커스텀 학습 관리 데이터 세트를 위한 학습 파이프라인 만들기

create_training_pipeline 메서드를 사용하여 커스텀 학습 관리 데이터 세트를 위한 학습 파이프라인을 만듭니다.

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.v1.GcsDestination;
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.TrainingPipeline;
import com.google.gson.JsonArray;
import com.google.gson.JsonObject;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;

public class CreateTrainingPipelineCustomTrainingManagedDatasetSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "PROJECT";
    String displayName = "DISPLAY_NAME";
    String modelDisplayName = "MODEL_DISPLAY_NAME";
    String datasetId = "DATASET_ID";
    String annotationSchemaUri = "ANNOTATION_SCHEMA_URI";
    String trainingContainerSpecImageUri = "TRAINING_CONTAINER_SPEC_IMAGE_URI";
    String modelContainerSpecImageUri = "MODEL_CONTAINER_SPEC_IMAGE_URI";
    String baseOutputUriPrefix = "BASE_OUTPUT_URI_PREFIX";
    createTrainingPipelineCustomTrainingManagedDatasetSample(
        project,
        displayName,
        modelDisplayName,
        datasetId,
        annotationSchemaUri,
        trainingContainerSpecImageUri,
        modelContainerSpecImageUri,
        baseOutputUriPrefix);
  }

  static void createTrainingPipelineCustomTrainingManagedDatasetSample(
      String project,
      String displayName,
      String modelDisplayName,
      String datasetId,
      String annotationSchemaUri,
      String trainingContainerSpecImageUri,
      String modelContainerSpecImageUri,
      String baseOutputUriPrefix)
      throws IOException {
    PipelineServiceSettings settings =
        PipelineServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();
    String location = "us-central1";

    // 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 client = PipelineServiceClient.create(settings)) {
      JsonArray jsonArgs = new JsonArray();
      jsonArgs.add("--model-dir=$(AIP_MODEL_DIR)");
      // training_task_inputs
      JsonObject jsonTrainingContainerSpec = new JsonObject();
      jsonTrainingContainerSpec.addProperty("imageUri", trainingContainerSpecImageUri);
      // AIP_MODEL_DIR is set by the service according to baseOutputDirectory.
      jsonTrainingContainerSpec.add("args", jsonArgs);

      JsonObject jsonMachineSpec = new JsonObject();
      jsonMachineSpec.addProperty("machineType", "n1-standard-8");

      JsonObject jsonTrainingWorkerPoolSpec = new JsonObject();
      jsonTrainingWorkerPoolSpec.addProperty("replicaCount", 1);
      jsonTrainingWorkerPoolSpec.add("machineSpec", jsonMachineSpec);
      jsonTrainingWorkerPoolSpec.add("containerSpec", jsonTrainingContainerSpec);

      JsonArray jsonWorkerPoolSpecs = new JsonArray();
      jsonWorkerPoolSpecs.add(jsonTrainingWorkerPoolSpec);

      JsonObject jsonBaseOutputDirectory = new JsonObject();
      jsonBaseOutputDirectory.addProperty("outputUriPrefix", baseOutputUriPrefix);

      JsonObject jsonTrainingTaskInputs = new JsonObject();
      jsonTrainingTaskInputs.add("workerPoolSpecs", jsonWorkerPoolSpecs);
      jsonTrainingTaskInputs.add("baseOutputDirectory", jsonBaseOutputDirectory);

      Value.Builder trainingTaskInputsBuilder = Value.newBuilder();
      JsonFormat.parser().merge(jsonTrainingTaskInputs.toString(), trainingTaskInputsBuilder);
      Value trainingTaskInputs = trainingTaskInputsBuilder.build();
      // model_to_upload
      ModelContainerSpec modelContainerSpec =
          ModelContainerSpec.newBuilder().setImageUri(modelContainerSpecImageUri).build();
      Model model =
          Model.newBuilder()
              .setDisplayName(modelDisplayName)
              .setContainerSpec(modelContainerSpec)
              .build();
      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(baseOutputUriPrefix).build();

      // input_data_config
      InputDataConfig inputDataConfig =
          InputDataConfig.newBuilder()
              .setDatasetId(datasetId)
              .setAnnotationSchemaUri(annotationSchemaUri)
              .setGcsDestination(gcsDestination)
              .build();

      // training_task_definition
      String customTaskDefinition =
          "gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml";

      TrainingPipeline trainingPipeline =
          TrainingPipeline.newBuilder()
              .setDisplayName(displayName)
              .setInputDataConfig(inputDataConfig)
              .setTrainingTaskDefinition(customTaskDefinition)
              .setTrainingTaskInputs(trainingTaskInputs)
              .setModelToUpload(model)
              .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());
    }
  }
}

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_custom_training_managed_dataset_sample(
    project: str,
    display_name: str,
    model_display_name: str,
    dataset_id: str,
    annotation_schema_uri: str,
    training_container_spec_image_uri: str,
    model_container_spec_image_uri: str,
    base_output_uri_prefix: 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)

    # input_data_config
    input_data_config = {
        "dataset_id": dataset_id,
        "annotation_schema_uri": annotation_schema_uri,
        "gcs_destination": {"output_uri_prefix": base_output_uri_prefix},
    }

    # training_task_definition
    custom_task_definition = "gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml"

    # training_task_inputs
    training_container_spec = {
        "imageUri": training_container_spec_image_uri,
        # AIP_MODEL_DIR is set by the service according to baseOutputDirectory.
        "args": ["--model-dir=$(AIP_MODEL_DIR)"],
    }

    training_worker_pool_spec = {
        "replicaCount": 1,
        "machineSpec": {"machineType": "n1-standard-8"},
        "containerSpec": training_container_spec,
    }

    training_task_inputs_dict = {
        "workerPoolSpecs": [training_worker_pool_spec],
        "baseOutputDirectory": {"outputUriPrefix": base_output_uri_prefix},
    }

    training_task_inputs = json_format.ParseDict(training_task_inputs_dict, Value())

    # model_to_upload
    model_container_spec = {
        "image_uri": model_container_spec_image_uri,
        "command": [],
        "args": [],
    }

    model = {"display_name": model_display_name, "container_spec": model_container_spec}

    training_pipeline = {
        "display_name": display_name,
        "input_data_config": input_data_config,
        "training_task_definition": custom_task_definition,
        "training_task_inputs": training_task_inputs,
        "model_to_upload": model,
    }
    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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