Bekerja dengan operasi yang berjalan lama

Beberapa operasi yang Anda minta berjalan lama, seperti pelatihan model, impor file ke set data, dan penghapusan model. Jenis permintaan ini akan menampilkan JSON dengan ID operasi yang dapat Anda gunakan untuk mendapatkan status operasi.

Misalnya, permintaan pelatihan model menampilkan JSON berikut:

{
  "name": "projects/PROJECT_ID/locations/us-central1/operations/IOD3074819451447672458",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-07-26T21:10:18.338846Z",
    "updateTime": "2019-07-26T21:10:18.338846Z",
    "createModelDetails": {}
  }
}

Dalam hal ini, ID operasinya adalah IOD3074819451447672458. Contoh berikut menunjukkan cara mendapatkan status operasi ini dengan ID ini.

REST

Sebelum menggunakan salah satu data permintaan, lakukan penggantian berikut:

  • project-id: project ID GCP Anda.
  • operation-id: ID operasi Anda. ID tersebut adalah elemen terakhir dari nama operasi Anda. Contoh:
    • nama operasi: projects/project-id/locations/location-id/operations/IOD5281059901324392598
    • ID Operasi: IOD5281059901324392598

Metode HTTP dan URL: 

GET https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID

Untuk mengirim permintaan Anda, pilih salah satu opsi berikut:

curl

Jalankan perintah berikut: 

curl -X GET \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "x-goog-user-project: project-id" \
     "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID"

PowerShell

Jalankan perintah berikut: 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://automl.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID" | Select-Object -Expand Content
Anda akan melihat output yang serupa dengan berikut ini dari operasi impor yang telah selesai: 
{
  "name": "projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2018-10-29T15:56:29.176485Z",
    "updateTime": "2018-10-29T16:10:41.326614Z",
    "importDataDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

Anda akan melihat output yang serupa dengan berikut ini dari operasi pembuatan model yang telah selesai:

{
  "name": "projects/PROJECT_ID/locations/us-central1/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-07-22T18:35:06.881193Z",
    "updateTime": "2019-07-22T19:58:44.972235Z",
    "createModelDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.Model",
    "name": "projects/PROJECT_ID/locations/us-central1/models/MODEL_ID"
  }
}

Go

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

import (
	"context"
	"fmt"
	"io"

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

// getOperationStatus gets an operation's status.
func getOperationStatus(w io.Writer, projectID string, location string, datasetID string, modelName string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetID := "ICN123456789..."
	// 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_ImageClassificationModelMetadata{
				ImageClassificationModelMetadata: &automlpb.ImageClassificationModelMetadata{
					TrainBudgetMilliNodeHours: 1000, // 1000 milli-node hours are 1 hour
				},
			},
		},
	}

	op, err := client.CreateModel(ctx, req)
	if err != nil {
		return err
	}
	fmt.Fprintf(w, "Name: %v\n", op.Name())

	// Wait for the longrunning operation complete.
	resp, err := op.Wait(ctx)
	if err != nil && !op.Done() {
		fmt.Println("failed to fetch operation status", err)
		return err
	}
	if err != nil && op.Done() {
		fmt.Println("operation completed with error", err)
		return err
	}
	fmt.Fprintf(w, "Response: %v\n", resp)

	return nil
}

Java

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

import com.google.cloud.automl.v1.AutoMlClient;
import com.google.longrunning.Operation;
import java.io.IOException;

class GetOperationStatus {

  static void getOperationStatus() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String operationFullId = "projects/[projectId]/locations/us-central1/operations/[operationId]";
    getOperationStatus(operationFullId);
  }

  // Get the status of an operation
  static void getOperationStatus(String operationFullId) throws IOException {
    // 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()) {
      // Get the latest state of a long-running operation.
      Operation operation = client.getOperationsClient().getOperation(operationFullId);

      // Display operation details.
      System.out.println("Operation details:");
      System.out.format("\tName: %s\n", operation.getName());
      System.out.format("\tMetadata Type Url: %s\n", operation.getMetadata().getTypeUrl());
      System.out.format("\tDone: %s\n", operation.getDone());
      if (operation.hasResponse()) {
        System.out.format("\tResponse Type Url: %s\n", operation.getResponse().getTypeUrl());
      }
      if (operation.hasError()) {
        System.out.println("\tResponse:");
        System.out.format("\t\tError code: %s\n", operation.getError().getCode());
        System.out.format("\t\tError message: %s\n", operation.getError().getMessage());
      }
    }
  }
}

Node.js

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

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

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

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

async function getOperationStatus() {
  // Construct request
  const request = {
    name: `projects/${projectId}/locations/${location}/operations/${operationId}`,
  };

  const [response] = await client.operationsClient.getOperation(request);

  console.log(`Name: ${response.name}`);
  console.log('Operation details:');
  console.log(`${response}`);
}

getOperationStatus();

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan untuk bahasa ini di halaman Library Klien.

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# operation_full_id = \
#     "projects/[projectId]/locations/us-central1/operations/[operationId]"

client = automl.AutoMlClient()
# Get the latest state of a long-running operation.
response = client._transport.operations_client.get_operation(operation_full_id)

print(f"Name: {response.name}")
print("Operation details:")
print(response)

Bahasa tambahan

C# : Ikuti Petunjuk penyiapan C# di halaman library klien, lalu kunjungi Dokumentasi referensi Deteksi Objek Vision AutoML untuk .NET.

PHP : Ikuti petunjuk penyiapan PHP di halaman library klien, lalu kunjungi dokumentasi referensi Deteksi Objek AutoML Vision untuk PHP.

Ruby: Ikuti Petunjuk penyiapan Ruby di halaman client libraries lalu kunjungi Dokumentasi referensi Deteksi Objek AutoML Vision untuk Ruby.