Obtenir des prédictions par lot avec une entrée Cloud Storage

Crée un job de prédiction par lot avec un fichier Cloud Storage en entrée.

En savoir plus

Pour obtenir une documentation détaillée incluant cet exemple de code, consultez les articles suivants :

Exemple de code

Java

Pour vous authentifier auprès d'AutoML Tables, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local. 

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1beta1.BatchPredictInputConfig;
import com.google.cloud.automl.v1beta1.BatchPredictOutputConfig;
import com.google.cloud.automl.v1beta1.BatchPredictRequest;
import com.google.cloud.automl.v1beta1.BatchPredictResult;
import com.google.cloud.automl.v1beta1.GcsDestination;
import com.google.cloud.automl.v1beta1.GcsSource;
import com.google.cloud.automl.v1beta1.ModelName;
import com.google.cloud.automl.v1beta1.OperationMetadata;
import com.google.cloud.automl.v1beta1.PredictionServiceClient;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

abstract class BatchPredict {

  static void batchPredict() throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    String inputUri = "gs://YOUR_BUCKET_ID/path_to_your_input_csv_or_jsonl";
    String outputUri = "gs://YOUR_BUCKET_ID/path_to_save_results/";
    batchPredict(projectId, modelId, inputUri, outputUri);
  }

  static void batchPredict(String projectId, String modelId, String inputUri, String outputUri)
      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 (PredictionServiceClient client = PredictionServiceClient.create()) {
      // Get the full path of the model.
      ModelName name = ModelName.of(projectId, "us-central1", modelId);

      // Configure the source of the file from a GCS bucket
      GcsSource gcsSource = GcsSource.newBuilder().addInputUris(inputUri).build();
      BatchPredictInputConfig inputConfig =
          BatchPredictInputConfig.newBuilder().setGcsSource(gcsSource).build();

      // Configure where to store the output in a GCS bucket
      GcsDestination gcsDestination =
          GcsDestination.newBuilder().setOutputUriPrefix(outputUri).build();
      BatchPredictOutputConfig outputConfig =
          BatchPredictOutputConfig.newBuilder().setGcsDestination(gcsDestination).build();

      // Build the request that will be sent to the API
      BatchPredictRequest request =
          BatchPredictRequest.newBuilder()
              .setName(name.toString())
              .setInputConfig(inputConfig)
              .setOutputConfig(outputConfig)
              .build();

      // Start an asynchronous request
      OperationFuture<BatchPredictResult, OperationMetadata> future =
          client.batchPredictAsync(request);

      System.out.println("Waiting for operation to complete...");
      future.get();
      System.out.println("Batch Prediction results saved to specified Cloud Storage bucket.");
    }
  }
}

Node.js

Pour vous authentifier auprès d'AutoML Tables, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local. 


/**
 * Demonstrates using the AutoML client to request prediction from
 * automl tables using GCS.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const modelId = '[MODEL_ID]' e.g., "TBL4704590352927948800";
// const inputUri = '[GCS_PATH]' e.g., "gs://<bucket-name>/<csv file>",
// `The Google Cloud Storage URI containing the inputs`;
// const outputUriPrefix = '[GCS_PATH]'
// e.g., "gs://<bucket-name>/<folder-name>",
// `The destination Google Cloud Storage URI for storing outputs`;

const automl = require('@google-cloud/automl');

// Create client for prediction service.
const automlClient = new automl.v1beta1.PredictionServiceClient();

// Get the full path of the model.
const modelFullId = automlClient.modelPath(projectId, computeRegion, modelId);

async function batchPredict() {
  // Construct request
  const inputConfig = {
    gcsSource: {
      inputUris: [inputUri],
    },
  };

  // Get the Google Cloud Storage output URI.
  const outputConfig = {
    gcsDestination: {
      outputUriPrefix: outputUriPrefix,
    },
  };

  const [, operation] = await automlClient.batchPredict({
    name: modelFullId,
    inputConfig: inputConfig,
    outputConfig: outputConfig,
  });

  // Get the latest state of long-running operation.
  console.log(`Operation name: ${operation.name}`);
  return operation;
}

batchPredict();

Python

Pour vous authentifier auprès d'AutoML Tables, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local. 

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'
# gcs_input_uri = 'gs://YOUR_BUCKET_ID/path_to_your_input_csv'
# gcs_output_uri = 'gs://YOUR_BUCKET_ID/path_to_save_results/'
# params = {}

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# Query model
response = client.batch_predict(
    gcs_input_uris=gcs_input_uri,
    gcs_output_uri_prefix=gcs_output_uri,
    model_display_name=model_display_name,
    params=params,
)
print("Making batch prediction... ")
# `response` is a async operation descriptor,
# you can register a callback for the operation to complete via `add_done_callback`:
# def callback(operation_future):
#   result = operation_future.result()
# response.add_done_callback(callback)
#
# or block the thread polling for the operation's results:
response.result()

print(f"Batch prediction complete.\n{response.metadata}")

Étapes suivantes

Pour rechercher et filtrer des exemples de code pour d'autres produits Google Cloud, consultez l'exemple de navigateur Google Cloud.