Class AutoMlAsyncClient (2.8.0)

AutoMlAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

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 dash-case, either of those cases is accepted.

Inheritance

builtins.object > AutoMlAsyncClient

Properties

transport

Returns the transport used by the client instance.

Returns

Type	Description
AutoMlTransport	The transport used by the client instance.

Methods

AutoMlAsyncClient

AutoMlAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Instantiates the 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.

annotation_spec_path

annotation_spec_path(
    project: str, location: str, dataset: str, annotation_spec: str
)

Returns a fully-qualified annotation_spec string.

common_billing_account_path

common_billing_account_path(billing_account: str)

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str)

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str)

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str)

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str)

Returns a fully-qualified project string.

create_dataset

create_dataset(request: Optional[Union[google.cloud.automl_v1.types.service.CreateDatasetRequest, dict]] = None, *, parent: Optional[str] = None, dataset: Optional[google.cloud.automl_v1.types.dataset.Dataset] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a dataset.

from google.cloud import automl_v1

async def sample_create_dataset():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    dataset = automl_v1.Dataset()
    dataset.translation_dataset_metadata.source_language_code = "source_language_code_value"
    dataset.translation_dataset_metadata.target_language_code = "target_language_code_value"

    request = automl_v1.CreateDatasetRequest(
        parent="parent_value",
        dataset=dataset,
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.CreateDatasetRequest, dict]` 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	`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
google.api_core.operation_async.AsyncOperation	An object representing a long-running operation. The result type for the operation will be 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[Union[google.cloud.automl_v1.types.service.CreateModelRequest, dict]] = None, *, parent: Optional[str] = None, model: Optional[google.cloud.automl_v1.types.model.Model] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates a 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.

from google.cloud import automl_v1

async def sample_create_model():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.CreateModelRequest(
        parent="parent_value",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.CreateModelRequest, dict]` 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	`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
google.api_core.operation_async.AsyncOperation	An object representing a long-running operation. The result type for the operation will be Model API proto representing a trained machine learning model.

dataset_path

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

Returns a fully-qualified dataset string.

delete_dataset

delete_dataset(request: Optional[Union[google.cloud.automl_v1.types.service.DeleteDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a 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.

from google.cloud import automl_v1

async def sample_delete_dataset():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.DeleteDatasetRequest(
        name="name_value",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.DeleteDatasetRequest, dict]` 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

google.api_core.operation_async.AsyncOperation

An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } The JSON representation for Empty is empty JSON object {}.

delete_model

delete_model(request: Optional[Union[google.cloud.automl_v1.types.service.DeleteModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes 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.

from google.cloud import automl_v1

async def sample_delete_model():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.DeleteModelRequest(
        name="name_value",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.DeleteModelRequest, dict]` 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

google.api_core.operation_async.AsyncOperation

deploy_model

deploy_model(request: Optional[Union[google.cloud.automl_v1.types.service.DeployModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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.

from google.cloud import automl_v1

async def sample_deploy_model():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.DeployModelRequest(
        name="name_value",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.DeployModelRequest, dict]` 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

google.api_core.operation_async.AsyncOperation

export_data

export_data(request: Optional[Union[google.cloud.automl_v1.types.service.ExportDataRequest, dict]] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.automl_v1.types.io.OutputConfig] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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

from google.cloud import automl_v1

async def sample_export_data():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    output_config = automl_v1.OutputConfig()
    output_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"

    request = automl_v1.ExportDataRequest(
        name="name_value",
        output_config=output_config,
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.ExportDataRequest, dict]` 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	`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

google.api_core.operation_async.AsyncOperation

export_model

export_model(request: Optional[Union[google.cloud.automl_v1.types.service.ExportModelRequest, dict]] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.automl_v1.types.io.ModelExportOutputConfig] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Exports 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.

from google.cloud import automl_v1

async def sample_export_model():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    output_config = automl_v1.ModelExportOutputConfig()
    output_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"

    request = automl_v1.ExportModelRequest(
        name="name_value",
        output_config=output_config,
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.ExportModelRequest, dict]` 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	`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

google.api_core.operation_async.AsyncOperation

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
AutoMlAsyncClient	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
AutoMlAsyncClient	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
AutoMlAsyncClient	The constructed client.

get_annotation_spec

get_annotation_spec(request: Optional[Union[google.cloud.automl_v1.types.service.GetAnnotationSpecRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets an annotation spec.

from google.cloud import automl_v1

async def sample_get_annotation_spec():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.GetAnnotationSpecRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_annotation_spec(request=request)

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.GetAnnotationSpecRequest, dict]` 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
google.cloud.automl_v1.types.AnnotationSpec	A definition of an annotation spec.

get_dataset

get_dataset(request: Optional[Union[google.cloud.automl_v1.types.service.GetDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a dataset.

from google.cloud import automl_v1

async def sample_get_dataset():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.GetDatasetRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_dataset(request=request)

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.GetDatasetRequest, dict]` 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
google.cloud.automl_v1.types.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[Union[google.cloud.automl_v1.types.service.GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a model.

from google.cloud import automl_v1

async def sample_get_model():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.GetModelRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.GetModelRequest, dict]` 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
google.cloud.automl_v1.types.Model	API proto representing a trained machine learning model.

