Class DataLabelingServiceAsyncClient (1.5.2)

Version 1.5.2

• 1.11.1 (latest)
• 1.11.0
• 1.10.5
• 1.9.0
• 1.8.3
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• 1.5.2
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• 1.3.0
• 1.2.4
• 1.1.0
• 1.0.0
• 0.4.2
• 0.3.0
• 0.2.1

DataLabelingServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.datalabeling_v1beta1.services.data_labeling_service.transports.base.DataLabelingServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Service for the AI Platform Data Labeling API.

Properties

transport

Returns the transport used by the client instance.

Returns
Type	Description
DataLabelingServiceTransport	The transport used by the client instance.

Methods

DataLabelingServiceAsyncClient

DataLabelingServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.datalabeling_v1beta1.services.data_labeling_service.transports.base.DataLabelingServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Instantiates the data labeling service client.

Parameters
Name	Description
credentials	Optional[google.auth.credentials.Credentials] The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.
transport	Union[str, .DataLabelingServiceTransport] The transport to use. If set to None, a transport is chosen automatically.
client_options	ClientOptions Custom options for the client. It won't take effect if a transport instance is provided. (1) The api_endpoint property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the api_endpoint property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used.

Exceptions
Type	Description
google.auth.exceptions.MutualTlsChannelError	If mutual TLS transport creation failed for any reason.

annotated_dataset_path

annotated_dataset_path(project: str, dataset: str, annotated_dataset: str)

Returns a fully-qualified annotated_dataset string.

annotation_spec_set_path

annotation_spec_set_path(project: str, annotation_spec_set: str)

Returns a fully-qualified annotation_spec_set string.

common_billing_account_path

common_billing_account_path(billing_account: str)

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str)

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str)

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str)

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str)

Returns a fully-qualified project string.

create_annotation_spec_set

create_annotation_spec_set(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.CreateAnnotationSpecSetRequest, dict]] = None, *, parent: Optional[str] = None, annotation_spec_set: Optional[google.cloud.datalabeling_v1beta1.types.annotation_spec_set.AnnotationSpecSet] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates an annotation spec set by providing a set of labels.

from google.cloud import datalabeling_v1beta1

async def sample_create_annotation_spec_set():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.CreateAnnotationSpecSetRequest(
        parent="parent_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.CreateAnnotationSpecSetRequest, dict] The request object. Request message for CreateAnnotationSpecSet.
parent	str Required. AnnotationSpecSet resource parent, format: projects/{project_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
annotation_spec_set	AnnotationSpecSet Required. Annotation spec set to create. Annotation specs must be included. Only one annotation spec will be accepted for annotation specs with same display_name. This corresponds to the annotation_spec_set field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.AnnotationSpecSet	An AnnotationSpecSet is a collection of label definitions. For example, in image classification tasks, you define a set of possible labels for images as an AnnotationSpecSet. An AnnotationSpecSet is immutable upon creation.

create_dataset

create_dataset(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.CreateDatasetRequest, dict]] = None, *, parent: Optional[str] = None, dataset: Optional[google.cloud.datalabeling_v1beta1.types.dataset.Dataset] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates dataset. If success return a Dataset resource.

from google.cloud import datalabeling_v1beta1

async def sample_create_dataset():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.CreateDatasetRequest(
        parent="parent_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.CreateDatasetRequest, dict] The request object. Request message for CreateDataset.
parent	str Required. Dataset resource parent, format: projects/{project_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
dataset	Dataset Required. The dataset to be created. This corresponds to the dataset field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.Dataset	Dataset is the resource to hold your data. You can request multiple labeling tasks for a dataset while each one will generate an AnnotatedDataset.

create_evaluation_job

create_evaluation_job(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.CreateEvaluationJobRequest, dict]] = None, *, parent: Optional[str] = None, job: Optional[google.cloud.datalabeling_v1beta1.types.evaluation_job.EvaluationJob] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates an evaluation job.

from google.cloud import datalabeling_v1beta1

async def sample_create_evaluation_job():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.CreateEvaluationJobRequest(
        parent="parent_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.CreateEvaluationJobRequest, dict] The request object. Request message for CreateEvaluationJob.
parent	str Required. Evaluation job resource parent. Format: "projects/{project_id}" This corresponds to the parent field on the request instance; if request is provided, this should not be set.
job	EvaluationJob Required. The evaluation job to create. This corresponds to the job field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.EvaluationJob	Defines an evaluation job that runs periodically to generate Evaluations. [Creating an evaluation job](/ml-engine/docs/continuous-evaluation/create-job) is the starting point for using continuous evaluation.

create_instruction

create_instruction(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.CreateInstructionRequest, dict]] = None, *, parent: Optional[str] = None, instruction: Optional[google.cloud.datalabeling_v1beta1.types.instruction.Instruction] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Creates an instruction for how data should be labeled.

