Class AutoMlAsyncClient (2.1.0)

AutoMlAsyncClient(*, credentials: google.auth.credentials.Credentials = None, transport: Union[str, google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport] = 'grpc_asyncio', client_options: <module 'google.api_core.client_options' from '/workspace/python-automl/.nox/docfx/lib/python3.9/site-packages/google/api_core/client_options.py'> = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

AutoML Server API.

The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted.

An ID of a resource is the last element of the item's resource name. For projects/{project_id}/locations/{location_id}/datasets/{dataset_id}, then the id for the item is {dataset_id}.

Currently the only supported location_id is "us-central1".

On any input that is documented to expect a string parameter in snake_case or kebab-case, either of those cases is accepted.

Methods

AutoMlAsyncClient

AutoMlAsyncClient(*, credentials: google.auth.credentials.Credentials = None, transport: Union[str, google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport] = 'grpc_asyncio', client_options: <module 'google.api_core.client_options' from '/workspace/python-automl/.nox/docfx/lib/python3.9/site-packages/google/api_core/client_options.py'> = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Instantiate the auto ml 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, .AutoMlTransport]` The transport to use. If set to None, a transport is chosen automatically.
`client_options`	`ClientOptions` Custom options for the client. It won't take effect if a `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.

Exceptions

Type	Description
`google.auth.exceptions.MutualTlsChannelError`	If mutual TLS transport creation failed for any reason.

create_dataset

create_dataset(request: Optional[google.cloud.automl_v1.types.service.CreateDatasetRequest] = None, *, parent: Optional[str] = None, dataset: Optional[google.cloud.automl_v1.types.dataset.Dataset] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a dataset.

Parameters

Name	Description
`request`	`.service.CreateDatasetRequest` The request object. Request message for AutoMl.CreateDataset.
`parent`	`str` Required. The resource name of the project to create the dataset for. This corresponds to the `parent` field on the `request` instance; if `request` is provided, this should not be set.
`dataset`	`.gca_dataset.Dataset` Required. The dataset to create. This corresponds to the `dataset` 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
`.operation_async.AsyncOperation`	An object representing a long-running operation. The result type for the operation will be .gca_dataset.Dataset : A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

create_model

create_model(request: Optional[google.cloud.automl_v1.types.service.CreateModelRequest] = None, *, parent: Optional[str] = None, model: Optional[google.cloud.automl_v1.types.model.Model] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a model. Returns a Model in the response][google.longrunning.Operation.response] field when it completes. When you create a model, several model evaluations are created for it: a global evaluation, and one evaluation for each annotation spec.

Parameters

Name	Description
`request`	`.service.CreateModelRequest` The request object. Request message for AutoMl.CreateModel.
`parent`	`str` Required. Resource name of the parent project where the model is being created. This corresponds to the `parent` field on the `request` instance; if `request` is provided, this should not be set.
`model`	`.gca_model.Model` Required. 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
`.operation_async.AsyncOperation`	An object representing a long-running operation. The result type for the operation will be .gca_model.Model : API proto representing a trained machine learning model.

dataset_path

dataset_path(project: str, location: str, dataset: str)

Return a fully-qualified dataset string.

delete_dataset

delete_dataset(request: Optional[google.cloud.automl_v1.types.service.DeleteDatasetRequest] = None, *, name: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a dataset and all of its contents. Returns empty response in the response][google.longrunning.Operation.response] field when it completes, and delete_details in the metadata][google.longrunning.Operation.metadata] field.

Parameters

Name	Description
`request`	`.service.DeleteDatasetRequest` The request object. Request message for AutoMl.DeleteDataset.
`name`	`str` Required. The resource name of the dataset to delete. 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

.operation_async.AsyncOperation

An object representing a long-running operation. The result type for the operation will be

.empty.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_model

delete_model(request: Optional[google.cloud.automl_v1.types.service.DeleteModelRequest] = None, *, name: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a model. Returns google.protobuf.Empty in the response][google.longrunning.Operation.response] field when it completes, and delete_details in the metadata][google.longrunning.Operation.metadata] field.

Parameters

Name	Description
`request`	`.service.DeleteModelRequest` The request object. Request message for AutoMl.DeleteModel.
`name`	`str` Required. Resource name of the model being deleted. 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

.operation_async.AsyncOperation

An object representing a long-running operation. The result type for the operation will be

.empty.Empty

deploy_model

deploy_model(request: Optional[google.cloud.automl_v1.types.service.DeployModelRequest] = None, *, name: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deploys a model. If a model is already deployed, deploying it with the same parameters has no effect. Deploying with different parametrs (as e.g. changing

xref_node_number) will reset the deployment state without pausing the model's availability.

Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage deployment automatically.

Returns an empty response in the response][google.longrunning.Operation.response] field when it completes.

Parameters

Name	Description
`request`	`.service.DeployModelRequest` The request object. Request message for AutoMl.DeployModel.
`name`	`str` Required. Resource name of the model to deploy. 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

.operation_async.AsyncOperation

An object representing a long-running operation. The result type for the operation will be

.empty.Empty

export_data

export_data(request: Optional[google.cloud.automl_v1.types.service.ExportDataRequest] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.automl_v1.types.io.OutputConfig] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Exports dataset's data to the provided output location. Returns an empty response in the response][google.longrunning.Operation.response] field when it completes.

Parameters

Name	Description
`request`	`.service.ExportDataRequest` The request object. Request message for AutoMl.ExportData.
`name`	`str` Required. The resource name of the dataset. This corresponds to the `name` field on the `request` instance; if `request` is provided, this should not be set.
`output_config`	`.io.OutputConfig` Required. The desired output location. This corresponds to the `output_config` 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

.operation_async.AsyncOperation

An object representing a long-running operation. The result type for the operation will be

.empty.Empty

export_model

export_model(request: Optional[google.cloud.automl_v1.types.service.ExportModelRequest] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.automl_v1.types.io.ModelExportOutputConfig] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Exports a trained, "export-able", model to a user specified Google Cloud Storage location. A model is considered export-able if and only if it has an export format defined for it in xref_ModelExportOutputConfig.

Returns an empty response in the response][google.longrunning.Operation.response] field when it completes.

Parameters

Name	Description
`request`	`.service.ExportModelRequest` The request object. Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.
`name`	`str` Required. The resource name of the model to export. This corresponds to the `name` field on the `request` instance; if `request` is provided, this should not be set.
`output_config`	`.io.ModelExportOutputConfig` Required. The desired output location and configuration. This corresponds to the `output_config` 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

.operation_async.AsyncOperation

An object representing a long-running operation. The result type for the operation will be

.empty.Empty

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
`{@api.name}`	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
`{@api.name}`	The constructed client.

get_annotation_spec

get_annotation_spec(request: Optional[google.cloud.automl_v1.types.service.GetAnnotationSpecRequest] = None, *, name: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets an annotation spec.

Parameters

Name	Description
`request`	`.service.GetAnnotationSpecRequest` The request object. Request message for AutoMl.GetAnnotationSpec.
`name`	`str` Required. The resource name of the annotation spec to retrieve. 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
`.annotation_spec.AnnotationSpec`	A definition of an annotation spec.

get_dataset

get_dataset(request: Optional[google.cloud.automl_v1.types.service.GetDatasetRequest] = None, *, name: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a dataset.

Parameters

Name	Description
`request`	`.service.GetDatasetRequest` The request object. Request message for AutoMl.GetDataset.
`name`	`str` Required. The resource name of the dataset to retrieve. 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
`.dataset.Dataset`	A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

get_model

get_model(request: Optional[google.cloud.automl_v1.types.service.GetModelRequest] = None, *, name: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a model.

Parameters

Name	Description
`request`	`.service.GetModelRequest` The request object. Request message for AutoMl.GetModel.
`name`	`str` Required. Resource name of the model. 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
`.model.Model`	API proto representing a trained machine learning model.

get_model_evaluation

get_model_evaluation(request: Optional[google.cloud.automl_v1.types.service.GetModelEvaluationRequest] = None, *, name: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a model evaluation.

Parameters

Name	Description
`request`	`.service.GetModelEvaluationRequest` The request object. Request message for AutoMl.GetModelEvaluation.
`name`	`str` Required. Resource name for the model evaluation. 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
`.model_evaluation.ModelEvaluation`	Evaluation results of a model.

get_transport_class

get_transport_class()

Return an appropriate transport class.

import_data

import_data(request: Optional[google.cloud.automl_v1.types.service.ImportDataRequest] = None, *, name: Optional[str] = None, input_config: Optional[google.cloud.automl_v1.types.io.InputConfig] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Imports data into a dataset. For Tables this method can only be called on an empty Dataset.

For Tables:

A xref_schema_inference_version parameter must be explicitly set. Returns an empty response in the response][google.longrunning.Operation.response] field when it completes.

