Class ModelServiceClient (1.10.0)

ModelServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.retail_v2beta.services.model_service.transports.base.ModelServiceTransport]] = 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>)

Service for performing CRUD operations on models. Recommendation models contain all the metadata necessary to generate a set of models for the Predict() API. A model is queried indirectly via a ServingConfig, which associates a model with a given Placement (e.g. Frequently Bought Together on Home Page).

This service allows you to do the following:

Initiate training of a model.
Pause training of an existing model.
List all the available models along with their metadata.
Control their tuning schedule.

Inheritance

builtins.object > ModelServiceClient

Properties

transport

Returns the transport used by the client instance.

Returns

Type	Description
ModelServiceTransport	The transport used by the client instance.

Methods

ModelServiceClient

ModelServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.retail_v2beta.services.model_service.transports.base.ModelServiceTransport]] = 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 model service client.

Parameters

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, ModelServiceTransport]` 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 `transport` instance is provided. (1) The `api_endpoint` property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the `api_endpoint` property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the `client_cert_source` property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used.
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 `None`, then default info will be used. Generally, you only need to set this if you're developing your own client library.

Exceptions

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.

catalog_path

catalog_path(project: str, location: str, catalog: str)

Returns a fully-qualified catalog 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_model

create_model(request: Optional[Union[google.cloud.retail_v2beta.types.model_service.CreateModelRequest, dict]] = None, *, parent: Optional[str] = None, model: Optional[google.cloud.retail_v2beta.types.model.Model] = 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 model.

from google.cloud import retail_v2beta

def sample_create_model():
    # Create a client
    client = retail_v2beta.ModelServiceClient()

    # Initialize request argument(s)
    model = retail_v2beta.Model()
    model.name = "name_value"
    model.display_name = "display_name_value"
    model.type_ = "type__value"

    request = retail_v2beta.CreateModelRequest(
        parent="parent_value",
        model=model,
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.retail_v2beta.types.CreateModelRequest, dict]` The request object. Request for creating a model.
parent	`str` Required. The parent resource under which to create the model. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}` This corresponds to the `parent` field on the `request` instance; if `request` is provided, this should not be set.
model	`google.cloud.retail_v2beta.types.Model` Required. The payload of the Model to create. This corresponds to the `model` field on the `request` instance; if `request` is provided, this should not be set.
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.

Returns

Type	Description
google.api_core.operation.Operation	An object representing a long-running operation. The result type for the operation will be Model Metadata that describes the training and serving parameters of a Model. A Model can be associated with a ServingConfig and then queried through the Predict API.

delete_model

delete_model(request: Optional[Union[google.cloud.retail_v2beta.types.model_service.DeleteModelRequest, 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 an existing model.

from google.cloud import retail_v2beta

def sample_delete_model():
    # Create a client
    client = retail_v2beta.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2beta.DeleteModelRequest(
        name="name_value",
    )

    # Make the request
    client.delete_model(request=request)

Parameters

Name	Description
request	`Union[google.cloud.retail_v2beta.types.DeleteModelRequest, dict]` The request object. Request for deleting a model.
name	`str` Required. The resource name of the Model to delete. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` This corresponds to the `name` field on the `request` instance; if `request` is provided, this should not be set.
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.

from_service_account_file

from_service_account_file(filename: str, *args, **kwargs)

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

Parameter

Name	Description
filename	`str` The path to the service account private key json file.

Returns

Type	Description
ModelServiceClient	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.

Parameter

Name	Description
info	`dict` The service account private key info.

Returns

Type	Description
ModelServiceClient	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.

Parameter

Name	Description
filename	`str` The path to the service account private key json file.

Returns

Type	Description
ModelServiceClient	The constructed client.

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.

Parameter

Name	Description
client_options	`google.api_core.client_options.ClientOptions` Custom options for the client. Only the `api_endpoint` and `client_cert_source` properties may be used in this method.

Exceptions

Type	Description
google.auth.exceptions.MutualTLSChannelError	If any errors happen.

Returns

Type	Description
Tuple[str, Callable[[], Tuple[bytes, bytes]]]	returns the API endpoint and the client cert source to use.

list_models

list_models(request: Optional[Union[google.cloud.retail_v2beta.types.model_service.ListModelsRequest, 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 all the models linked to this event store.

from google.cloud import retail_v2beta

def sample_list_models():
    # Create a client
    client = retail_v2beta.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2beta.ListModelsRequest(
        parent="parent_value",
    )

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

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

Parameters

Name	Description
request	`Union[google.cloud.retail_v2beta.types.ListModelsRequest, dict]` The request object. Request for listing models associated with a resource.
parent	`str` Required. The parent for which to list models. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}` This corresponds to the `parent` field on the `request` instance; if `request` is provided, this should not be set.
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.