get_model_evaluation

get_model_evaluation(request: Optional[Union[google.cloud.automl_v1.types.service.GetModelEvaluationRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a model evaluation.

from google.cloud import automl_v1

async def sample_get_model_evaluation():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.GetModelEvaluationRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_model_evaluation(request=request)

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.GetModelEvaluationRequest, dict]` 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
google.cloud.automl_v1.types.ModelEvaluation	Evaluation results of a model.

get_mtls_endpoint_and_cert_source

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

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variabel is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameter

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

Exceptions

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

Returns

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

get_transport_class

get_transport_class()

Returns an appropriate transport class.

import_data

import_data(request: Optional[Union[google.cloud.automl_v1.types.service.ImportDataRequest, dict]] = None, *, name: Optional[str] = None, input_config: Optional[google.cloud.automl_v1.types.io.InputConfig] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Imports 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.

from google.cloud import automl_v1

async def sample_import_data():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    input_config = automl_v1.InputConfig()
    input_config.gcs_source.input_uris = ['input_uris_value_1', 'input_uris_value_2']

    request = automl_v1.ImportDataRequest(
        name="name_value",
        input_config=input_config,
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.ImportDataRequest, dict]` 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	`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

google.api_core.operation_async.AsyncOperation

list_datasets

list_datasets(request: Optional[Union[google.cloud.automl_v1.types.service.ListDatasetsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists datasets in a project.

from google.cloud import automl_v1

async def sample_list_datasets():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.ListDatasetsRequest(
        parent="parent_value",
    )

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

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

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.ListDatasetsRequest, dict]` 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
google.cloud.automl_v1.services.auto_ml.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[Union[google.cloud.automl_v1.types.service.ListModelEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists model evaluations.

from google.cloud import automl_v1

async def sample_list_model_evaluations():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.ListModelEvaluationsRequest(
        parent="parent_value",
        filter="filter_value",
    )

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

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

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.ListModelEvaluationsRequest, dict]` 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
google.cloud.automl_v1.services.auto_ml.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[Union[google.cloud.automl_v1.types.service.ListModelsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists models.

from google.cloud import automl_v1

async def sample_list_models():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.ListModelsRequest(
        parent="parent_value",
    )

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

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

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.ListModelsRequest, dict]` 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
google.cloud.automl_v1.services.auto_ml.pagers.ListModelsAsyncPager	Response message for AutoMl.ListModels. Iterating over this object will yield results and resolve additional pages automatically.

model_evaluation_path

model_evaluation_path(
    project: str, location: str, model: str, model_evaluation: str
)

Returns a fully-qualified model_evaluation string.

model_path

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

Returns a fully-qualified model string.

parse_annotation_spec_path

parse_annotation_spec_path(path: str)

Parses a annotation_spec path into its component segments.

parse_common_billing_account_path

parse_common_billing_account_path(path: str)

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str)

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str)

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str)

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str)

Parse a project path into its component segments.

parse_dataset_path

parse_dataset_path(path: str)

Parses a dataset path into its component segments.

parse_model_evaluation_path

parse_model_evaluation_path(path: str)

Parses a model_evaluation path into its component segments.

parse_model_path

parse_model_path(path: str)

Parses a model path into its component segments.

undeploy_model

undeploy_model(request: Optional[Union[google.cloud.automl_v1.types.service.UndeployModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

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.

from google.cloud import automl_v1

async def sample_undeploy_model():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.UndeployModelRequest(
        name="name_value",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.UndeployModelRequest, dict]` 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

google.api_core.operation_async.AsyncOperation

update_dataset

update_dataset(request: Optional[Union[google.cloud.automl_v1.types.service.UpdateDatasetRequest, dict]] = None, *, dataset: Optional[google.cloud.automl_v1.types.dataset.Dataset] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a dataset.

from google.cloud import automl_v1

async def sample_update_dataset():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    dataset = automl_v1.Dataset()
    dataset.translation_dataset_metadata.source_language_code = "source_language_code_value"
    dataset.translation_dataset_metadata.target_language_code = "target_language_code_value"

    request = automl_v1.UpdateDatasetRequest(
        dataset=dataset,
    )

    # Make the request
    response = await client.update_dataset(request=request)

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.UpdateDatasetRequest, dict]` The request object. Request message for AutoMl.UpdateDataset
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	`google.protobuf.field_mask_pb2.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
google.cloud.automl_v1.types.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[Union[google.cloud.automl_v1.types.service.UpdateModelRequest, dict]] = None, *, model: Optional[google.cloud.automl_v1.types.model.Model] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates a model.

from google.cloud import automl_v1

async def sample_update_model():
    # Create a client
    client = automl_v1.AutoMlAsyncClient()

    # Initialize request argument(s)
    request = automl_v1.UpdateModelRequest(
    )

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

    # Handle the response
    print(response)

Parameters

Name	Description
request	`Union[google.cloud.automl_v1.types.UpdateModelRequest, dict]` The request object. Request message for AutoMl.UpdateModel
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	`google.protobuf.field_mask_pb2.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
google.cloud.automl_v1.types.Model	API proto representing a trained machine learning model.