Crea una canalización de entrenamiento para un conjunto de datos administrado de entrenamiento personalizado

Crea una canalización de entrenamiento para un conjunto de datos administrado de entrenamiento personalizado mediante el método create_training_pipeline.

Muestra de código

Java

Antes de probar este ejemplo, sigue las instrucciones de configuración para Java incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Java.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local.

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

Antes de probar este ejemplo, sigue las instrucciones de configuración para Python incluidas en la guía de inicio rápido de Vertex AI sobre cómo usar bibliotecas cliente. Para obtener más información, consulta la documentación de referencia de la API de Vertex AI Python.

Para autenticarte en Vertex AI, configura las credenciales predeterminadas de la aplicación. Si deseas obtener más información, consulta Configura la autenticación para un entorno de desarrollo local.

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)

¿Qué sigue?

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