As of January 1, 2020 this library no longer supports Python 2 on the latest released version. Library versions released prior to that date will continue to be available. For more information please visit Python 2 support on Google Cloud.

AutoMl

class google.cloud.automl_v1.services.auto_ml.AutoMlAsyncClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport, typing.Callable[[...], google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport]]] = 'grpc_asyncio', client_options: typing.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>)[source]

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.

Instantiates the auto ml async client.

Parameters

  • 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 (Optional[Union[str,AutoMlTransport,Callable[..., AutoMlTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport to use. If a Callable is given, it will be called with the same set of initialization arguments as used in the AutoMlTransport constructor. If set to None, a transport is chosen automatically.

  • client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –

    Custom options for the client.

    1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

    2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

    3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.

  • client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises

google.auth.exceptions.MutualTlsChannelError – If mutual TLS transport creation failed for any reason.

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

Returns a fully-qualified annotation_spec string.

property api_endpoint

Return the API endpoint used by the client instance.

Returns

The API endpoint used by the client instance.

Return type

str

static common_billing_account_path(billing_account: str) → str

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str

Returns a fully-qualified organization string.

static common_project_path(project: str) → str

Returns a fully-qualified project string.

async 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: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.CreateDatasetRequest, dict]]) – The request object. Request message for [AutoMl.CreateDataset][google.cloud.automl.v1.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_async.AsyncRetry) – 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

An object representing a long-running operation.

The result type for the operation will be 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.

Return type

google.api_core.operation_async.AsyncOperation

async 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: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.CreateModelRequest, dict]]) – The request object. Request message for [AutoMl.CreateModel][google.cloud.automl.v1.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_async.AsyncRetry) – 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

An object representing a long-running operation.

The result type for the operation will be google.cloud.automl_v1.types.Model API proto representing a trained machine learning model.

Return type

google.api_core.operation_async.AsyncOperation

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

Returns a fully-qualified dataset string.

async delete_dataset(request: Optional[Union[google.cloud.automl_v1.types.service.DeleteDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.DeleteDatasetRequest, dict]]) – The request object. Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1.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_async.AsyncRetry) – 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

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);

}

Return type

google.api_core.operation_async.AsyncOperation

async delete_model(request: Optional[Union[google.cloud.automl_v1.types.service.DeleteModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.DeleteModelRequest, dict]]) – The request object. Request message for [AutoMl.DeleteModel][google.cloud.automl.v1.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_async.AsyncRetry) – 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

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);

}

Return type

google.api_core.operation_async.AsyncOperation

async deploy_model(request: Optional[Union[google.cloud.automl_v1.types.service.DeployModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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 [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.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

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

  • request (Optional[Union[google.cloud.automl_v1.types.DeployModelRequest, dict]]) – The request object. Request message for [AutoMl.DeployModel][google.cloud.automl.v1.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_async.AsyncRetry) – 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

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);

}

Return type

google.api_core.operation_async.AsyncOperation

async 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: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.ExportDataRequest, dict]]) – The request object. Request message for [AutoMl.ExportData][google.cloud.automl.v1.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_async.AsyncRetry) – 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

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);

}

Return type

google.api_core.operation_async.AsyncOperation

async 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: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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 [ModelExportOutputConfig][google.cloud.automl.v1.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

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

  • request (Optional[Union[google.cloud.automl_v1.types.ExportModelRequest, dict]]) – The request object. Request message for [AutoMl.ExportModel][google.cloud.automl.v1.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_async.AsyncRetry) – 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

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);

}

Return type

google.api_core.operation_async.AsyncOperation

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

AutoMlAsyncClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

info.

Parameters

  • info (dict) – The service account private key info.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

AutoMlAsyncClient

classmethod from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

AutoMlAsyncClient

async get_annotation_spec(request: Optional[Union[google.cloud.automl_v1.types.service.GetAnnotationSpecRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.annotation_spec.AnnotationSpec[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.GetAnnotationSpecRequest, dict]]) – The request object. Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.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_async.AsyncRetry) – 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

A definition of an annotation spec.

Return type

google.cloud.automl_v1.types.AnnotationSpec

async get_dataset(request: Optional[Union[google.cloud.automl_v1.types.service.GetDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.dataset.Dataset[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.GetDatasetRequest, dict]]) – The request object. Request message for [AutoMl.GetDataset][google.cloud.automl.v1.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_async.AsyncRetry) – 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

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

Return type

google.cloud.automl_v1.types.Dataset

async get_model(request: Optional[Union[google.cloud.automl_v1.types.service.GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.model.Model[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.GetModelRequest, dict]]) – The request object. Request message for [AutoMl.GetModel][google.cloud.automl.v1.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_async.AsyncRetry) – 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

API proto representing a trained machine learning model.

