNodeHours(8000).build();
        FractionSplit fractionSplit = FractionSplit.newBuilder().setTrainingFraction(0.8).setValidationFraction(0.1).setTestFraction(0.1).build();
        InputDataConfig inputDataConfig = InputDataConfig.newBuilder().setDatasetId(datasetId).setFractionSplit(fractionSplit).build();
        Model modelToUpload = Model.newBuilder().setDisplayName(modelDisplayName).build();
        TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder().setDisplayName(modelDisplayName).setTrainingTaskDefinition(trainingTaskDefinition).setTrainingTaskInputs(ValueConverter.toValue(autoMlTablesInputs)).setInputDataConfig(inputDataConfig).setModelToUpload(modelToUpload).build();
        TrainingPipeline trainingPipelineResponse = pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);
        System.out.println("Create Training Pipeline Tabular 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("\tState: %s\n", trainingPipelineResponse.getState());
        System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());
        System.out.format("\tStart Time: %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 inputDataConfigResponse = trainingPipelineResponse.getInputDataConfig();
        System.out.println("\tInput Data Config");
        System.out.format("\t\tDataset Id: %s\n", inputDataConfigResponse.getDatasetId());
        System.out.format("\t\tAnnotations Filter: %s\n", inputDataConfigResponse.getAnnotationsFilter());
        FractionSplit fractionSplitResponse = inputDataConfigResponse.getFractionSplit();
        System.out.println("\t\tFraction Split");
        System.out.format("\t\t\tTraining Fraction: %s\n", fractionSplitResponse.getTrainingFraction());
        System.out.format("\t\t\tValidation Fraction: %s\n", fractionSplitResponse.getValidationFraction());
        System.out.format("\t\t\tTest Fraction: %s\n", fractionSplitResponse.getTestFraction());
        FilterSplit filterSplit = inputDataConfigResponse.getFilterSplit();
        System.out.println("\t\tFilter Split");
        System.out.format("\t\t\tTraining Fraction: %s\n", filterSplit.getTrainingFilter());
        System.out.format("\t\t\tValidation Fraction: %s\n", filterSplit.getValidationFilter());
        System.out.format("\t\t\tTest Fraction: %s\n", filterSplit.getTestFilter());
        PredefinedSplit predefinedSplit = inputDataConfigResponse.getPredefinedSplit();
        System.out.println("\t\tPredefined Split");
        System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());
        TimestampSplit timestampSplit = inputDataConfigResponse.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\tMeta Data: %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().toString());
        System.out.format("\t\tSupported Input Storage Formats: %s\n", modelResponse.getSupportedInputStorageFormatsList().toString());
        System.out.format("\t\tSupported Output Storage Formats: %s\n", modelResponse.getSupportedOutputStorageFormatsList().toString());
        System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());
        System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());
        System.out.format("\t\tLables: %s\n", modelResponse.getLabelsMap());
        PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
        System.out.println("\tPredict Schemata");
        System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
        System.out.format("\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
        System.out.format("\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());
        for (Model.ExportFormat supportedExportFormat : modelResponse.getSupportedExportFormatsList()) {
            System.out.println("\tSupported Export Format");
            System.out.format("\t\tId: %s\n", supportedExportFormat.getId());
        }
        ModelContainerSpec containerSpec = modelResponse.getContainerSpec();
        System.out.println("\tContainer Spec");
        System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
        System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
        System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
        System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
        System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());
        for (EnvVar envVar : containerSpec.getEnvList()) {
            System.out.println("\t\tEnv");
            System.out.format("\t\t\tName: %s\n", envVar.getName());
            System.out.format("\t\t\tValue: %s\n", envVar.getValue());
        }
        for (Port port : containerSpec.getPortsList()) {
            System.out.println("\t\tPort");
            System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
        }
        for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
            System.out.println("\tDeployed Model");
            System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
            System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
        }
        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());
    }
}

Example 12 with Model

use of com.google.cloud.automl.v1.Model in project java-aiplatform by googleapis.

the class CreateTrainingPipelineTabularRegressionSample method createTrainingPipelineTableRegression.

