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DatasetServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1beta1.services.dataset_service.transports.base.DatasetServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
The service that handles the CRUD of Vertex AI Dataset and its child resources.
Inheritance
builtins.object > DatasetServiceAsyncClientProperties
transport
Returns the transport used by the client instance.
Type | Description |
DatasetServiceTransport | The transport used by the client instance. |
Methods
DatasetServiceAsyncClient
DatasetServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1beta1.services.dataset_service.transports.base.DatasetServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
Instantiates the dataset service client.
Name | Description |
credentials |
Optional[google.auth.credentials.Credentials]
The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. |
transport |
Union[str, `.DatasetServiceTransport`]
The transport to use. If set to None, a transport is chosen automatically. |
client_options |
ClientOptions
Custom options for the client. It won't take effect if a |
Type | Description |
google.auth.exceptions.MutualTlsChannelError | If mutual TLS transport creation failed for any reason. |
annotation_path
annotation_path(
project: str, location: str, dataset: str, data_item: str, annotation: str
)
Returns a fully-qualified annotation string.
annotation_spec_path
annotation_spec_path(
project: str, location: str, dataset: str, annotation_spec: str
)
Returns a fully-qualified annotation_spec string.
common_billing_account_path
common_billing_account_path(billing_account: str)
Returns a fully-qualified billing_account string.
common_folder_path
common_folder_path(folder: str)
Returns a fully-qualified folder string.
common_location_path
common_location_path(project: str, location: str)
Returns a fully-qualified location string.
common_organization_path
common_organization_path(organization: str)
Returns a fully-qualified organization string.
common_project_path
common_project_path(project: str)
Returns a fully-qualified project string.
create_dataset
create_dataset(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.CreateDatasetRequest, dict]] = None, *, parent: Optional[str] = None, dataset: Optional[google.cloud.aiplatform_v1beta1.types.dataset.Dataset] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Creates a Dataset.
from google.cloud import aiplatform_v1beta1
async def sample_create_dataset():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
dataset = aiplatform_v1beta1.Dataset()
dataset.display_name = "display_name_value"
dataset.metadata_schema_uri = "metadata_schema_uri_value"
dataset.metadata.null_value = "NULL_VALUE"
request = aiplatform_v1beta1.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)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.CreateDatasetRequest, dict]
The request object. Request message for DatasetService.CreateDataset. |
parent |
`str`
Required. The resource name of the Location to create the Dataset in. Format: |
dataset |
Dataset
Required. The Dataset to create. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be Dataset A collection of DataItems and Annotations on them. |
data_item_path
data_item_path(project: str, location: str, dataset: str, data_item: str)
Returns a fully-qualified data_item string.
dataset_path
dataset_path(project: str, location: str, dataset: str)
Returns a fully-qualified dataset string.
delete_dataset
delete_dataset(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.DeleteDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Deletes a Dataset.
from google.cloud import aiplatform_v1beta1
async def sample_delete_dataset():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.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)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.DeleteDatasetRequest, dict]
The request object. Request message for DatasetService.DeleteDataset. |
name |
`str`
Required. The resource name of the Dataset to delete. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}. |
export_data
export_data(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.ExportDataRequest, dict]] = None, *, name: Optional[str] = None, export_config: Optional[google.cloud.aiplatform_v1beta1.types.dataset.ExportDataConfig] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Exports data from a Dataset.
from google.cloud import aiplatform_v1beta1
async def sample_export_data():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
export_config = aiplatform_v1beta1.ExportDataConfig()
export_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"
request = aiplatform_v1beta1.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)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ExportDataRequest, dict]
The request object. Request message for DatasetService.ExportData. |
name |
`str`
Required. The name of the Dataset resource. Format: |
export_config |
ExportDataConfig
Required. The desired output location. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be ExportDataResponse Response message for DatasetService.ExportData. |
from_service_account_file
from_service_account_file(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
DatasetServiceAsyncClient | The constructed client. |
from_service_account_info
from_service_account_info(info: dict, *args, **kwargs)
Creates an instance of this client using the provided credentials info.
Name | Description |
info |
dict
The service account private key info. |
Type | Description |
DatasetServiceAsyncClient | The constructed client. |
from_service_account_json
from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
DatasetServiceAsyncClient | The constructed client. |
get_annotation_spec
get_annotation_spec(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.GetAnnotationSpecRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Gets an AnnotationSpec.
from google.cloud import aiplatform_v1beta1
async def sample_get_annotation_spec():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetAnnotationSpecRequest(
name="name_value",
)
# Make the request
response = await client.get_annotation_spec(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetAnnotationSpecRequest, dict]
The request object. Request message for DatasetService.GetAnnotationSpec. |
name |
`str`
Required. The name of the AnnotationSpec resource. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.cloud.aiplatform_v1beta1.types.AnnotationSpec | Identifies a concept with which DataItems may be annotated with. |
get_dataset
get_dataset(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.GetDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Gets a Dataset.
from google.cloud import aiplatform_v1beta1
async def sample_get_dataset():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetDatasetRequest(
name="name_value",
)
# Make the request
response = await client.get_dataset(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetDatasetRequest, dict]
The request object. Request message for DatasetService.GetDataset. |
name |
`str`
Required. The name of the Dataset resource. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.cloud.aiplatform_v1beta1.types.Dataset | A collection of DataItems and Annotations on them. |
get_mtls_endpoint_and_cert_source
get_mtls_endpoint_and_cert_source(
client_options: Optional[google.api_core.client_options.ClientOptions] = None,
)
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 variabel 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.
