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FeaturestoreServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1beta1.services.featurestore_service.transports.base.FeaturestoreServiceTransport]] = None, 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 CRUD and List for resources for Featurestore.
Inheritance
builtins.object > FeaturestoreServiceClientProperties
transport
Returns the transport used by the client instance.
Type | Description |
FeaturestoreServiceTransport | The transport used by the client instance. |
Methods
FeaturestoreServiceClient
FeaturestoreServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1beta1.services.featurestore_service.transports.base.FeaturestoreServiceTransport]] = None, 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 featurestore 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, FeaturestoreServiceTransport]
The transport to use. If set to None, a transport is chosen automatically. |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. It won't take effect if a |
client_info |
google.api_core.gapic_v1.client_info.ClientInfo
The client info used to send a user-agent string along with API requests. If |
Type | Description |
google.auth.exceptions.MutualTLSChannelError | If mutual TLS transport creation failed for any reason. |
__exit__
__exit__(type, value, traceback)
Releases underlying transport's resources.
batch_create_features
batch_create_features(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.BatchCreateFeaturesRequest, dict]] = None, *, parent: Optional[str] = None, requests: Optional[Sequence[google.cloud.aiplatform_v1beta1.types.featurestore_service.CreateFeatureRequest]] = 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 batch of Features in a given EntityType.
from google.cloud import aiplatform_v1beta1
def sample_batch_create_features():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
requests = aiplatform_v1beta1.CreateFeatureRequest()
requests.parent = "parent_value"
requests.feature.value_type = "BYTES"
requests.feature_id = "feature_id_value"
request = aiplatform_v1beta1.BatchCreateFeaturesRequest(
parent="parent_value",
requests=requests,
)
# Make the request
operation = client.batch_create_features(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.BatchCreateFeaturesRequest, dict]
The request object. Request message for FeaturestoreService.BatchCreateFeatures. |
parent |
str
Required. The resource name of the EntityType to create the batch of Features under. Format: |
requests |
Sequence[google.cloud.aiplatform_v1beta1.types.CreateFeatureRequest]
Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType. 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.Operation | An object representing a long-running operation. The result type for the operation will be BatchCreateFeaturesResponse Response message for FeaturestoreService.BatchCreateFeatures. |
batch_read_feature_values
batch_read_feature_values(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.BatchReadFeatureValuesRequest, dict]] = None, *, featurestore: 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]] = ())
Batch reads Feature values from a Featurestore. This API enables batch reading Feature values, where each read instance in the batch may read Feature values of entities from one or more EntityTypes. Point-in-time correctness is guaranteed for Feature values of each read instance as of each instance's read timestamp.
from google.cloud import aiplatform_v1beta1
def sample_batch_read_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
csv_read_instances = aiplatform_v1beta1.CsvSource()
csv_read_instances.gcs_source.uris = ['uris_value_1', 'uris_value_2']
destination = aiplatform_v1beta1.FeatureValueDestination()
destination.bigquery_destination.output_uri = "output_uri_value"
entity_type_specs = aiplatform_v1beta1.EntityTypeSpec()
entity_type_specs.entity_type_id = "entity_type_id_value"
entity_type_specs.feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']
request = aiplatform_v1beta1.BatchReadFeatureValuesRequest(
csv_read_instances=csv_read_instances,
featurestore="featurestore_value",
destination=destination,
entity_type_specs=entity_type_specs,
)
# Make the request
operation = client.batch_read_feature_values(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.BatchReadFeatureValuesRequest, dict]
The request object. Request message for FeaturestoreService.BatchReadFeatureValues. |
featurestore |
str
Required. The resource name of the Featurestore from which to query Feature values. 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.Operation | An object representing a long-running operation. The result type for the operation will be BatchReadFeatureValuesResponse Response message for FeaturestoreService.BatchReadFeatureValues. |
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_entity_type
create_entity_type(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.CreateEntityTypeRequest, dict]] = None, *, parent: Optional[str] = None, entity_type: Optional[google.cloud.aiplatform_v1beta1.types.entity_type.EntityType] = None, entity_type_id: 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]] = ())
Creates a new EntityType in a given Featurestore.