from google.cloud import datalabeling_v1beta1

async def sample_create_instruction():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.CreateInstructionRequest(
        parent="parent_value",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.CreateInstructionRequest, dict] The request object. Request message for CreateInstruction.
parent	str Required. Instruction resource parent, format: projects/{project_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
instruction	Instruction Required. Instruction of how to perform the labeling task. This corresponds to the instruction field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.api_core.operation_async.AsyncOperation	An object representing a long-running operation. The result type for the operation will be Instruction Instruction of how to perform the labeling task for human operators. Currently only PDF instruction is supported.

data_item_path

data_item_path(project: str, dataset: str, data_item: str)

Returns a fully-qualified data_item string.

dataset_path

dataset_path(project: str, dataset: str)

Returns a fully-qualified dataset string.

delete_annotated_dataset

delete_annotated_dataset(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.DeleteAnnotatedDatasetRequest, dict]] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes an annotated dataset by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_delete_annotated_dataset():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.DeleteAnnotatedDatasetRequest(
        name="name_value",
    )

    # Make the request
    await client.delete_annotated_dataset(request=request)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.DeleteAnnotatedDatasetRequest, dict] The request object. Request message for DeleteAnnotatedDataset.
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.

delete_annotation_spec_set

delete_annotation_spec_set(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.DeleteAnnotationSpecSetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes an annotation spec set by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_delete_annotation_spec_set():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.DeleteAnnotationSpecSetRequest(
        name="name_value",
    )

    # Make the request
    await client.delete_annotation_spec_set(request=request)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.DeleteAnnotationSpecSetRequest, dict] The request object. Request message for DeleteAnnotationSpecSet.
name	str Required. AnnotationSpec resource name, format: projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}. 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.

delete_dataset

delete_dataset(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.DeleteDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes a dataset by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_delete_dataset():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.DeleteDatasetRequest(
        name="name_value",
    )

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.DeleteDatasetRequest, dict] The request object. Request message for DeleteDataset.
name	str Required. Dataset resource name, format: projects/{project_id}/datasets/{dataset_id} 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.

delete_evaluation_job

delete_evaluation_job(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.DeleteEvaluationJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Stops and deletes an evaluation job.

from google.cloud import datalabeling_v1beta1

async def sample_delete_evaluation_job():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.DeleteEvaluationJobRequest(
        name="name_value",
    )

    # Make the request
    await client.delete_evaluation_job(request=request)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.DeleteEvaluationJobRequest, dict] The request object. Request message DeleteEvaluationJob.
name	str Required. Name of the evaluation job that is going to be deleted. Format: "projects/{project_id}/evaluationJobs/{evaluation_job_id}" 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.

delete_instruction

delete_instruction(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.DeleteInstructionRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Deletes an instruction object by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_delete_instruction():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.DeleteInstructionRequest(
        name="name_value",
    )

    # Make the request
    await client.delete_instruction(request=request)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.DeleteInstructionRequest, dict] The request object. Request message for DeleteInstruction.
name	str Required. Instruction resource name, format: projects/{project_id}/instructions/{instruction_id} 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.

evaluation_job_path

evaluation_job_path(project: str, evaluation_job: str)

Returns a fully-qualified evaluation_job string.

evaluation_path

evaluation_path(project: str, dataset: str, evaluation: str)

Returns a fully-qualified evaluation string.

example_path

example_path(project: str, dataset: str, annotated_dataset: str, example: str)

Returns a fully-qualified example string.

export_data

export_data(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ExportDataRequest, dict]] = None, *, name: Optional[str] = None, annotated_dataset: Optional[str] = None, filter: Optional[str] = None, output_config: Optional[google.cloud.datalabeling_v1beta1.types.dataset.OutputConfig] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Exports data and annotations from dataset.

from google.cloud import datalabeling_v1beta1

async def sample_export_data():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ExportDataRequest(
        name="name_value",
        annotated_dataset="annotated_dataset_value",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ExportDataRequest, dict] The request object. Request message for ExportData API.
name	str Required. Dataset resource name, format: projects/{project_id}/datasets/{dataset_id} This corresponds to the name field on the request instance; if request is provided, this should not be set.
annotated_dataset	str Required. Annotated dataset resource name. DataItem in Dataset and their annotations in specified annotated dataset will be exported. It's in format of projects/{project_id}/datasets/{dataset_id}/annotatedDatasets/ {annotated_dataset_id} This corresponds to the annotated_dataset field on the request instance; if request is provided, this should not be set.
filter	str Optional. Filter is not supported at this moment. This corresponds to the filter field on the request instance; if request is provided, this should not be set.
output_config	OutputConfig Required. Specify the output destination. This corresponds to the output_config field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.api_core.operation_async.AsyncOperation	An object representing a long-running operation. The result type for the operation will be ExportDataOperationResponse Response used for ExportDataset longrunning operation.

from_service_account_file

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

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

Parameter
Name	Description
filename	str The path to the service account private key json file.

