Class AutoMlAsyncClient (2.7.3)

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.

Returns the transport used by the client instance.

Instantiates the auto ml client.

Returns a fully-qualified annotation_spec string.

Returns a fully-qualified billing_account string.

Returns a fully-qualified folder string.

Returns a fully-qualified location string.

Returns a fully-qualified organization string.

Returns a fully-qualified project string.

Creates a dataset.

from google.cloud import automl_v1

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

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

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

from google.cloud import automl_v1

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Returns a fully-qualified dataset string.

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

from google.cloud import automl_v1

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

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

from google.cloud import automl_v1

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Deploys a model. If a model is already deployed, deploying it with the same parameters has no effect. Deploying with different parametrs (as e.g. changing xref_node_number) will reset the deployment state without pausing the model's availability.

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

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

from google.cloud import automl_v1

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

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

from google.cloud import automl_v1

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

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

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

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

from google.cloud import automl_v1

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

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

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

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

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

Gets an annotation spec.

from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)

Gets a dataset.

from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)

Gets a model.

from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)

Gets a model evaluation.

from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)

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

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

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

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

Returns an appropriate transport class.

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

For Tables:

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

from google.cloud import automl_v1

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

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Lists datasets in a project.

from google.cloud import automl_v1

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

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

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

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

Lists model evaluations.

from google.cloud import automl_v1

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

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

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

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

Lists models.

from google.cloud import automl_v1

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

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

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

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

Returns a fully-qualified model_evaluation string.

Returns a fully-qualified model string.

Parses a annotation_spec path into its component segments.

Parse a billing_account path into its component segments.

Parse a folder path into its component segments.

Parse a location path into its component segments.

Parse a organization path into its component segments.

Parse a project path into its component segments.

Parses a dataset path into its component segments.

Parses a model_evaluation path into its component segments.

Parses a model path into its component segments.

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

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

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

from google.cloud import automl_v1

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

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

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Updates a dataset.

from google.cloud import automl_v1

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

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

    request = automl_v1.UpdateDatasetRequest(
        dataset=dataset,
    )

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

    # Handle the response
    print(response)

Updates a model.

from google.cloud import automl_v1

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

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

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

    # Handle the response
    print(response)