Delete a dataset

Deletes a dataset using the delete_dataset method.

Code sample

Go

Before trying this sample, follow the Go setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Go API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import (
	"context"
	"fmt"
	"io"

	aiplatform "cloud.google.com/go/aiplatform/apiv1"
	aiplatformpb "cloud.google.com/go/aiplatform/apiv1/aiplatformpb"
	"google.golang.org/api/option"
)

func deleteDataset(w io.Writer, projectID, location, datasetID string) error {
	// projectID := "my-project"
	// location := "us-central1"
	// datasetID := "my-dataset"

	apiEndpoint := fmt.Sprintf("%s-aiplatform.googleapis.com:443", location)
	clientOption := option.WithEndpoint(apiEndpoint)

	ctx := context.Background()
	aiplatformService, err := aiplatform.NewDatasetClient(ctx, clientOption)
	if err != nil {
		return err
	}
	defer aiplatformService.Close()

	req := &aiplatformpb.DeleteDatasetRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/datasets/%s",
			projectID, location, datasetID),
	}

	op, err := aiplatformService.DeleteDataset(ctx, req)
	if err != nil {
		return err
	}

	err = op.Wait(ctx)
	if err != nil {
		return ctx.Err()
	}

	fmt.Fprintf(w, "Deleted dataset: %s\n", datasetID)
	return nil
}

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DatasetName;
import com.google.cloud.aiplatform.v1.DatasetServiceClient;
import com.google.cloud.aiplatform.v1.DatasetServiceSettings;
import com.google.cloud.aiplatform.v1.DeleteOperationMetadata;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class DeleteDatasetSample {

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

  static void deleteDatasetSample(String project, String datasetId)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    DatasetServiceSettings datasetServiceSettings =
        DatasetServiceSettings.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 (DatasetServiceClient datasetServiceClient =
        DatasetServiceClient.create(datasetServiceSettings)) {
      String location = "us-central1";
      DatasetName datasetName = DatasetName.of(project, location, datasetId);

      OperationFuture<Empty, DeleteOperationMetadata> operationFuture =
          datasetServiceClient.deleteDatasetAsync(datasetName);
      System.out.format("Operation name: %s\n", operationFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      operationFuture.get(300, TimeUnit.SECONDS);

      System.out.format("Deleted Dataset.");
    }
  }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const datasetId = 'YOUR_DATASET_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Dataset Service Client library
const {DatasetServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const datasetServiceClient = new DatasetServiceClient(clientOptions);

async function deleteDataset() {
  // Configure the resource
  const name = datasetServiceClient.datasetPath(project, location, datasetId);
  const request = {name};

  // Delete Dataset Request
  const [response] = await datasetServiceClient.deleteDataset(request);
  console.log(`Long running operation: ${response.name}`);

  // Wait for operation to complete
  await response.promise();
  const result = response.result;

  console.log('Delete dataset response:\n', result);
}
deleteDataset();

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from google.cloud import aiplatform


def delete_dataset_sample(
    project: str,
    dataset_id: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    # 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.DatasetServiceClient(client_options=client_options)
    name = client.dataset_path(project=project, location=location, dataset=dataset_id)
    response = client.delete_dataset(name=name)
    print("Long running operation:", response.operation.name)
    delete_dataset_response = response.result(timeout=timeout)
    print("delete_dataset_response:", delete_dataset_response)

What's next

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