Crea un modelo

Crea un modelo.

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

Crea y administra modelos

Muestra de código

Go

Para obtener información sobre cómo instalar y usar la biblioteca cliente de AutoML Translation, consulta las bibliotecas cliente de AutoML Translation. Si quieres obtener más información, consulta la documentación de referencia de la API de AutoML Translation Go.

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

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// translateCreateModel creates a model for translate.
func translateCreateModel(w io.Writer, projectID string, location string, datasetID string, modelName string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetID := "TRL123456789..."
	// modelName := "model_display_name"

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer client.Close()

	req := &automlpb.CreateModelRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		Model: &automlpb.Model{
			DisplayName: modelName,
			DatasetId:   datasetID,
			ModelMetadata: &automlpb.Model_TranslationModelMetadata{
				TranslationModelMetadata: &automlpb.TranslationModelMetadata{},
			},
		},
	}

	op, err := client.CreateModel(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateModel: %w", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())
	fmt.Fprintf(w, "Training started...\n")

	return nil
}

Java

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.LocationName;
import com.google.cloud.automl.v1.Model;
import com.google.cloud.automl.v1.OperationMetadata;
import com.google.cloud.automl.v1.TranslationModelMetadata;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class TranslateCreateModel {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String datasetId = "YOUR_DATASET_ID";
    String displayName = "YOUR_DATASET_NAME";
    createModel(projectId, datasetId, displayName);
  }

  // Create a model
  static void createModel(String projectId, String datasetId, String displayName)
      throws IOException, ExecutionException, InterruptedException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");
      TranslationModelMetadata translationModelMetadata =
          TranslationModelMetadata.newBuilder().build();
      Model model =
          Model.newBuilder()
              .setDisplayName(displayName)
              .setDatasetId(datasetId)
              .setTranslationModelMetadata(translationModelMetadata)
              .build();

      // Create a model with the model metadata in the region.
      OperationFuture<Model, OperationMetadata> future =
          client.createModelAsync(projectLocation, model);
      // OperationFuture.get() will block until the model is created, which may take several hours.
      // You can use OperationFuture.getInitialFuture to get a future representing the initial
      // response to the request, which contains information while the operation is in progress.
      System.out.format("Training operation name: %s\n", future.getInitialFuture().get().getName());
      System.out.println("Training started...");
    }
  }
}

Node.js

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const dataset_id = 'YOUR_DATASET_ID';
// const displayName = 'YOUR_DISPLAY_NAME';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function createModel() {
  // Construct request
  const request = {
    parent: client.locationPath(projectId, location),
    model: {
      displayName: displayName,
      datasetId: datasetId,
      translationModelMetadata: {},
    },
  };

  // Don't wait for the LRO
  const [operation] = await client.createModel(request);
  console.log('Training started...');
  console.log(`Training operation name: ${operation.name}`);
}

createModel();

Python

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# dataset_id = "YOUR_DATASET_ID"
# display_name = "YOUR_MODEL_NAME"

client = automl.AutoMlClient()

# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"
translation_model_metadata = automl.TranslationModelMetadata()
model = automl.Model(
    display_name=display_name,
    dataset_id=dataset_id,
    translation_model_metadata=translation_model_metadata,
)

# Create a model with the model metadata in the region.
response = client.create_model(parent=project_location, model=model)

print(f"Training operation name: {response.operation.name}")
print("Training started...")

¿Qué sigue?

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