AutoMlAsyncClient(*, credentials: google.auth.credentials.Credentials = None, transport: Union[str, google.cloud.automl_v1beta1.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_v1beta1.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.
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,
The transport to use. If set to None, a transport is chosen automatically. |
client_options |
ClientOptions
Custom options for the client. It won't take effect if a |
Type | Description |
google.auth.exceptions.MutualTlsChannelError | If mutual TLS transport creation failed for any reason. |
column_spec_path
column_spec_path(
project: str, location: str, dataset: str, table_spec: str, column_spec: str
)
Return a fully-qualified column_spec string.
create_dataset
create_dataset(request: Optional[google.cloud.automl_v1beta1.types.service.CreateDatasetRequest] = None, *, parent: Optional[str] = None, dataset: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.CreateDataset. |
parent |
Required. The resource name of the project to create the dataset for. This corresponds to the |
dataset |
Required. The dataset to create. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
| 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_v1beta1.types.service.CreateModelRequest] = None, *, parent: Optional[str] = None, model: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.CreateModel. |
parent |
Required. Resource name of the parent project where the model is being created. This corresponds to the |
model |
Required. The model to create. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
| 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_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.DeleteDataset. |
name |
Required. The resource name of the dataset to delete. 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 |
| 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_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.DeleteModel. |
name |
Required. Resource name of the model being deleted. 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 |
| 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 {} . |
deploy_model
deploy_model(request: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.DeployModel. |
name |
Required. Resource name of the model to deploy. 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 |
| 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 {} . |
export_data
export_data(request: Optional[google.cloud.automl_v1beta1.types.service.ExportDataRequest] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.ExportData. |
name |
Required. The resource name of the dataset. This corresponds to the |
output_config |
Required. The desired output location. This corresponds to the |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
Type | Description |
| 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 {} . |
export_evaluated_examples
export_evaluated_examples(request: Optional[google.cloud.automl_v1beta1.types.service.ExportEvaluatedExamplesRequest] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.automl_v1beta1.types.io.ExportEvaluatedExamplesOutputConfig] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Exports examples on which the model was evaluated (i.e. which were in the TEST set of the dataset the model was created from), together with their ground truth annotations and the annotations created (predicted) by the model. The examples, ground truth and predictions are exported in the state they were at the moment the model was evaluated.
This export is available only for 30 days since the model evaluation is created.
Currently only available for Tables.
Returns an empty response in the
response][google.longrunning.Operation.response]
field when it
completes.
Name | Description |
request |
The request object. Request message for AutoMl.ExportEvaluatedExamples. |
name |
Required. The resource name of the model whose evaluated examples are to be exported. This corresponds to the |
output_config |
Required. The desired output location and configuration. 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 |
| 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 {} . |
export_model
export_model(request: Optional[google.cloud.automl_v1beta1.types.service.ExportModelRequest] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned. |
name |
Required. The resource name of the model to export. This corresponds to the |
output_config |
Required. The desired output location and configuration. 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 |
| 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 {} . |
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 |
{@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.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
{@api.name} | The constructed client. |
get_annotation_spec
get_annotation_spec(request: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.GetAnnotationSpec. |
name |
Required. The resource name of the annotation spec to retrieve. 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 |
| A definition of an annotation spec. |
get_column_spec
get_column_spec(request: Optional[google.cloud.automl_v1beta1.types.service.GetColumnSpecRequest] = 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 column spec.
Name | Description |
request |
The request object. Request message for AutoMl.GetColumnSpec. |
name |
Required. The resource name of the column spec to retrieve. 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 |
| A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: - Tables |
get_dataset
get_dataset(request: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.GetDataset. |
name |
Required. The resource name of the dataset to retrieve. 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 |
| 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_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.GetModel. |
name |
Required. Resource name of the model. 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 |
| API proto representing a trained machine learning model. |
get_model_evaluation
get_model_evaluation(request: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.GetModelEvaluation. |
name |
Required. Resource name for the model evaluation. 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 |
| Evaluation results of a model. |
get_table_spec
get_table_spec(request: Optional[google.cloud.automl_v1beta1.types.service.GetTableSpecRequest] = 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 table spec.
