Class DatasetServiceAsyncClient (1.70.0)

The service that manages Vertex AI Dataset and its child resources.

Return the API endpoint used by the client instance.

Returns the transport used by the client instance.

Return the universe domain used by the client instance.

Instantiates the dataset service async client.

Returns a fully-qualified annotation string.

Returns a fully-qualified annotation_spec string.

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Returns a fully-qualified billing_account string.

Returns a fully-qualified folder string.

Returns a fully-qualified location string.

Returns a fully-qualified organization string.

Returns a fully-qualified project string.

Creates a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_dataset():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    dataset = aiplatform_v1.Dataset()
    dataset.display_name = "display_name_value"
    dataset.metadata_schema_uri = "metadata_schema_uri_value"
    dataset.metadata.null_value = "NULL_VALUE"

    request = aiplatform_v1.CreateDatasetRequest(
        parent="parent_value",
        dataset=dataset,
    )

    # Make the request
    operation = client.create_dataset(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Create a version from a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_create_dataset_version():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    dataset_version = aiplatform_v1.DatasetVersion()
    dataset_version.metadata.null_value = "NULL_VALUE"

    request = aiplatform_v1.CreateDatasetVersionRequest(
        parent="parent_value",
        dataset_version=dataset_version,
    )

    # Make the request
    operation = client.create_dataset_version(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Returns a fully-qualified data_item string.

Returns a fully-qualified dataset string.

Returns a fully-qualified dataset_version string.

Deletes a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_dataset():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteDatasetRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_dataset(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a Dataset version.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_dataset_version():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteDatasetVersionRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_dataset_version(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Deletes a SavedQuery.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_delete_saved_query():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DeleteSavedQueryRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_saved_query(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Exports data from a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_export_data():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    export_config = aiplatform_v1.ExportDataConfig()
    export_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"

    request = aiplatform_v1.ExportDataRequest(
        name="name_value",
        export_config=export_config,
    )

    # Make the request
    operation = client.export_data(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Creates an instance of this client using the provided credentials file.

Creates an instance of this client using the provided credentials info.

Creates an instance of this client using the provided credentials file.

Gets an AnnotationSpec.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_annotation_spec():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetAnnotationSpecRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_annotation_spec(request=request)

    # Handle the response
    print(response)

Gets a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_dataset():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetDatasetRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_dataset(request=request)

    # Handle the response
    print(response)

Gets a Dataset version.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_get_dataset_version():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.GetDatasetVersionRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_dataset_version(request=request)

    # Handle the response
    print(response)

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Gets information about a location.

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variable is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Gets the latest state of a long-running operation.

Returns an appropriate transport class.

Imports data into a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_import_data():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    import_configs = aiplatform_v1.ImportDataConfig()
    import_configs.gcs_source.uris = ['uris_value1', 'uris_value2']
    import_configs.import_schema_uri = "import_schema_uri_value"

    request = aiplatform_v1.ImportDataRequest(
        name="name_value",
        import_configs=import_configs,
    )

    # Make the request
    operation = client.import_data(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Lists Annotations belongs to a dataitem

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_annotations():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListAnnotationsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_annotations(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists DataItems in a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_data_items():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListDataItemsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_data_items(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists DatasetVersions in a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_dataset_versions():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListDatasetVersionsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_dataset_versions(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists Datasets in a Location.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_datasets():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListDatasetsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_datasets(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Lists information about the supported locations for this service.

Lists operations that match the specified filter in the request.

Lists SavedQueries in a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_list_saved_queries():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.ListSavedQueriesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_saved_queries(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parses a annotation path into its component segments.

Parses a annotation_spec path into its component segments.

Parse a billing_account path into its component segments.

Parse a folder path into its component segments.

Parse a location path into its component segments.

Parse a organization path into its component segments.

Parse a project path into its component segments.

Parses a data_item path into its component segments.

Parses a dataset path into its component segments.

Parses a dataset_version path into its component segments.

Parses a saved_query path into its component segments.

Restores a dataset version.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_restore_dataset_version():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.RestoreDatasetVersionRequest(
        name="name_value",
    )

    # Make the request
    operation = client.restore_dataset_version(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Returns a fully-qualified saved_query string.

Searches DataItems in a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_search_data_items():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.SearchDataItemsRequest(
        order_by_data_item="order_by_data_item_value",
        dataset="dataset_value",
    )

    # Make the request
    page_result = client.search_data_items(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Tests the specified IAM permissions against the IAM access control policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Updates a Dataset.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_dataset():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    dataset = aiplatform_v1.Dataset()
    dataset.display_name = "display_name_value"
    dataset.metadata_schema_uri = "metadata_schema_uri_value"
    dataset.metadata.null_value = "NULL_VALUE"

    request = aiplatform_v1.UpdateDatasetRequest(
        dataset=dataset,
    )

    # Make the request
    response = await client.update_dataset(request=request)

    # Handle the response
    print(response)

Updates a DatasetVersion.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

async def sample_update_dataset_version():
    # Create a client
    client = aiplatform_v1.DatasetServiceAsyncClient()

    # Initialize request argument(s)
    dataset_version = aiplatform_v1.DatasetVersion()
    dataset_version.metadata.null_value = "NULL_VALUE"

    request = aiplatform_v1.UpdateDatasetVersionRequest(
        dataset_version=dataset_version,
    )

    # Make the request
    response = await client.update_dataset_version(request=request)

    # Handle the response
    print(response)

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.