from google.cloud import aiplatform_v1beta1
def sample_create_entity_type():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.CreateEntityTypeRequest(
parent="parent_value",
entity_type_id="entity_type_id_value",
)
# Make the request
operation = client.create_entity_type(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.CreateEntityTypeRequest, dict]
The request object. Request message for FeaturestoreService.CreateEntityType. |
parent |
str
Required. The resource name of the Featurestore to create EntityTypes. Format: |
entity_type |
google.cloud.aiplatform_v1beta1.types.EntityType
The EntityType to create. This corresponds to the |
entity_type_id |
str
Required. The ID to use for the EntityType, which will become the final component of the EntityType's resource name. This value may be up to 60 characters, and valid characters are |
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.Operation | An object representing a long-running operation. The result type for the operation will be EntityType An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver. |
create_feature
create_feature(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.CreateFeatureRequest, dict]] = None, *, parent: Optional[str] = None, feature: Optional[google.cloud.aiplatform_v1beta1.types.feature.Feature] = None, feature_id: 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]] = ())
Creates a new Feature in a given EntityType.
from google.cloud import aiplatform_v1beta1
def sample_create_feature():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
feature = aiplatform_v1beta1.Feature()
feature.value_type = "BYTES"
request = aiplatform_v1beta1.CreateFeatureRequest(
parent="parent_value",
feature=feature,
feature_id="feature_id_value",
)
# Make the request
operation = client.create_feature(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.CreateFeatureRequest, dict]
The request object. Request message for FeaturestoreService.CreateFeature. |
parent |
str
Required. The resource name of the EntityType to create a Feature. Format: |
feature |
google.cloud.aiplatform_v1beta1.types.Feature
Required. The Feature to create. This corresponds to the |
feature_id |
str
Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 60 characters, and valid characters are |
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.Operation | An object representing a long-running operation. The result type for the operation will be Feature Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple. |
create_featurestore
create_featurestore(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.CreateFeaturestoreRequest, dict]] = None, *, parent: Optional[str] = None, featurestore: Optional[google.cloud.aiplatform_v1beta1.types.featurestore.Featurestore] = None, featurestore_id: 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]] = ())
Creates a new Featurestore in a given project and location.
from google.cloud import aiplatform_v1beta1
def sample_create_featurestore():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.CreateFeaturestoreRequest(
parent="parent_value",
featurestore_id="featurestore_id_value",
)
# Make the request
operation = client.create_featurestore(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.CreateFeaturestoreRequest, dict]
The request object. Request message for FeaturestoreService.CreateFeaturestore. |
parent |
str
Required. The resource name of the Location to create Featurestores. Format: |
featurestore |
google.cloud.aiplatform_v1beta1.types.Featurestore
Required. The Featurestore to create. This corresponds to the |
featurestore_id |
str
Required. The ID to use for this Featurestore, which will become the final component of the Featurestore's resource name. This value may be up to 60 characters, and valid characters are |
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.Operation | An object representing a long-running operation. The result type for the operation will be Featurestore Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values. |
delete_entity_type
delete_entity_type(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.DeleteEntityTypeRequest, dict]] = None, *, name: Optional[str] = None, force: Optional[bool] = 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 single EntityType. The EntityType must not have any
Features or force
must be set to true for the request to
succeed.
from google.cloud import aiplatform_v1beta1
def sample_delete_entity_type():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteEntityTypeRequest(
name="name_value",
)
# Make the request
operation = client.delete_entity_type(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.DeleteEntityTypeRequest, dict]
The request object. Request message for [FeaturestoreService.DeleteEntityTypes][]. |
name |
str
Required. The name of the EntityType to be deleted. Format: |
force |
bool
If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.) 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.Operation | 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 {}. |
delete_feature
delete_feature(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.DeleteFeatureRequest, 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 single Feature.
