Crea una canalización de entrenamiento para la detección de objetos en imágenes

Crea una canalización de entrenamiento para la detección de objetos de imagen mediante el método create_training_pipeline.

Explora más

Para obtener documentación en la que se incluye esta muestra de código, consulta lo siguiente:

• Entrena un modelo de detección de objetos de imagen

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.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.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageObjectDetectionInputs;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageObjectDetectionInputs.ModelType;
import com.google.rpc.Status;
import java.io.IOException;

public class CreateTrainingPipelineImageObjectDetectionSample {

  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";
    createTrainingPipelineImageObjectDetectionSample(
        project, trainingPipelineDisplayName, datasetId, modelDisplayName);
  }

  static void createTrainingPipelineImageObjectDetectionSample(
      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_image_object_detection_1.0.0.yaml";
      LocationName locationName = LocationName.of(project, location);

      AutoMlImageObjectDetectionInputs autoMlImageObjectDetectionInputs =
          AutoMlImageObjectDetectionInputs.newBuilder()
              .setModelType(ModelType.CLOUD_HIGH_ACCURACY_1)
              .setBudgetMilliNodeHours(20000)
              .setDisableEarlyStopping(false)
              .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(autoMlImageObjectDetectionInputs))
              .setInputDataConfig(trainingInputDataConfig)
              .setModelToUpload(model)
              .build();

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

      System.out.println("Create Training Pipeline Image Object Detection Response");
      System.out.format("Name: %s\n", trainingPipelineResponse.getName());
      System.out.format("Display Name: %s\n", trainingPipelineResponse.getDisplayName());

      System.out.format(
          "Training Task Definition %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
      System.out.format(
          "Training Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
      System.out.format(
          "Training Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());
      System.out.format("State: %s\n", trainingPipelineResponse.getState());

      System.out.format("Create Time: %s\n", trainingPipelineResponse.getCreateTime());
      System.out.format("StartTime %s\n", trainingPipelineResponse.getStartTime());
      System.out.format("End Time: %s\n", trainingPipelineResponse.getEndTime());
      System.out.format("Update Time: %s\n", trainingPipelineResponse.getUpdateTime());
      System.out.format("Labels: %s\n", trainingPipelineResponse.getLabelsMap());

      InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();
      System.out.println("Input Data Config");
      System.out.format("Dataset Id: %s", inputDataConfig.getDatasetId());
      System.out.format("Annotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());

      FractionSplit fractionSplit = inputDataConfig.getFractionSplit();
      System.out.println("Fraction Split");
      System.out.format("Training Fraction: %s\n", fractionSplit.getTrainingFraction());
      System.out.format("Validation Fraction: %s\n", fractionSplit.getValidationFraction());
      System.out.format("Test Fraction: %s\n", fractionSplit.getTestFraction());

      FilterSplit filterSplit = inputDataConfig.getFilterSplit();
      System.out.println("Filter Split");
      System.out.format("Training Filter: %s\n", filterSplit.getTrainingFilter());
      System.out.format("Validation Filter: %s\n", filterSplit.getValidationFilter());
      System.out.format("Test Filter: %s\n", filterSplit.getTestFilter());

      PredefinedSplit predefinedSplit = inputDataConfig.getPredefinedSplit();
      System.out.println("Predefined Split");
      System.out.format("Key: %s\n", predefinedSplit.getKey());

      TimestampSplit timestampSplit = inputDataConfig.getTimestampSplit();
      System.out.println("Timestamp Split");
      System.out.format("Training Fraction: %s\n", timestampSplit.getTrainingFraction());
      System.out.format("Validation Fraction: %s\n", timestampSplit.getValidationFraction());
      System.out.format("Test Fraction: %s\n", timestampSplit.getTestFraction());
      System.out.format("Key: %s\n", timestampSplit.getKey());

      Model modelResponse = trainingPipelineResponse.getModelToUpload();
      System.out.println("Model To Upload");
      System.out.format("Name: %s\n", modelResponse.getName());
      System.out.format("Display Name: %s\n", modelResponse.getDisplayName());
      System.out.format("Description: %s\n", modelResponse.getDescription());

      System.out.format("Metadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("Metadata: %s\n", modelResponse.getMetadata());
      System.out.format("Training Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("Artifact Uri: %s\n", modelResponse.getArtifactUri());

      System.out.format(
          "Supported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList());
      System.out.format(
          "Supported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList());
      System.out.format(
          "Supported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList());

      System.out.format("Create Time: %s\n", modelResponse.getCreateTime());
      System.out.format("Update Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("Labels: %sn\n", modelResponse.getLabelsMap());

      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
      System.out.println("Predict Schemata");
      System.out.format("Instance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format("Parameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format("Prediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {
        System.out.println("Supported Export Format");
        System.out.format("Id: %s\n", exportFormat.getId());
      }

      ModelContainerSpec modelContainerSpec = modelResponse.getContainerSpec();
      System.out.println("Container Spec");
      System.out.format("Image Uri: %s\n", modelContainerSpec.getImageUri());
      System.out.format("Command: %s\n", modelContainerSpec.getCommandList());
      System.out.format("Args: %s\n", modelContainerSpec.getArgsList());
      System.out.format("Predict Route: %s\n", modelContainerSpec.getPredictRoute());
      System.out.format("Health Route: %s\n", modelContainerSpec.getHealthRoute());

      for (EnvVar envVar : modelContainerSpec.getEnvList()) {
        System.out.println("Env");
        System.out.format("Name: %s\n", envVar.getName());
        System.out.format("Value: %s\n", envVar.getValue());
      }

      for (Port port : modelContainerSpec.getPortsList()) {
        System.out.println("Port");
        System.out.format("Container Port: %s\n", port.getContainerPort());
      }

      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
        System.out.println("Deployed Model");
        System.out.format("Endpoint: %s\n", deployedModelRef.getEndpoint());
        System.out.format("Deployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
      }

      Status status = trainingPipelineResponse.getError();
      System.out.println("Error");
      System.out.format("Code: %s\n", status.getCode());
      System.out.format("Message: %s\n", status.getMessage());
    }
  }
}

Node.js

Antes de probar este ejemplo, sigue las instrucciones de configuración para Node.js 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 Node.js.

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.

/**
 * 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;
const ModelType = definition.AutoMlImageObjectDetectionInputs.ModelType;

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

  const trainingTaskInputsObj =
    new definition.AutoMlImageObjectDetectionInputs({
      disableEarlyStopping: false,
      modelType: ModelType.CLOUD_1,
      budgetMilliNodeHours: 20000,
    });

  const trainingTaskInputs = trainingTaskInputsObj.toValue();
  const modelToUpload = {displayName: modelDisplayName};
  const inputDataConfig = {datasetId: datasetId};
  const trainingPipeline = {
    displayName: trainingPipelineDisplayName,
    trainingTaskDefinition:
      'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_object_detection_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 image object detection response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createTrainingPipelineImageObjectDetection();

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.cloud.aiplatform.gapic.schema import trainingjob


def create_training_pipeline_image_object_detection_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.AutoMlImageObjectDetectionInputs(
        model_type="CLOUD_HIGH_ACCURACY_1",
        budget_milli_node_hours=20000,
        disable_early_stopping=False,
    ).to_value()

    training_pipeline = {
        "display_name": display_name,
        "training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_object_detection_1.0.0.yaml",
        "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)

¿Qué sigue?

Para buscar y filtrar muestras de código para otros productos de Google Cloud, consulta el navegador de muestra de Google Cloud.