Return type

google.cloud.automl_v1.types.Model

async get_model_evaluation(request: Optional[Union[google.cloud.automl_v1.types.service.GetModelEvaluationRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.model_evaluation.ModelEvaluation[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.GetModelEvaluationRequest, dict]]) – The request object. Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1.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_async.AsyncRetry) – 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

Evaluation results of a model.

Return type

google.cloud.automl_v1.types.ModelEvaluation

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

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

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

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

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

Parameters

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.

Returns

returns the API endpoint and the

client cert source to use.

Return type

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

classmethod get_transport_class(label: Optional[str] = None) → Type[google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport]

Returns an appropriate transport class.

Parameters

label – The name of the desired transport. If none is provided, then the first transport in the registry is used.

Returns

The transport class to use.

async 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: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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

For Tables:

  • A [schema_inference_version][google.cloud.automl.v1.InputConfig.params] 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

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_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 = (await operation).result()

    # Handle the response
    print(response)
Parameters

  • request (Optional[Union[google.cloud.automl_v1.types.ImportDataRequest, dict]]) – The request object. Request message for [AutoMl.ImportData][google.cloud.automl.v1.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_async.AsyncRetry) – 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

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);

}

Return type

google.api_core.operation_async.AsyncOperation

async list_datasets(request: Optional[Union[google.cloud.automl_v1.types.service.ListDatasetsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.services.auto_ml.pagers.ListDatasetsAsyncPager[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.ListDatasetsRequest, dict]]) – The request object. Request message for [AutoMl.ListDatasets][google.cloud.automl.v1.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_async.AsyncRetry) – 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

Response message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.cloud.automl_v1.services.auto_ml.pagers.ListDatasetsAsyncPager

async list_model_evaluations(request: Optional[Union[google.cloud.automl_v1.types.service.ListModelEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.services.auto_ml.pagers.ListModelEvaluationsAsyncPager[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.ListModelEvaluationsRequest, dict]]) – The request object. Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.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_async.AsyncRetry) – 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

Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.cloud.automl_v1.services.auto_ml.pagers.ListModelEvaluationsAsyncPager

async list_models(request: Optional[Union[google.cloud.automl_v1.types.service.ListModelsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.services.auto_ml.pagers.ListModelsAsyncPager[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.ListModelsRequest, dict]]) – The request object. Request message for [AutoMl.ListModels][google.cloud.automl.v1.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_async.AsyncRetry) – 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

Response message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.cloud.automl_v1.services.auto_ml.pagers.ListModelsAsyncPager

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

Returns a fully-qualified model_evaluation string.

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

Returns a fully-qualified model string.

static parse_annotation_spec_path(path: str) → Dict[str, str]

Parses a annotation_spec path into its component segments.

static parse_common_billing_account_path(path: str) → Dict[str, str]

Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str]

Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str]

Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str]

Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str]

Parse a project path into its component segments.

static parse_dataset_path(path: str) → Dict[str, str]

Parses a dataset path into its component segments.

static parse_model_evaluation_path(path: str) → Dict[str, str]

Parses a model_evaluation path into its component segments.

static parse_model_path(path: str) → Dict[str, str]

Parses a model path into its component segments.

property transport: google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport

Returns the transport used by the client instance.

Returns

The transport used by the client instance.

Return type

AutoMlTransport

async undeploy_model(request: Optional[Union[google.cloud.automl_v1.types.service.UndeployModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation_async.AsyncOperation[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.UndeployModelRequest, dict]]) – The request object. Request message for [AutoMl.UndeployModel][google.cloud.automl.v1.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_async.AsyncRetry) – 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

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);

}

Return type

google.api_core.operation_async.AsyncOperation

property universe_domain: str

Return the universe domain used by the client instance.

Returns

The universe domain used

by the client instance.

Return type

str

async 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: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.dataset.Dataset[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.UpdateDatasetRequest, dict]]) – The request object. Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1.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_async.AsyncRetry) – 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

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

Return type

google.cloud.automl_v1.types.Dataset

async 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: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.model.Model[source]

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

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

  • request (Optional[Union[google.cloud.automl_v1.types.UpdateModelRequest, dict]]) – The request object. Request message for [AutoMl.UpdateModel][google.cloud.automl.v1.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_async.AsyncRetry) – 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

API proto representing a trained machine learning model.