static void createTrainingPipelineTableRegression(String project, String modelDisplayName, String datasetId, String targetColumn) throws IOException {
    PipelineServiceSettings pipelineServiceSettings = PipelineServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // 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 trainingTaskDefinition = "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml";
        // Set the columns used for training and their data types
        ArrayList<Transformation> tranformations = new ArrayList<>();
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("STRING_5000unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("INTEGER_5000unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("FLOAT_5000unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("FLOAT_5000unique_REPEATED")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("NUMERIC_5000unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("BOOLEAN_2unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setTimestamp(TimestampTransformation.newBuilder().setColumnName("TIMESTAMP_1unique_NULLABLE").setInvalidValuesAllowed(true)).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("DATE_1unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("TIME_1unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setTimestamp(TimestampTransformation.newBuilder().setColumnName("DATETIME_1unique_NULLABLE").setInvalidValuesAllowed(true)).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("STRUCT_NULLABLE.STRING_5000unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("STRUCT_NULLABLE.INTEGER_5000unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("STRUCT_NULLABLE.FLOAT_5000unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("STRUCT_NULLABLE.FLOAT_5000unique_REQUIRED")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("STRUCT_NULLABLE.FLOAT_5000unique_REPEATED")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("STRUCT_NULLABLE.NUMERIC_5000unique_NULLABLE")).build());
        tranformations.add(Transformation.newBuilder().setAuto(AutoTransformation.newBuilder().setColumnName("STRUCT_NULLABLE.TIMESTAMP_1unique_NULLABLE")).build());
        AutoMlTablesInputs trainingTaskInputs = AutoMlTablesInputs.newBuilder().addAllTransformations(tranformations).setTargetColumn(targetColumn).setPredictionType("regression").setTrainBudgetMilliNodeHours(8000).setDisableEarlyStopping(false).setOptimizationObjective("minimize-rmse").build();
        FractionSplit fractionSplit = FractionSplit.newBuilder().setTrainingFraction(0.8).setValidationFraction(0.1).setTestFraction(0.1).build();
        InputDataConfig inputDataConfig = InputDataConfig.newBuilder().setDatasetId(datasetId).setFractionSplit(fractionSplit).build();
        Model modelToUpload = Model.newBuilder().setDisplayName(modelDisplayName).build();
        TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder().setDisplayName(modelDisplayName).setTrainingTaskDefinition(trainingTaskDefinition).setTrainingTaskInputs(ValueConverter.toValue(trainingTaskInputs)).setInputDataConfig(inputDataConfig).setModelToUpload(modelToUpload).build();
        TrainingPipeline trainingPipelineResponse = pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);
        System.out.println("Create Training Pipeline Tabular Regression 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("\tState: %s\n", trainingPipelineResponse.getState());
        System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());
        System.out.format("\tStart Time: %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 inputDataConfigResponse = trainingPipelineResponse.getInputDataConfig();
        System.out.println("\tInput Data Config");
        System.out.format("\t\tDataset Id: %s\n", inputDataConfigResponse.getDatasetId());
        System.out.format("\t\tAnnotations Filter: %s\n", inputDataConfigResponse.getAnnotationsFilter());
        FractionSplit fractionSplitResponse = inputDataConfigResponse.getFractionSplit();
        System.out.println("\t\tFraction Split");
        System.out.format("\t\t\tTraining Fraction: %s\n", fractionSplitResponse.getTrainingFraction());
        System.out.format("\t\t\tValidation Fraction: %s\n", fractionSplitResponse.getValidationFraction());
        System.out.format("\t\t\tTest Fraction: %s\n", fractionSplitResponse.getTestFraction());
        FilterSplit filterSplit = inputDataConfigResponse.getFilterSplit();
        System.out.println("\t\tFilter Split");
        System.out.format("\t\t\tTraining Fraction: %s\n", filterSplit.getTrainingFilter());
        System.out.format("\t\t\tValidation Fraction: %s\n", filterSplit.getValidationFilter());
        System.out.format("\t\t\tTest Fraction: %s\n", filterSplit.getTestFilter());
        PredefinedSplit predefinedSplit = inputDataConfigResponse.getPredefinedSplit();
        System.out.println("\t\tPredefined Split");
        System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());
        TimestampSplit timestampSplit = inputDataConfigResponse.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\tMeta Data: %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().toString());
        System.out.format("\t\tSupported Input Storage Formats: %s\n", modelResponse.getSupportedInputStorageFormatsList().toString());
        System.out.format("\t\tSupported Output Storage Formats: %s\n", modelResponse.getSupportedOutputStorageFormatsList().toString());
        System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());
        System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());
        System.out.format("\t\tLables: %s\n", modelResponse.getLabelsMap());
        PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
        System.out.println("\tPredict Schemata");
        System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
        System.out.format("\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
        System.out.format("\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());
        for (Model.ExportFormat supportedExportFormat : modelResponse.getSupportedExportFormatsList()) {
            System.out.println("\tSupported Export Format");
            System.out.format("\t\tId: %s\n", supportedExportFormat.getId());
        }
        ModelContainerSpec containerSpec = modelResponse.getContainerSpec();
        System.out.println("\tContainer Spec");
        System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
        System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
        System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
        System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
        System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());
        for (EnvVar envVar : containerSpec.getEnvList()) {
            System.out.println("\t\tEnv");
            System.out.format("\t\t\tName: %s\n", envVar.getName());
            System.out.format("\t\t\tValue: %s\n", envVar.getValue());
        }
        for (Port port : containerSpec.getPortsList()) {
            System.out.println("\t\tPort");
            System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
        }
        for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
            System.out.println("\tDeployed Model");
            System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
            System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
        }
        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());
    }
}

