텍스트 항목 추출을 위한 학습 파이프라인 만들기

create_training_pipeline 메서드를 사용하여 텍스트 항목 추출을 위한 학습 파이프라인을 만듭니다.

더 살펴보기

이 코드 샘플이 포함된 자세한 문서는 다음을 참조하세요.

코드 샘플

자바

Vertex AI용 클라이언트 라이브러리를 설치하고 사용하는 방법은 Vertex AI 클라이언트 라이브러리를 참조하세요. 자세한 내용은 Vertex AI 자바 API 참조 문서를 확인하세요.


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.Model.ExportFormat;
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.rpc.Status;
import java.io.IOException;

public class CreateTrainingPipelineTextEntityExtractionSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String trainingPipelineDisplayName = "YOUR_TRAINING_PIPELINE_DISPLAY_NAME";
    String project = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    String modelDisplayName = "YOUR_MODEL_DISPLAY_NAME";

    createTrainingPipelineTextEntityExtractionSample(
        project, trainingPipelineDisplayName, datasetId, modelDisplayName);
  }

  static void createTrainingPipelineTextEntityExtractionSample(
      String project, String trainingPipelineDisplayName, String datasetId, String modelDisplayName)
      throws IOException {
    PipelineServiceSettings pipelineServiceSettings =
        PipelineServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();

    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PipelineServiceClient pipelineServiceClient =
        PipelineServiceClient.create(pipelineServiceSettings)) {
      String location = "us-central1";
      String trainingTaskDefinition =
          "gs://google-cloud-aiplatform/schema/trainingjob/definition/"
              + "automl_text_extraction_1.0.0.yaml";

      LocationName locationName = LocationName.of(project, location);

      InputDataConfig trainingInputDataConfig =
          InputDataConfig.newBuilder().setDatasetId(datasetId).build();
      Model model = Model.newBuilder().setDisplayName(modelDisplayName).build();
      TrainingPipeline trainingPipeline =
          TrainingPipeline.newBuilder()
              .setDisplayName(trainingPipelineDisplayName)
              .setTrainingTaskDefinition(trainingTaskDefinition)
              .setTrainingTaskInputs(ValueConverter.EMPTY_VALUE)
              .setInputDataConfig(trainingInputDataConfig)
              .setModelToUpload(model)
              .build();

      TrainingPipeline trainingPipelineResponse =
          pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);

      System.out.println("Create Training Pipeline Text Entity Extraction 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());
    }
  }
}

Node.js

Vertex AI용 클라이언트 라이브러리를 설치하고 사용하는 방법은 Vertex AI 클라이언트 라이브러리를 참조하세요. 자세한 내용은 Vertex AI Node.js API 참조 문서를 확인하세요.

/**
 * 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 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 createTrainingPipelineTextEntityExtraction() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;

  const trainingTaskInputObj = new definition.AutoMlTextExtractionInputs({});
  const trainingTaskInputs = trainingTaskInputObj.toValue();

  const modelToUpload = {displayName: modelDisplayName};
  const inputDataConfig = {datasetId: datasetId};
  const trainingPipeline = {
    displayName: trainingPipelineDisplayName,
    trainingTaskDefinition:
      'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_text_extraction_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 text entity extraction response :');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createTrainingPipelineTextEntityExtraction();

Python

Vertex AI용 클라이언트 라이브러리를 설치하고 사용하는 방법은 Vertex AI 클라이언트 라이브러리를 참조하세요. 자세한 내용은 Vertex AI Python API 참조 문서를 확인하세요.

from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import trainingjob

def create_training_pipeline_text_entity_extraction_sample(
    project: str,
    display_name: str,
    dataset_id: str,
    model_display_name: 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)
    training_task_inputs = (
        trainingjob.definition.AutoMlTextExtractionInputs().to_value()
    )

    training_pipeline = {
        "display_name": display_name,
        "training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_text_extraction_1.0.0.yaml",
        # Training task inputs are empty for text entity extraction
        "training_task_inputs": training_task_inputs,
        "input_data_config": {"dataset_id": dataset_id},
        "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)

다음 단계

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