Return type

google.cloud.automl_v1.types.Model

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

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.

Instantiates the auto ml client.

Parameters

  • 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 (Optional[Union[str,AutoMlTransport,Callable[..., AutoMlTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport. If a Callable is given, it will be called with the same set of initialization arguments as used in the AutoMlTransport constructor. If set to None, a transport is chosen automatically.

  • client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –

    Custom options for the client.

    1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

    2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

    3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that the api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.

  • client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises

google.auth.exceptions.MutualTLSChannelError – If mutual TLS transport creation failed for any reason.

__exit__(type, value, traceback)[source]

Releases underlying transport’s resources.

Warning

ONLY use as a context manager if the transport is NOT shared with other clients! Exiting the with block will CLOSE the transport and may cause errors in other clients!

static annotation_spec_path(project: str, location: str, dataset: str, annotation_spec: str) → str[source]

Returns a fully-qualified annotation_spec string.

property api_endpoint

Return the API endpoint used by the client instance.

Returns

The API endpoint used by the client instance.

Return type

str

static common_billing_account_path(billing_account: str) → str[source]

Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str[source]

Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str[source]

Returns a fully-qualified location string.

static common_organization_path(organization: str) → str[source]

Returns a fully-qualified organization string.

static common_project_path(project: str) → str[source]

Returns a fully-qualified project string.

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: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation.Operation[source]

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

  • request (Union[google.cloud.automl_v1.types.CreateDatasetRequest, dict]) – The request object. Request message for [AutoMl.CreateDataset][google.cloud.automl.v1.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

An object representing a long-running operation.

The result type for the operation will be 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.

Return type

google.api_core.operation.Operation

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: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation.Operation[source]

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

  • request (Union[google.cloud.automl_v1.types.CreateModelRequest, dict]) – The request object. Request message for [AutoMl.CreateModel][google.cloud.automl.v1.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

An object representing a long-running operation.

The result type for the operation will be google.cloud.automl_v1.types.Model API proto representing a trained machine learning model.

Return type

google.api_core.operation.Operation

static dataset_path(project: str, location: str, dataset: str) → str[source]

Returns a fully-qualified dataset string.

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

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

  • request (Union[google.cloud.automl_v1.types.DeleteDatasetRequest, dict]) – The request object. Request message for [AutoMl.DeleteDataset][google.cloud.automl.v1.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

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);

}

Return type

google.api_core.operation.Operation

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

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

  • request (Union[google.cloud.automl_v1.types.DeleteModelRequest, dict]) – The request object. Request message for [AutoMl.DeleteModel][google.cloud.automl.v1.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

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);

}

Return type

google.api_core.operation.Operation

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

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 [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.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

  • request (Union[google.cloud.automl_v1.types.DeployModelRequest, dict]) – The request object. Request message for [AutoMl.DeployModel][google.cloud.automl.v1.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

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);

}

Return type

google.api_core.operation.Operation

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: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation.Operation[source]

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

  • request (Union[google.cloud.automl_v1.types.ExportDataRequest, dict]) – The request object. Request message for [AutoMl.ExportData][google.cloud.automl.v1.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

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);

}

Return type

google.api_core.operation.Operation

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: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation.Operation[source]

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 [ModelExportOutputConfig][google.cloud.automl.v1.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

  • request (Union[google.cloud.automl_v1.types.ExportModelRequest, dict]) – The request object. Request message for [AutoMl.ExportModel][google.cloud.automl.v1.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

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);

}

Return type

google.api_core.operation.Operation

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

AutoMlClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]
Creates an instance of this client using the provided credentials

info.

Parameters

  • info (dict) – The service account private key info.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

AutoMlClient

classmethod from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials

file.

Parameters

  • filename (str) – The path to the service account private key json file.

  • args – Additional arguments to pass to the constructor.

  • kwargs – Additional arguments to pass to the constructor.

Returns

The constructed client.

Return type

AutoMlClient

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

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

  • request (Union[google.cloud.automl_v1.types.GetAnnotationSpecRequest, dict]) – The request object. Request message for [AutoMl.GetAnnotationSpec][google.cloud.automl.v1.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

A definition of an annotation spec.