Example 13 with Model

use of com.google.cloud.automl.v1.Model in project java-aiplatform by googleapis.

the class CreateTrainingPipelineTextSentimentAnalysisSample method createTrainingPipelineTextSentimentAnalysisSample.

static void createTrainingPipelineTextSentimentAnalysisSample(String project, String trainingPipelineDisplayName, String datasetId, String modelDisplayName) throws IOException {
    PipelineServiceSettings pipelineServiceSettings = PipelineServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // 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_sentiment_1.0.0.yaml";
        LocationName locationName = LocationName.of(project, location);
        AutoMlTextSentimentInputs trainingTaskInputs = AutoMlTextSentimentInputs.newBuilder().setSentimentMax(4).build();
        InputDataConfig trainingInputDataConfig = InputDataConfig.newBuilder().setDatasetId(datasetId).build();
        Model model = Model.newBuilder().setDisplayName(modelDisplayName).build();
        TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder().setDisplayName(trainingPipelineDisplayName).setTrainingTaskDefinition(trainingTaskDefinition).setTrainingTaskInputs(ValueConverter.toValue(trainingTaskInputs)).setInputDataConfig(trainingInputDataConfig).setModelToUpload(model).build();
        TrainingPipeline trainingPipelineResponse = pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);
        System.out.println("Create Training Pipeline Text Sentiment Analysis 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());
        }
        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());
    }
}

Example 14 with Model

use of com.google.cloud.automl.v1.Model in project java-aiplatform by googleapis.

the class CreateTrainingPipelineVideoActionRecognitionSample method createTrainingPipelineVideoActionRecognitionSample.

static void createTrainingPipelineVideoActionRecognitionSample(String project, String displayName, String datasetId, String modelDisplayName) throws IOException {
    PipelineServiceSettings settings = PipelineServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    String location = "us-central1";
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PipelineServiceClient client = PipelineServiceClient.create(settings)) {
        AutoMlVideoActionRecognitionInputs trainingTaskInputs = AutoMlVideoActionRecognitionInputs.newBuilder().setModelType(ModelType.CLOUD).build();
        InputDataConfig inputDataConfig = InputDataConfig.newBuilder().setDatasetId(datasetId).build();
        Model modelToUpload = Model.newBuilder().setDisplayName(modelDisplayName).build();
        TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder().setDisplayName(displayName).setTrainingTaskDefinition("gs://google-cloud-aiplatform/schema/trainingjob/definition/" + "automl_video_action_recognition_1.0.0.yaml").setTrainingTaskInputs(ValueConverter.toValue(trainingTaskInputs)).setInputDataConfig(inputDataConfig).setModelToUpload(modelToUpload).build();
        LocationName parent = LocationName.of(project, location);
        TrainingPipeline response = client.createTrainingPipeline(parent, trainingPipeline);
        System.out.format("response: %s\n", response);
        System.out.format("Name: %s\n", response.getName());
    }
}

Example 15 with Model

use of com.google.cloud.automl.v1.Model in project org.hl7.fhir.core by hapifhir.

the class TerminologyClientR5 method read.

@Override
public CanonicalResource read(String type, String id) {
    Class<Resource> t;
    try {
        // todo: do we have to deal with any resource renaming? Use cases are limited...
        t = (Class<Resource>) Class.forName("org.hl7.fhir.r5.model." + type);
    } catch (ClassNotFoundException e) {
        throw new FHIRException("Unable to fetch resources of type " + type + " in R5");
    }
    org.hl7.fhir.r5.model.Resource r5 = client.read(t, id);
    if (r5 != null) {
        throw new FHIRException("Unable to convert resource " + Utilities.pathURL(getAddress(), type, id) + " to R5 (internal representation)");
    }
    if (!(r5 instanceof CanonicalResource)) {
        throw new FHIRException("Unable to convert resource " + Utilities.pathURL(getAddress(), type, id) + " to R5 canonical resource (internal representation)");
    }
    return (CanonicalResource) r5;
}