from google.cloud import aiplatform_v1beta1
def sample_delete_feature():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteFeatureRequest(
name="name_value",
)
# Make the request
operation = client.delete_feature(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.DeleteFeatureRequest, dict]
The request object. Request message for FeaturestoreService.DeleteFeature. |
name |
str
Required. The name of the Features to be deleted. 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.Operation | 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 {}. |
delete_featurestore
delete_featurestore(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.DeleteFeaturestoreRequest, dict]] = None, *, name: Optional[str] = None, force: Optional[bool] = 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 single Featurestore. The Featurestore must not contain
any EntityTypes or force
must be set to true for the request
to succeed.
from google.cloud import aiplatform_v1beta1
def sample_delete_featurestore():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.DeleteFeaturestoreRequest(
name="name_value",
)
# Make the request
operation = client.delete_featurestore(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.DeleteFeaturestoreRequest, dict]
The request object. Request message for FeaturestoreService.DeleteFeaturestore. |
name |
str
Required. The name of the Featurestore to be deleted. Format: |
force |
bool
If set to true, any EntityTypes and Features for this Featurestore will also be deleted. (Otherwise, the request will only work if the Featurestore has no EntityTypes.) 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.Operation | 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 {}. |
entity_type_path
entity_type_path(project: str, location: str, featurestore: str, entity_type: str)
Returns a fully-qualified entity_type string.
export_feature_values
export_feature_values(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.ExportFeatureValuesRequest, dict]] = None, *, entity_type: 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]] = ())
Exports Feature values from all the entities of a target EntityType.
from google.cloud import aiplatform_v1beta1
def sample_export_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
destination = aiplatform_v1beta1.FeatureValueDestination()
destination.bigquery_destination.output_uri = "output_uri_value"
feature_selector = aiplatform_v1beta1.FeatureSelector()
feature_selector.id_matcher.ids = ['ids_value_1', 'ids_value_2']
request = aiplatform_v1beta1.ExportFeatureValuesRequest(
entity_type="entity_type_value",
destination=destination,
feature_selector=feature_selector,
)
# Make the request
operation = client.export_feature_values(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ExportFeatureValuesRequest, dict]
The request object. Request message for FeaturestoreService.ExportFeatureValues. |
entity_type |
str
Required. The resource name of the EntityType from which to export Feature values. 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.Operation | An object representing a long-running operation. The result type for the operation will be ExportFeatureValuesResponse Response message for FeaturestoreService.ExportFeatureValues. |
feature_path
feature_path(
project: str, location: str, featurestore: str, entity_type: str, feature: str
)
Returns a fully-qualified feature string.
featurestore_path
featurestore_path(project: str, location: str, featurestore: str)
Returns a fully-qualified featurestore string.
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 |
FeaturestoreServiceClient | 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 |
FeaturestoreServiceClient | 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 |
FeaturestoreServiceClient | The constructed client. |
get_entity_type
get_entity_type(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.GetEntityTypeRequest, 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 details of a single EntityType.
from google.cloud import aiplatform_v1beta1
def sample_get_entity_type():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetEntityTypeRequest(
name="name_value",
)
# Make the request
response = client.get_entity_type(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetEntityTypeRequest, dict]
The request object. Request message for FeaturestoreService.GetEntityType. |
name |
str
Required. The name of the EntityType 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.EntityType | An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver. |
get_feature
get_feature(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.GetFeatureRequest, 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 details of a single Feature.
from google.cloud import aiplatform_v1beta1
def sample_get_feature():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetFeatureRequest(
name="name_value",
)
# Make the request
response = client.get_feature(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetFeatureRequest, dict]
The request object. Request message for FeaturestoreService.GetFeature. |
name |
str
Required. The name of the Feature 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.Feature | Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple. |
get_featurestore
get_featurestore(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.GetFeaturestoreRequest, 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 details of a single Featurestore.