Return type

google.cloud.automl_v1.types.AnnotationSpec

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

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

  • request (Union[google.cloud.automl_v1.types.GetDatasetRequest, dict]) – The request object. Request message for [AutoMl.GetDataset][google.cloud.automl.v1.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

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

Return type

google.cloud.automl_v1.types.Dataset

get_model(request: Optional[Union[google.cloud.automl_v1.types.service.GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.model.Model[source]

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

  • request (Union[google.cloud.automl_v1.types.GetModelRequest, dict]) – The request object. Request message for [AutoMl.GetModel][google.cloud.automl.v1.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

API proto representing a trained machine learning model.

Return type

google.cloud.automl_v1.types.Model

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

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

  • request (Union[google.cloud.automl_v1.types.GetModelEvaluationRequest, dict]) – The request object. Request message for [AutoMl.GetModelEvaluation][google.cloud.automl.v1.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

Evaluation results of a model.

Return type

google.cloud.automl_v1.types.ModelEvaluation

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

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

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

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

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

Parameters

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.

Returns

returns the API endpoint and the

client cert source to use.

Return type

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

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: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.api_core.operation.Operation[source]

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

For Tables:

  • A [schema_inference_version][google.cloud.automl.v1.InputConfig.params] 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

  • request (Union[google.cloud.automl_v1.types.ImportDataRequest, dict]) – The request object. Request message for [AutoMl.ImportData][google.cloud.automl.v1.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

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);

}

Return type

google.api_core.operation.Operation

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

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

  • request (Union[google.cloud.automl_v1.types.ListDatasetsRequest, dict]) – The request object. Request message for [AutoMl.ListDatasets][google.cloud.automl.v1.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

Response message for [AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.cloud.automl_v1.services.auto_ml.pagers.ListDatasetsPager

list_model_evaluations(request: Optional[Union[google.cloud.automl_v1.types.service.ListModelEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.services.auto_ml.pagers.ListModelEvaluationsPager[source]

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

  • request (Union[google.cloud.automl_v1.types.ListModelEvaluationsRequest, dict]) – The request object. Request message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.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

Response message for [AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.cloud.automl_v1.services.auto_ml.pagers.ListModelEvaluationsPager

list_models(request: Optional[Union[google.cloud.automl_v1.types.service.ListModelsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.services.auto_ml.pagers.ListModelsPager[source]

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

  • request (Union[google.cloud.automl_v1.types.ListModelsRequest, dict]) – The request object. Request message for [AutoMl.ListModels][google.cloud.automl.v1.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

Response message for [AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].

Iterating over this object will yield results and resolve additional pages automatically.

Return type

google.cloud.automl_v1.services.auto_ml.pagers.ListModelsPager

static model_evaluation_path(project: str, location: str, model: str, model_evaluation: str) → str[source]

Returns a fully-qualified model_evaluation string.

static model_path(project: str, location: str, model: str) → str[source]

Returns a fully-qualified model string.

static parse_annotation_spec_path(path: str) → Dict[str, str][source]

Parses a annotation_spec path into its component segments.

static parse_common_billing_account_path(path: str) → Dict[str, str][source]

Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str][source]

Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str][source]

Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str][source]

Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str][source]

Parse a project path into its component segments.

static parse_dataset_path(path: str) → Dict[str, str][source]

Parses a dataset path into its component segments.

static parse_model_evaluation_path(path: str) → Dict[str, str][source]

Parses a model_evaluation path into its component segments.

static parse_model_path(path: str) → Dict[str, str][source]

Parses a model path into its component segments.

property transport: google.cloud.automl_v1.services.auto_ml.transports.base.AutoMlTransport

Returns the transport used by the client instance.

Returns

The transport used by the client

instance.

Return type

AutoMlTransport

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

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

  • request (Union[google.cloud.automl_v1.types.UndeployModelRequest, dict]) – The request object. Request message for [AutoMl.UndeployModel][google.cloud.automl.v1.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

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);

}

Return type

google.api_core.operation.Operation

property universe_domain: str

Return the universe domain used by the client instance.

Returns

The universe domain used by the client instance.

Return type

str

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: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.dataset.Dataset[source]

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

  • request (Union[google.cloud.automl_v1.types.UpdateDatasetRequest, dict]) – The request object. Request message for [AutoMl.UpdateDataset][google.cloud.automl.v1.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

A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.

Return type

google.cloud.automl_v1.types.Dataset

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: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → google.cloud.automl_v1.types.model.Model[source]

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

  • request (Union[google.cloud.automl_v1.types.UpdateModelRequest, dict]) – The request object. Request message for [AutoMl.UpdateModel][google.cloud.automl.v1.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

API proto representing a trained machine learning model.

