테이블 형식 분류를 위한 학습 파이프라인 만들기

create_training_pipeline 메서드를 사용하여 표 형식 분류를 위한 학습 파이프라인을 만듭니다.

더 살펴보기

이 코드 샘플이 포함된 자세한 문서는 다음을 참조하세요.

• 분류 또는 회귀 모델 학습

코드 샘플

Java

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Java 설정 안내를 따르세요. 자세한 내용은 Vertex AI Java API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

import com.google.cloud.aiplatform.util.ValueConverter;
import com.google.cloud.aiplatform.v1.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.FilterSplit;
import com.google.cloud.aiplatform.v1.FractionSplit;
import com.google.cloud.aiplatform.v1.InputDataConfig;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredefinedSplit;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import com.google.cloud.aiplatform.v1.TimestampSplit;
import com.google.cloud.aiplatform.v1.TrainingPipeline;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation.AutoTransformation;
import com.google.rpc.Status;
import java.io.IOException;
import java.util.ArrayList;

public class CreateTrainingPipelineTabularClassificationSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String modelDisplayName = "YOUR_DATASET_DISPLAY_NAME";
    String datasetId = "YOUR_DATASET_ID";
    String targetColumn = "TARGET_COLUMN";
    createTrainingPipelineTableClassification(project, modelDisplayName, datasetId, targetColumn);
  }

  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();

    // 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 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());
    }
  }
}

Node.js

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Node.js 설정 안내를 따르세요. 자세한 내용은 Vertex AI Node.js API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const datasetId = 'YOUR_DATASET_ID';
// const modelDisplayName = 'YOUR_MODEL_DISPLAY_NAME';
// const trainingPipelineDisplayName = 'YOUR_TRAINING_PIPELINE_DISPLAY_NAME';
// const targetColumn = 'YOUR_TARGET_COLUMN';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {definition} =
  aiplatform.protos.google.cloud.aiplatform.v1.schema.trainingjob;

// Imports the Google Cloud Pipeline Service Client library
const {PipelineServiceClient} = aiplatform.v1;
// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const pipelineServiceClient = new PipelineServiceClient(clientOptions);

async function createTrainingPipelineTablesClassification() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;

  const transformations = [
    {auto: {column_name: 'sepal_width'}},
    {auto: {column_name: 'sepal_length'}},
    {auto: {column_name: 'petal_length'}},
    {auto: {column_name: 'petal_width'}},
  ];
  const trainingTaskInputsObj = new definition.AutoMlTablesInputs({
    targetColumn: targetColumn,
    predictionType: 'classification',
    transformations: transformations,
    trainBudgetMilliNodeHours: 8000,
    disableEarlyStopping: false,
    optimizationObjective: 'minimize-log-loss',
  });
  const trainingTaskInputs = trainingTaskInputsObj.toValue();

  const modelToUpload = {displayName: modelDisplayName};
  const inputDataConfig = {
    datasetId: datasetId,
    fractionSplit: {
      trainingFraction: 0.8,
      validationFraction: 0.1,
      testFraction: 0.1,
    },
  };
  const trainingPipeline = {
    displayName: trainingPipelineDisplayName,
    trainingTaskDefinition:
      'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml',
    trainingTaskInputs,
    inputDataConfig,
    modelToUpload,
  };
  const request = {
    parent,
    trainingPipeline,
  };

  // Create training pipeline request
  const [response] =
    await pipelineServiceClient.createTrainingPipeline(request);

  console.log('Create training pipeline tabular classification response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createTrainingPipelineTablesClassification();

Python

이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Vertex AI Python API 참고 문서를 참조하세요.

Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.

from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value


def create_training_pipeline_tabular_classification_sample(
    project: str,
    display_name: str,
    dataset_id: str,
    model_display_name: str,
    target_column: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
    # set the columns used for training and their data types
    transformations = [
        {"auto": {"column_name": "sepal_width"}},
        {"auto": {"column_name": "sepal_length"}},
        {"auto": {"column_name": "petal_length"}},
        {"auto": {"column_name": "petal_width"}},
    ]

    training_task_inputs_dict = {
        # required inputs
        "targetColumn": target_column,
        "predictionType": "classification",
        "transformations": transformations,
        "trainBudgetMilliNodeHours": 8000,
        # optional inputs
        "disableEarlyStopping": False,
        # supported binary classification optimisation objectives:
        # maximize-au-roc, minimize-log-loss, maximize-au-prc,
        # maximize-precision-at-recall, maximize-recall-at-precision
        # supported multi-class classification optimisation objective:
        # minimize-log-loss
        "optimizationObjective": "minimize-log-loss",
        # possibly required inputs
        # required when using maximize-precision-at-recall
        # "optimizationObjectiveRecallValue": 0.5, # 0.0 - 1.0
        # required when using maximize-recall-at-precision
        # "optimizationObjectivePrecisionValue": 0.5, # 0.0 - 1.0
    }
    training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())

    training_pipeline = {
        "display_name": display_name,
        "training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tabular_1.0.0.yaml",
        "training_task_inputs": training_task_inputs,
        "input_data_config": {
            "dataset_id": dataset_id,
            "fraction_split": {
                "training_fraction": 0.8,
                "validation_fraction": 0.1,
                "test_fraction": 0.1,
            },
        },
        "model_to_upload": {"display_name": model_display_name},
    }
    parent = f"projects/{project}/locations/{location}"
    response = client.create_training_pipeline(
        parent=parent, training_pipeline=training_pipeline
    )
    print("response:", response)

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