from google.cloud import aiplatform_v1beta1
def sample_get_featurestore():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.GetFeaturestoreRequest(
name="name_value",
)
# Make the request
response = client.get_featurestore(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.GetFeaturestoreRequest, dict]
The request object. Request message for FeaturestoreService.GetFeaturestore. |
name |
str
Required. The name of the Featurestore 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.Featurestore | Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values. |
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. |
import_feature_values
import_feature_values(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.ImportFeatureValuesRequest, dict]] = None, *, entity_type: 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]] = ())
Imports Feature values into the Featurestore from a source storage. The progress of the import is tracked by the returned operation. The imported features are guaranteed to be visible to subsequent read operations after the operation is marked as successfully done. If an import operation fails, the Feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same import request again and wait till the new operation returned is marked as successfully done. There are also scenarios where the caller can cause inconsistency.
- Source data for import contains multiple distinct Feature values for the same entity ID and timestamp.
- Source is modified during an import. This includes adding, updating, or removing source data and/or metadata. Examples of updating metadata include but are not limited to changing storage location, storage class, or retention policy.
- Online serving cluster is under-provisioned.
from google.cloud import aiplatform_v1beta1
def sample_import_feature_values():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
avro_source = aiplatform_v1beta1.AvroSource()
avro_source.gcs_source.uris = ['uris_value_1', 'uris_value_2']
feature_specs = aiplatform_v1beta1.FeatureSpec()
feature_specs.id = "id_value"
request = aiplatform_v1beta1.ImportFeatureValuesRequest(
avro_source=avro_source,
feature_time_field="feature_time_field_value",
entity_type="entity_type_value",
feature_specs=feature_specs,
)
# Make the request
operation = client.import_feature_values(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ImportFeatureValuesRequest, dict]
The request object. Request message for FeaturestoreService.ImportFeatureValues. |
entity_type |
str
Required. The resource name of the EntityType grouping the Features for which values are being imported. 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.Operation | An object representing a long-running operation. The result type for the operation will be ImportFeatureValuesResponse Response message for FeaturestoreService.ImportFeatureValues. |
list_entity_types
list_entity_types(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.ListEntityTypesRequest, 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 EntityTypes in a given Featurestore.
from google.cloud import aiplatform_v1beta1
def sample_list_entity_types():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListEntityTypesRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_entity_types(request=request)
# Handle the response
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListEntityTypesRequest, dict]
The request object. Request message for FeaturestoreService.ListEntityTypes. |
parent |
str
Required. The resource name of the Featurestore to list EntityTypes. 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.featurestore_service.pagers.ListEntityTypesPager | Response message for FeaturestoreService.ListEntityTypes. Iterating over this object will yield results and resolve additional pages automatically. |
list_features
list_features(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.ListFeaturesRequest, 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 Features in a given EntityType.
from google.cloud import aiplatform_v1beta1
def sample_list_features():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListFeaturesRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_features(request=request)
# Handle the response
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListFeaturesRequest, dict]
The request object. Request message for FeaturestoreService.ListFeatures. |
parent |
str
Required. The resource name of the Location to list Features. 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.featurestore_service.pagers.ListFeaturesPager | Response message for FeaturestoreService.ListFeatures. Iterating over this object will yield results and resolve additional pages automatically. |
list_featurestores
list_featurestores(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.ListFeaturestoresRequest, 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 Featurestores in a given project and location.
from google.cloud import aiplatform_v1beta1
def sample_list_featurestores():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.ListFeaturestoresRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_featurestores(request=request)
# Handle the response
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.ListFeaturestoresRequest, dict]
The request object. Request message for FeaturestoreService.ListFeaturestores. |
parent |
str
Required. The resource name of the Location to list Featurestores. 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.featurestore_service.pagers.ListFeaturestoresPager | Response message for FeaturestoreService.ListFeaturestores. Iterating over this object will yield results and resolve additional pages automatically. |
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_entity_type_path
parse_entity_type_path(path: str)
Parses a entity_type path into its component segments.
parse_feature_path
parse_feature_path(path: str)
Parses a feature path into its component segments.
parse_featurestore_path
parse_featurestore_path(path: str)
Parses a featurestore path into its component segments.