Name | Description |
request |
The request object. Request message for AutoMl.GetTableSpec. |
name |
Required. The resource name of the table spec to retrieve. 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 |
| A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: - Tables |
get_transport_class
get_transport_class()
Return an appropriate transport class.
import_data
import_data(request: Optional[google.cloud.automl_v1beta1.types.service.ImportDataRequest] = None, *, name: Optional[str] = None, input_config: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.ImportData. |
name |
Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added. This corresponds to the |
input_config |
Required. The desired input location and its domain specific semantics, if any. 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 |
| 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 {} . |
list_column_specs
list_column_specs(request: Optional[google.cloud.automl_v1beta1.types.service.ListColumnSpecsRequest] = 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 column specs in a table spec.
Name | Description |
request |
The request object. Request message for AutoMl.ListColumnSpecs. |
parent |
Required. The resource name of the table spec to list column specs from. 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 |
| Response message for AutoMl.ListColumnSpecs. Iterating over this object will yield results and resolve additional pages automatically. |
list_datasets
list_datasets(request: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.ListDatasets. |
parent |
Required. The resource name of the project from which to list datasets. 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 |
| 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_v1beta1.types.service.ListModelEvaluationsRequest] = 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 model evaluations.
Name | Description |
request |
The request object. Request message for AutoMl.ListModelEvaluations. |
parent |
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 |
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 |
| 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_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.ListModels. |
parent |
Required. Resource name of the project, from which to list the models. 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 |
| Response message for AutoMl.ListModels. Iterating over this object will yield results and resolve additional pages automatically. |
list_table_specs
list_table_specs(request: Optional[google.cloud.automl_v1beta1.types.service.ListTableSpecsRequest] = 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 table specs in a dataset.
Name | Description |
request |
The request object. Request message for AutoMl.ListTableSpecs. |
parent |
Required. The resource name of the dataset to list table specs from. 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 |
| Response message for AutoMl.ListTableSpecs. 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_column_spec_path
parse_column_spec_path(path: str)
Parse a column_spec path into its component segments.
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.
parse_table_spec_path
parse_table_spec_path(path: str)
Parse a table_spec path into its component segments.
table_spec_path
table_spec_path(project: str, location: str, dataset: str, table_spec: str)
Return a fully-qualified table_spec string.
undeploy_model
undeploy_model(request: Optional[google.cloud.automl_v1beta1.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.
Name | Description |
request |
The request object. Request message for AutoMl.UndeployModel. |
name |
Required. Resource name of the model to undeploy. 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 |
| 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 {} . |
update_column_spec
update_column_spec(request: Optional[google.cloud.automl_v1beta1.types.service.UpdateColumnSpecRequest] = None, *, column_spec: Optional[google.cloud.automl_v1beta1.types.column_spec.ColumnSpec] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Updates a column spec.
Name | Description |
request |
The request object. Request message for AutoMl.UpdateColumnSpec |
column_spec |
Required. The column spec which replaces the resource on the server. 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 |
| A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were given on import . Used by: - Tables |
update_dataset
update_dataset(request: Optional[google.cloud.automl_v1beta1.types.service.UpdateDatasetRequest] = None, *, dataset: Optional[google.cloud.automl_v1beta1.types.dataset.Dataset] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Updates a dataset.
Name | Description |
request |
The request object. Request message for AutoMl.UpdateDataset |
dataset |
Required. The dataset which replaces the resource on the server. 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 |
| A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated. |
update_table_spec
update_table_spec(request: Optional[google.cloud.automl_v1beta1.types.service.UpdateTableSpecRequest] = None, *, table_spec: Optional[google.cloud.automl_v1beta1.types.table_spec.TableSpec] = None, retry: google.api_core.retry.Retry = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Updates a table spec.
Name | Description |
request |
The request object. Request message for AutoMl.UpdateTableSpec |
table_spec |
Required. The table spec which replaces the resource on the server. 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 |
| A specification of a relational table. The table's schema is represented via its child column specs. It is pre-populated as part of ImportData by schema inference algorithm, the version of which is a required parameter of ImportData InputConfig. Note: While working with a table, at times the schema may be inconsistent with the data in the table (e.g. string in a FLOAT64 column). The consistency validation is done upon creation of a model. Used by: - Tables |