Returns

Type	Description
google.cloud.retail_v2beta.services.model_service.pagers.ListModelsPager	Response to a ListModelRequest. Iterating over this object will yield results and resolve additional pages automatically.

model_path

model_path(project: str, location: str, catalog: str, model: str)

Returns a fully-qualified model string.

parse_catalog_path

parse_catalog_path(path: str)

Parses a catalog 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_model_path

parse_model_path(path: str)

Parses a model path into its component segments.

pause_model

pause_model(request: Optional[Union[google.cloud.retail_v2beta.types.model_service.PauseModelRequest, 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]] = ())

Pauses the training of an existing model.

from google.cloud import retail_v2beta

def sample_pause_model():
    # Create a client
    client = retail_v2beta.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2beta.PauseModelRequest(
        name="name_value",
    )

    # Make the request
    response = client.pause_model(request=request)

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.retail_v2beta.types.PauseModelRequest, dict]` The request object. Request for pausing training of a model.
name	`str` Required. The name of the model to pause. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` This corresponds to the `name` field on the `request` instance; if `request` is provided, this should not be set.
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.

Returns

Type	Description
google.cloud.retail_v2beta.types.Model	Metadata that describes the training and serving parameters of a Model. A Model can be associated with a ServingConfig and then queried through the Predict API.

resume_model

resume_model(request: Optional[Union[google.cloud.retail_v2beta.types.model_service.ResumeModelRequest, 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]] = ())

Resumes the training of an existing model.

from google.cloud import retail_v2beta

def sample_resume_model():
    # Create a client
    client = retail_v2beta.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2beta.ResumeModelRequest(
        name="name_value",
    )

    # Make the request
    response = client.resume_model(request=request)

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.retail_v2beta.types.ResumeModelRequest, dict]` The request object. Request for resuming training of a model.
name	`str` Required. The name of the model to resume. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` This corresponds to the `name` field on the `request` instance; if `request` is provided, this should not be set.
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.

Returns

Type	Description
google.cloud.retail_v2beta.types.Model	Metadata that describes the training and serving parameters of a Model. A Model can be associated with a ServingConfig and then queried through the Predict API.

tune_model

tune_model(request: Optional[Union[google.cloud.retail_v2beta.types.model_service.TuneModelRequest, 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]] = ())

Tunes an existing model.

from google.cloud import retail_v2beta

def sample_tune_model():
    # Create a client
    client = retail_v2beta.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2beta.TuneModelRequest(
        name="name_value",
    )

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

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

    response = operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.retail_v2beta.types.TuneModelRequest, dict]` The request object. Request to manually start a tuning process now (instead of waiting for the periodically scheduled tuning to happen).
name	`str` Required. The resource name of the model to tune. Format: `projects/{project_number}/locations/{location_id}/catalogs/{catalog_id}/models/{model_id}` This corresponds to the `name` field on the `request` instance; if `request` is provided, this should not be set.
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.

Returns

Type	Description
google.api_core.operation.Operation	An object representing a long-running operation. The result type for the operation will be TuneModelResponse Response associated with a tune operation.

update_model

update_model(request: Optional[Union[google.cloud.retail_v2beta.types.model_service.UpdateModelRequest, dict]] = None, *, model: Optional[google.cloud.retail_v2beta.types.model.Model] = 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]] = ())

Update of model metadata. Only fields that currently can be updated are: filtering_option and periodic_tuning_state. If other values are provided, this API method ignores them.

from google.cloud import retail_v2beta

def sample_update_model():
    # Create a client
    client = retail_v2beta.ModelServiceClient()

    # Initialize request argument(s)
    model = retail_v2beta.Model()
    model.name = "name_value"
    model.display_name = "display_name_value"
    model.type_ = "type__value"

    request = retail_v2beta.UpdateModelRequest(
        model=model,
    )

    # Make the request
    response = client.update_model(request=request)

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.retail_v2beta.types.UpdateModelRequest, dict]` The request object. Request for updating an existing model.
model	`google.cloud.retail_v2beta.types.Model` Required. The body of the updated Model. This corresponds to the `model` field on the `request` instance; if `request` is provided, this should not be set.
update_mask	`google.protobuf.field_mask_pb2.FieldMask` Optional. Indicates which fields in the provided 'model' to update. If not set, by default updates all fields. This corresponds to the `update_mask` field on the `request` instance; if `request` is provided, this should not be set.
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.

Returns

Type	Description
google.cloud.retail_v2beta.types.Model	Metadata that describes the training and serving parameters of a Model. A Model can be associated with a ServingConfig and then queried through the Predict API.