search_features
search_features(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.SearchFeaturesRequest, dict]] = None, *, location: Optional[str] = None, query: 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]] = ())
Searches Features matching a query in a given project.
from google.cloud import aiplatform_v1beta1
def sample_search_features():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.SearchFeaturesRequest(
location="location_value",
)
# Make the request
page_result = client.search_features(request=request)
# Handle the response
for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.SearchFeaturesRequest, dict]
The request object. Request message for FeaturestoreService.SearchFeatures. |
location |
str
Required. The resource name of the Location to search Features. Format: |
query |
str
Query string that is a conjunction of field-restricted queries and/or field-restricted filters. Field-restricted queries and filters can be combined using |
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.featurestore_service.pagers.SearchFeaturesPager | Response message for FeaturestoreService.SearchFeatures. Iterating over this object will yield results and resolve additional pages automatically. |
update_entity_type
update_entity_type(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.UpdateEntityTypeRequest, dict]] = None, *, entity_type: Optional[google.cloud.aiplatform_v1beta1.types.entity_type.EntityType] = 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 the parameters of a single EntityType.
from google.cloud import aiplatform_v1beta1
def sample_update_entity_type():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.UpdateEntityTypeRequest(
)
# Make the request
response = client.update_entity_type(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.UpdateEntityTypeRequest, dict]
The request object. Request message for FeaturestoreService.UpdateEntityType. |
entity_type |
google.cloud.aiplatform_v1beta1.types.EntityType
Required. The EntityType's |
update_mask |
google.protobuf.field_mask_pb2.FieldMask
Field mask is used to specify the fields to be overwritten in the EntityType resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to |
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.EntityType | An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver. |
update_feature
update_feature(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.UpdateFeatureRequest, dict]] = None, *, feature: Optional[google.cloud.aiplatform_v1beta1.types.feature.Feature] = 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 the parameters of a single Feature.
from google.cloud import aiplatform_v1beta1
def sample_update_feature():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
feature = aiplatform_v1beta1.Feature()
feature.value_type = "BYTES"
request = aiplatform_v1beta1.UpdateFeatureRequest(
feature=feature,
)
# Make the request
response = client.update_feature(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.UpdateFeatureRequest, dict]
The request object. Request message for FeaturestoreService.UpdateFeature. |
feature |
google.cloud.aiplatform_v1beta1.types.Feature
Required. The Feature's |
update_mask |
google.protobuf.field_mask_pb2.FieldMask
Field mask is used to specify the fields to be overwritten in the Features resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to |
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.Feature | Feature Metadata information that describes an attribute of an entity type. For example, apple is an entity type, and color is a feature that describes apple. |
update_featurestore
update_featurestore(request: Optional[Union[google.cloud.aiplatform_v1beta1.types.featurestore_service.UpdateFeaturestoreRequest, dict]] = None, *, featurestore: Optional[google.cloud.aiplatform_v1beta1.types.featurestore.Featurestore] = 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 the parameters of a single Featurestore.
from google.cloud import aiplatform_v1beta1
def sample_update_featurestore():
# Create a client
client = aiplatform_v1beta1.FeaturestoreServiceClient()
# Initialize request argument(s)
request = aiplatform_v1beta1.UpdateFeaturestoreRequest(
)
# Make the request
operation = client.update_featurestore(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.types.UpdateFeaturestoreRequest, dict]
The request object. Request message for FeaturestoreService.UpdateFeaturestore. |
featurestore |
google.cloud.aiplatform_v1beta1.types.Featurestore
Required. The Featurestore's |
update_mask |
google.protobuf.field_mask_pb2.FieldMask
Field mask is used to specify the fields to be overwritten in the Featurestore resource by the update. The fields specified in the update_mask are relative to the resource, not the full request. A field will be overwritten if it is in the mask. If the user does not provide a mask then only the non-empty fields present in the request will be overwritten. Set the update_mask to |
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.Operation | An object representing a long-running operation. The result type for the operation will be Featurestore Vertex AI Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values. |