Name | Description |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. Only the |
Type | Description |
google.auth.exceptions.MutualTLSChannelError | If any errors happen. |
Type | Description |
Tuple[str, Callable[[], Tuple[bytes, bytes]]] | returns the API endpoint and the client cert source to use. |
get_transport_class
get_transport_class()
Returns an appropriate transport class.
import_data
import_data(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.ImportDataRequest, dict]] = None, *, name: Optional[str] = None, import_configs: Optional[Sequence[google.cloud.aiplatform_v1beta1.types.dataset.ImportDataConfig]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Imports data into a Dataset.
from google.cloud import aiplatform_v1beta1
async def sample_import_data():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
import_configs = aiplatform_v1beta1.ImportDataConfig()
import_configs.gcs_source.uris = ['uris_value_1', 'uris_value_2']
import_configs.import_schema_uri = "import_schema_uri_value"
request = aiplatform_v1beta1.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)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ImportDataRequest, dict]
The request object. Request message for DatasetService.ImportData. |
name |
`str`
Required. The name of the Dataset resource. Format: |
import_configs |
:class:`Sequence[google.cloud.aiplatform_v1beta1.types.ImportDataConfig]`
Required. The desired input locations. The contents of all input locations will be imported in one batch. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be ImportDataResponse Response message for DatasetService.ImportData. |
list_annotations
list_annotations(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.ListAnnotationsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists Annotations belongs to a dataitem
from google.cloud import aiplatform_v1beta1
async def sample_list_annotations():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.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)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListAnnotationsRequest, dict]
The request object. Request message for DatasetService.ListAnnotations. |
parent |
`str`
Required. The resource name of the DataItem to list Annotations from. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.cloud.aiplatform_v1beta1.services.dataset_service.pagers.ListAnnotationsAsyncPager | Response message for DatasetService.ListAnnotations. Iterating over this object will yield results and resolve additional pages automatically. |
list_data_items
list_data_items(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.ListDataItemsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists DataItems in a Dataset.
from google.cloud import aiplatform_v1beta1
async def sample_list_data_items():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.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)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListDataItemsRequest, dict]
The request object. Request message for DatasetService.ListDataItems. |
parent |
`str`
Required. The resource name of the Dataset to list DataItems from. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.cloud.aiplatform_v1beta1.services.dataset_service.pagers.ListDataItemsAsyncPager | Response message for DatasetService.ListDataItems. Iterating over this object will yield results and resolve additional pages automatically. |
list_datasets
list_datasets(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.ListDatasetsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists Datasets in a Location.
from google.cloud import aiplatform_v1beta1
async def sample_list_datasets():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.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)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListDatasetsRequest, dict]
The request object. Request message for DatasetService.ListDatasets. |
parent |
`str`
Required. The name of the Dataset's parent resource. Format: |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.cloud.aiplatform_v1beta1.services.dataset_service.pagers.ListDatasetsAsyncPager | Response message for DatasetService.ListDatasets. Iterating over this object will yield results and resolve additional pages automatically. |
parse_annotation_path
parse_annotation_path(path: str)
Parses a annotation path into its component segments.
parse_annotation_spec_path
parse_annotation_spec_path(path: str)
Parses a annotation_spec path into its component segments.
parse_common_billing_account_path
parse_common_billing_account_path(path: str)
Parse a billing_account path into its component segments.
parse_common_folder_path
parse_common_folder_path(path: str)
Parse a folder path into its component segments.
parse_common_location_path
parse_common_location_path(path: str)
Parse a location path into its component segments.
parse_common_organization_path
parse_common_organization_path(path: str)
Parse a organization path into its component segments.
parse_common_project_path
parse_common_project_path(path: str)
Parse a project path into its component segments.
parse_data_item_path
parse_data_item_path(path: str)
Parses a data_item path into its component segments.
parse_dataset_path
parse_dataset_path(path: str)
Parses a dataset path into its component segments.
update_dataset
update_dataset(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.dataset_service.UpdateDatasetRequest, dict]] = None, *, dataset: Optional[google.cloud.aiplatform_v1beta1.types.dataset.Dataset] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Updates a Dataset.
from google.cloud import aiplatform_v1beta1
async def sample_update_dataset():
# Create a client
client = aiplatform_v1beta1.DatasetServiceAsyncClient()
# Initialize request argument(s)
dataset = aiplatform_v1beta1.Dataset()
dataset.display_name = "display_name_value"
dataset.metadata_schema_uri = "metadata_schema_uri_value"
dataset.metadata.null_value = "NULL_VALUE"
request = aiplatform_v1beta1.UpdateDatasetRequest(
dataset=dataset,
)
# Make the request
response = await client.update_dataset(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.UpdateDatasetRequest, dict]
The request object. Request message for DatasetService.UpdateDataset. |
dataset |
Dataset
Required. The Dataset which replaces the resource on the server. This corresponds to the |
update_mask |
`google.protobuf.field_mask_pb2.FieldMask`
Required. The update mask applies to the resource. For the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
google.cloud.aiplatform_v1beta1.types.Dataset | A collection of DataItems and Annotations on them. |