Class AutoMlClient (2.8.3)

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

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 > AutoMlClient

Properties

transport

Returns the transport used by the client instance.

Returns
TypeDescription
AutoMlTransportThe transport used by the client instance.

Methods

AutoMlClient

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

Instantiates the auto ml client.

Parameters
NameDescription
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 google.api_core.client_options.ClientOptions

Custom options for the client. It won't take effect if a transport instance is provided. (1) The api_endpoint property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the api_endpoint property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used.

client_info google.api_core.gapic_v1.client_info.ClientInfo

The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you're developing your own client library.

Exceptions
TypeDescription
google.auth.exceptions.MutualTLSChannelErrorIf mutual TLS transport creation failed for any reason.

__exit__

__exit__(type, value, traceback)

Releases underlying transport's resources.

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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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 google.cloud.automl_v1.types.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
TypeDescription
google.api_core.operation.OperationAn 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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 google.cloud.automl_v1.types.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
TypeDescription
google.api_core.operation.OperationAn 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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
TypeDescription
google.api_core.operation.OperationAn 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); }

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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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
TypeDescription
google.api_core.operation.OperationAn 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); }

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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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
TypeDescription
google.api_core.operation.OperationAn 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); }

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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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 google.cloud.automl_v1.types.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
TypeDescription
google.api_core.operation.OperationAn 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); }

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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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 google.cloud.automl_v1.types.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
TypeDescription
google.api_core.operation.OperationAn 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); }

from_service_account_file

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

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

Parameter
NameDescription
filename str

The path to the service account private key json file.

Returns
TypeDescription
AutoMlClientThe 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
NameDescription
info dict

The service account private key info.

Returns
TypeDescription
AutoMlClientThe 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
NameDescription
filename str

The path to the service account private key json file.

Returns
TypeDescription
AutoMlClientThe 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)
Parameters
NameDescription
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
TypeDescription
google.cloud.automl_v1.types.AnnotationSpecA 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)
Parameters
NameDescription
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
TypeDescription
google.cloud.automl_v1.types.DatasetA 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)
Parameters
NameDescription
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
TypeDescription
google.cloud.automl_v1.types.ModelAPI 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)
Parameters
NameDescription
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
TypeDescription
google.cloud.automl_v1.types.ModelEvaluationEvaluation 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
NameDescription
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
TypeDescription
google.auth.exceptions.MutualTLSChannelErrorIf any errors happen.
Returns
TypeDescription
Tuple[str, Callable[[], Tuple[bytes, bytes]]]returns the API endpoint and the client cert source to use.

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.
# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # Initialize request argument(s)
    input_config = automl_v1.InputConfig()
    input_config.gcs_source.input_uris = ['input_uris_value1', 'input_uris_value2']

    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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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 google.cloud.automl_v1.types.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
TypeDescription
google.api_core.operation.OperationAn 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); }

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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

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

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
NameDescription
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
TypeDescription
google.cloud.automl_v1.services.auto_ml.pagers.ListDatasetsPagerResponse 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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
    for response in page_result:
        print(response)
Parameters
NameDescription
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
TypeDescription
google.cloud.automl_v1.services.auto_ml.pagers.ListModelEvaluationsPagerResponse 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

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

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

    # Handle the response
    for response in page_result:
        print(response)
Parameters
NameDescription
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
TypeDescription
google.cloud.automl_v1.services.auto_ml.pagers.ListModelsPagerResponse 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = operation.result()

    # Handle the response
    print(response)
Parameters
NameDescription
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
TypeDescription
google.api_core.operation.OperationAn 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); }

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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

    # 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 = client.update_dataset(request=request)

    # Handle the response
    print(response)
Parameters
NameDescription
request Union[google.cloud.automl_v1.types.UpdateDatasetRequest, dict]

The request object. Request message for AutoMl.UpdateDataset

dataset google.cloud.automl_v1.types.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
TypeDescription
google.cloud.automl_v1.types.DatasetA 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.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)
Parameters
NameDescription
request Union[google.cloud.automl_v1.types.UpdateModelRequest, dict]

The request object. Request message for AutoMl.UpdateModel

model google.cloud.automl_v1.types.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
TypeDescription
google.cloud.automl_v1.types.ModelAPI proto representing a trained machine learning model.