Parameters

Name	Description
`request`	`.service.ImportDataRequest` The request object. Request message for AutoMl.ImportData.
`name`	`str` Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added. This corresponds to the `name` field on the `request` instance; if `request` is provided, this should not be set.
`input_config`	`.io.InputConfig` Required. The desired input location and its domain specific semantics, if any. This corresponds to the `input_config` 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

.operation_async.AsyncOperation

An object representing a long-running operation. The result type for the operation will be

.empty.Empty

list_datasets

list_datasets(request: Optional[google.cloud.automl_v1.types.service.ListDatasetsRequest] = None, *, parent: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists datasets in a project.

Parameters

Name	Description
`request`	`.service.ListDatasetsRequest` The request object. Request message for AutoMl.ListDatasets.
`parent`	`str` Required. The resource name of the project from which to list datasets. 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
`.pagers.ListDatasetsAsyncPager`	Response message for AutoMl.ListDatasets. Iterating over this object will yield results and resolve additional pages automatically.

list_model_evaluations

list_model_evaluations(request: Optional[google.cloud.automl_v1.types.service.ListModelEvaluationsRequest] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists model evaluations.

Parameters

Name	Description
`request`	`.service.ListModelEvaluationsRequest` The request object. Request message for AutoMl.ListModelEvaluations.
`parent`	`str` Required. Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location. This corresponds to the `parent` field on the `request` instance; if `request` is provided, this should not be set.
`filter`	`str` Required. An expression for filtering the results of the request. - `annotation_spec_id` - for =, != or existence. See example below for the last. Some examples of using the filter are: - `annotation_spec_id!=4` --> The model evaluation was done for annotation spec with ID different than 4. - `NOT annotation_spec_id:*` --> The model evaluation was done for aggregate of all annotation specs. This corresponds to the `filter` 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
`.pagers.ListModelEvaluationsAsyncPager`	Response message for AutoMl.ListModelEvaluations. Iterating over this object will yield results and resolve additional pages automatically.

list_models

list_models(request: Optional[google.cloud.automl_v1.types.service.ListModelsRequest] = None, *, parent: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists models.

Parameters

Name	Description
`request`	`.service.ListModelsRequest` The request object. Request message for AutoMl.ListModels.
`parent`	`str` Required. Resource name of the project, from which to list the models. 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
`.pagers.ListModelsAsyncPager`	Response message for AutoMl.ListModels. Iterating over this object will yield results and resolve additional pages automatically.

model_path

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

Return a fully-qualified model string.

parse_dataset_path

parse_dataset_path(path: str)

Parse a dataset path into its component segments.

parse_model_path

parse_model_path(path: str)

Parse a model path into its component segments.

undeploy_model

undeploy_model(request: Optional[google.cloud.automl_v1.types.service.UndeployModelRequest] = None, *, name: Optional[str] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Undeploys a model. If the model is not deployed this method has no effect.

Only applicable for Text Classification, Image Object Detection and Tables; all other domains manage deployment automatically.

Returns an empty response in the response][google.longrunning.Operation.response] field when it completes.

Parameters

Name	Description
`request`	`.service.UndeployModelRequest` The request object. Request message for AutoMl.UndeployModel.
`name`	`str` Required. Resource name of the model to undeploy. 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

.operation_async.AsyncOperation

An object representing a long-running operation. The result type for the operation will be

.empty.Empty

update_dataset

update_dataset(request: Optional[google.cloud.automl_v1.types.service.UpdateDatasetRequest] = None, *, dataset: Optional[google.cloud.automl_v1.types.dataset.Dataset] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a dataset.

Parameters

Name	Description
`request`	`.service.UpdateDatasetRequest` The request object. Request message for AutoMl.UpdateDataset
`dataset`	`.gca_dataset.Dataset` Required. The dataset which replaces the resource on the server. This corresponds to the `dataset` field on the `request` instance; if `request` is provided, this should not be set.
`update_mask`	`.field_mask.FieldMask` Required. The update mask applies to the resource. 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
`.gca_dataset.Dataset`	A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

update_model

update_model(request: Optional[google.cloud.automl_v1.types.service.UpdateModelRequest] = None, *, model: Optional[google.cloud.automl_v1.types.model.Model] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a model.

Parameters

Name	Description
`request`	`.service.UpdateModelRequest` The request object. Request message for AutoMl.UpdateModel
`model`	`.gca_model.Model` Required. The model which replaces the resource on the server. This corresponds to the `model` field on the `request` instance; if `request` is provided, this should not be set.
`update_mask`	`.field_mask.FieldMask` Required. The update mask applies to the resource. 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
`.gca_model.Model`	API proto representing a trained machine learning model.