Return type

google.cloud.automl_v1.types.Model

class google.cloud.automl_v1.services.auto_ml.pagers.ListDatasetsAsyncPager(method: Callable[[...], Awaitable[google.cloud.automl_v1.types.service.ListDatasetsResponse]], request: google.cloud.automl_v1.types.service.ListDatasetsRequest, response: google.cloud.automl_v1.types.service.ListDatasetsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_datasets requests.

This class thinly wraps an initial google.cloud.automl_v1.types.ListDatasetsResponse object, and provides an __aiter__ method to iterate through its datasets field.

If there are more pages, the __aiter__ method will make additional ListDatasets requests and continue to iterate through the datasets field on the corresponding responses.

All the usual google.cloud.automl_v1.types.ListDatasetsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters
class google.cloud.automl_v1.services.auto_ml.pagers.ListDatasetsPager(method: Callable[[...], google.cloud.automl_v1.types.service.ListDatasetsResponse], request: google.cloud.automl_v1.types.service.ListDatasetsRequest, response: google.cloud.automl_v1.types.service.ListDatasetsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_datasets requests.

This class thinly wraps an initial google.cloud.automl_v1.types.ListDatasetsResponse object, and provides an __iter__ method to iterate through its datasets field.

If there are more pages, the __iter__ method will make additional ListDatasets requests and continue to iterate through the datasets field on the corresponding responses.

All the usual google.cloud.automl_v1.types.ListDatasetsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters
class google.cloud.automl_v1.services.auto_ml.pagers.ListModelEvaluationsAsyncPager(method: Callable[[...], Awaitable[google.cloud.automl_v1.types.service.ListModelEvaluationsResponse]], request: google.cloud.automl_v1.types.service.ListModelEvaluationsRequest, response: google.cloud.automl_v1.types.service.ListModelEvaluationsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_model_evaluations requests.

This class thinly wraps an initial google.cloud.automl_v1.types.ListModelEvaluationsResponse object, and provides an __aiter__ method to iterate through its model_evaluation field.

If there are more pages, the __aiter__ method will make additional ListModelEvaluations requests and continue to iterate through the model_evaluation field on the corresponding responses.

All the usual google.cloud.automl_v1.types.ListModelEvaluationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters
class google.cloud.automl_v1.services.auto_ml.pagers.ListModelEvaluationsPager(method: Callable[[...], google.cloud.automl_v1.types.service.ListModelEvaluationsResponse], request: google.cloud.automl_v1.types.service.ListModelEvaluationsRequest, response: google.cloud.automl_v1.types.service.ListModelEvaluationsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_model_evaluations requests.

This class thinly wraps an initial google.cloud.automl_v1.types.ListModelEvaluationsResponse object, and provides an __iter__ method to iterate through its model_evaluation field.

If there are more pages, the __iter__ method will make additional ListModelEvaluations requests and continue to iterate through the model_evaluation field on the corresponding responses.

All the usual google.cloud.automl_v1.types.ListModelEvaluationsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters
class google.cloud.automl_v1.services.auto_ml.pagers.ListModelsAsyncPager(method: Callable[[...], Awaitable[google.cloud.automl_v1.types.service.ListModelsResponse]], request: google.cloud.automl_v1.types.service.ListModelsRequest, response: google.cloud.automl_v1.types.service.ListModelsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary_async.AsyncRetry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_models requests.

This class thinly wraps an initial google.cloud.automl_v1.types.ListModelsResponse object, and provides an __aiter__ method to iterate through its model field.

If there are more pages, the __aiter__ method will make additional ListModels requests and continue to iterate through the model field on the corresponding responses.

All the usual google.cloud.automl_v1.types.ListModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiates the pager.

Parameters
class google.cloud.automl_v1.services.auto_ml.pagers.ListModelsPager(method: Callable[[...], google.cloud.automl_v1.types.service.ListModelsResponse], request: google.cloud.automl_v1.types.service.ListModelsRequest, response: google.cloud.automl_v1.types.service.ListModelsResponse, *, retry: Optional[Union[google.api_core.retry.retry_unary.Retry, google.api_core.gapic_v1.method._MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ())[source]

A pager for iterating through list_models requests.

This class thinly wraps an initial google.cloud.automl_v1.types.ListModelsResponse object, and provides an __iter__ method to iterate through its model field.

If there are more pages, the __iter__ method will make additional ListModels requests and continue to iterate through the model field on the corresponding responses.

All the usual google.cloud.automl_v1.types.ListModelsResponse attributes are available on the pager. If multiple requests are made, only the most recent response is retained, and thus used for attribute lookup.

Instantiate the pager.

Parameters