Class ModelServiceClient (1.20.1) 

ModelServiceClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.retail_v2alpha.services.model_service.transports.base.ModelServiceTransport]] = None, client_options: typing.Optional[typing.Union[google.api_core.client_options.ClientOptions, dict]] = 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.

Properties

api_endpoint

Return the API endpoint used by the client instance.

Returns
Type Description
str The API endpoint used by the client instance.

transport

Returns the transport used by the client instance.

Returns
Type Description
ModelServiceTransport The transport used by the client instance.

universe_domain

Return the universe domain used by the client instance.

Returns
Type Description
str The universe domain used by the client instance.

Methods

ModelServiceClient

ModelServiceClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.retail_v2alpha.services.model_service.transports.base.ModelServiceTransport]] = None, client_options: typing.Optional[typing.Union[google.api_core.client_options.ClientOptions, dict]] = 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 Optional[Union[google.api_core.client_options.ClientOptions, dict]]

Custom options for the client. 1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: "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). 2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide a client certificate for mTLS 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. 3. The universe_domain property can be used to override the default "googleapis.com" universe. Note that the api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.
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) -> str

Returns a fully-qualified catalog string.

common_billing_account_path

common_billing_account_path(billing_account: str) -> str

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str) -> str

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str) -> str

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str) -> str

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str) -> str

Returns a fully-qualified project string.

create_model

create_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.retail_v2alpha.types.model_service.CreateModelRequest, dict
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    model: typing.Optional[google.cloud.retail_v2alpha.types.model.Model] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Creates a new model.

# 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 retail_v2alpha

def sample_create_model():
    # Create a client
    client = retail_v2alpha.ModelServiceClient()

    # Initialize request argument(s)
    model = retail_v2alpha.Model()
    model.page_optimization_config.page_optimization_event_type = "page_optimization_event_type_value"
    model.page_optimization_config.panels.candidates.serving_config_id = "serving_config_id_value"
    model.page_optimization_config.panels.default_candidate.serving_config_id = "serving_config_id_value"
    model.name = "name_value"
    model.display_name = "display_name_value"
    model.type_ = "type__value"

    request = retail_v2alpha.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_v2alpha.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_v2alpha.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: typing.Optional[
        typing.Union[
            google.cloud.retail_v2alpha.types.model_service.DeleteModelRequest, dict
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None

Deletes an existing model.

# 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 retail_v2alpha

def sample_delete_model():
    # Create a client
    client = retail_v2alpha.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2alpha.DeleteModelRequest(
        name="name_value",
    )

    # Make the request
    client.delete_model(request=request)
Parameters
Name Description
request Union[google.cloud.retail_v2alpha.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_model

get_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.retail_v2alpha.types.model_service.GetModelRequest, dict
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.retail_v2alpha.types.model.Model

Gets a model.

# 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 retail_v2alpha

def sample_get_model():
    # Create a client
    client = retail_v2alpha.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2alpha.GetModelRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)
Parameters
Name Description
request Union[google.cloud.retail_v2alpha.types.GetModelRequest, dict]

The request object. Request for getting a model.
name str

Required. The resource name of the Model to get. Format: projects/{project_number}/locations/{location_id}/catalogs/{catalog}/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_v2alpha.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.

get_mtls_endpoint_and_cert_source

get_mtls_endpoint_and_cert_source(
    client_options: typing.Optional[
        google.api_core.client_options.ClientOptions
    ] = None,
)

Deprecated. 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.

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.

get_operation

get_operation(
    request: typing.Optional[
        google.longrunning.operations_pb2.GetOperationRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.Operation

Gets the latest state of a long-running operation.

Parameters
Name Description
request .operations_pb2.GetOperationRequest

The request object. Request message for GetOperation method.
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
.operations_pb2.Operation An Operation object.

list_models

list_models(
    request: typing.Optional[
        typing.Union[
            google.cloud.retail_v2alpha.types.model_service.ListModelsRequest, dict
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.retail_v2alpha.services.model_service.pagers.ListModelsPager

Lists all the models linked to this event store.

# 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 retail_v2alpha

def sample_list_models():
    # Create a client
    client = retail_v2alpha.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2alpha.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_v2alpha.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_v2alpha.services.model_service.pagers.ListModelsPager Response to a ListModelRequest. Iterating over this object will yield results and resolve additional pages automatically.

list_operations

list_operations(
    request: typing.Optional[
        google.longrunning.operations_pb2.ListOperationsRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.ListOperationsResponse

Lists operations that match the specified filter in the request.

Parameters
Name Description
request .operations_pb2.ListOperationsRequest

The request object. Request message for ListOperations method.
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
.operations_pb2.ListOperationsResponse Response message for ListOperations method.

model_path

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

Returns a fully-qualified model string.

parse_catalog_path

parse_catalog_path(path: str) -> typing.Dict[str, str]

Parses a catalog path into its component segments.

parse_common_billing_account_path

parse_common_billing_account_path(path: str) -> typing.Dict[str, str]

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str) -> typing.Dict[str, str]

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str) -> typing.Dict[str, str]

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str) -> typing.Dict[str, str]

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str) -> typing.Dict[str, str]

Parse a project path into its component segments.

parse_model_path

parse_model_path(path: str) -> typing.Dict[str, str]

Parses a model path into its component segments.

pause_model

pause_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.retail_v2alpha.types.model_service.PauseModelRequest, dict
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.retail_v2alpha.types.model.Model

Pauses the training of an existing model.

# 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 retail_v2alpha

def sample_pause_model():
    # Create a client
    client = retail_v2alpha.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2alpha.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_v2alpha.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_v2alpha.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: typing.Optional[
        typing.Union[
            google.cloud.retail_v2alpha.types.model_service.ResumeModelRequest, dict
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.retail_v2alpha.types.model.Model

Resumes the training of an existing model.

# 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 retail_v2alpha

def sample_resume_model():
    # Create a client
    client = retail_v2alpha.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2alpha.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_v2alpha.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_v2alpha.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: typing.Optional[
        typing.Union[
            google.cloud.retail_v2alpha.types.model_service.TuneModelRequest, dict
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Tunes an existing model.

# 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 retail_v2alpha

def sample_tune_model():
    # Create a client
    client = retail_v2alpha.ModelServiceClient()

    # Initialize request argument(s)
    request = retail_v2alpha.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_v2alpha.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: typing.Optional[
        typing.Union[
            google.cloud.retail_v2alpha.types.model_service.UpdateModelRequest, dict
        ]
    ] = None,
    *,
    model: typing.Optional[google.cloud.retail_v2alpha.types.model.Model] = None,
    update_mask: typing.Optional[google.protobuf.field_mask_pb2.FieldMask] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.retail_v2alpha.types.model.Model

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.

# 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 retail_v2alpha

def sample_update_model():
    # Create a client
    client = retail_v2alpha.ModelServiceClient()

    # Initialize request argument(s)
    model = retail_v2alpha.Model()
    model.page_optimization_config.page_optimization_event_type = "page_optimization_event_type_value"
    model.page_optimization_config.panels.candidates.serving_config_id = "serving_config_id_value"
    model.page_optimization_config.panels.default_candidate.serving_config_id = "serving_config_id_value"
    model.name = "name_value"
    model.display_name = "display_name_value"
    model.type_ = "type__value"

    request = retail_v2alpha.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_v2alpha.types.UpdateModelRequest, dict]

The request object. Request for updating an existing model.
model google.cloud.retail_v2alpha.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_v2alpha.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.