Returns
Type	Description
DataLabelingServiceAsyncClient	The constructed client.

from_service_account_info

from_service_account_info(info: dict, *args, **kwargs)

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

Parameter
Name	Description
info	dict The service account private key info.

Returns
Type	Description
DataLabelingServiceAsyncClient	The constructed client.

from_service_account_json

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

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

Parameter
Name	Description
filename	str The path to the service account private key json file.

Returns
Type	Description
DataLabelingServiceAsyncClient	The constructed client.

get_annotated_dataset

get_annotated_dataset(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.GetAnnotatedDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets an annotated dataset by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_get_annotated_dataset():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.GetAnnotatedDatasetRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.GetAnnotatedDatasetRequest, dict] The request object. Request message for GetAnnotatedDataset.
name	str Required. Name of the annotated dataset to get, format: projects/{project_id}/datasets/{dataset_id}/annotatedDatasets/ {annotated_dataset_id} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.AnnotatedDataset	AnnotatedDataset is a set holding annotations for data in a Dataset. Each labeling task will generate an AnnotatedDataset under the Dataset that the task is requested for.

get_annotation_spec_set

get_annotation_spec_set(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.GetAnnotationSpecSetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets an annotation spec set by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_get_annotation_spec_set():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.GetAnnotationSpecSetRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.GetAnnotationSpecSetRequest, dict] The request object. Request message for GetAnnotationSpecSet.
name	str Required. AnnotationSpecSet resource name, format: projects/{project_id}/annotationSpecSets/{annotation_spec_set_id} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.AnnotationSpecSet	An AnnotationSpecSet is a collection of label definitions. For example, in image classification tasks, you define a set of possible labels for images as an AnnotationSpecSet. An AnnotationSpecSet is immutable upon creation.

get_data_item

get_data_item(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.GetDataItemRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a data item in a dataset by resource name. This API can be called after data are imported into dataset.

from google.cloud import datalabeling_v1beta1

async def sample_get_data_item():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.GetDataItemRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.GetDataItemRequest, dict] The request object. Request message for GetDataItem.
name	str Required. The name of the data item to get, format: projects/{project_id}/datasets/{dataset_id}/dataItems/{data_item_id} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.DataItem	DataItem is a piece of data, without annotation. For example, an image.

get_dataset

get_dataset(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.GetDatasetRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets dataset by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_get_dataset():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.GetDatasetRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.GetDatasetRequest, dict] The request object. Request message for GetDataSet.
name	str Required. Dataset resource name, format: projects/{project_id}/datasets/{dataset_id} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.Dataset	Dataset is the resource to hold your data. You can request multiple labeling tasks for a dataset while each one will generate an AnnotatedDataset.

get_evaluation

get_evaluation(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.GetEvaluationRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets an evaluation by resource name (to search, use xref_projects.evaluations.search).

from google.cloud import datalabeling_v1beta1

async def sample_get_evaluation():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.GetEvaluationRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.GetEvaluationRequest, dict] The request object. Request message for GetEvaluation.
name	str Required. Name of the evaluation. Format: "projects/{project_id}/datasets/{dataset_id}/evaluations/{evaluation_id}' This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.Evaluation	Describes an evaluation between a machine learning model's predictions and ground truth labels. Created when an EvaluationJob runs successfully.

get_evaluation_job

get_evaluation_job(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.GetEvaluationJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets an evaluation job by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_get_evaluation_job():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.GetEvaluationJobRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.GetEvaluationJobRequest, dict] The request object. Request message for GetEvaluationJob.
name	str Required. Name of the evaluation job. Format: "projects/{project_id}/evaluationJobs/{evaluation_job_id}" This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.EvaluationJob	Defines an evaluation job that runs periodically to generate Evaluations. [Creating an evaluation job](/ml-engine/docs/continuous-evaluation/create-job) is the starting point for using continuous evaluation.

get_example

get_example(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.GetExampleRequest, dict]] = None, *, name: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets an example by resource name, including both data and annotation.

from google.cloud import datalabeling_v1beta1

async def sample_get_example():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.GetExampleRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.GetExampleRequest, dict] The request object. Request message for GetExample
name	str Required. Name of example, format: projects/{project_id}/datasets/{dataset_id}/annotatedDatasets/ {annotated_dataset_id}/examples/{example_id} This corresponds to the name field on the request instance; if request is provided, this should not be set.
filter	str Optional. An expression for filtering Examples. Filter by annotation_spec.display_name is supported. Format "annotation_spec.display_name = {display_name}" This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.Example	An Example is a piece of data and its annotation. For example, an image with label "house".

get_instruction

get_instruction(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.GetInstructionRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets an instruction by resource name.

from google.cloud import datalabeling_v1beta1

async def sample_get_instruction():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.GetInstructionRequest(
        name="name_value",
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.GetInstructionRequest, dict] The request object. Request message for GetInstruction.
name	str Required. Instruction resource name, format: projects/{project_id}/instructions/{instruction_id} This corresponds to the name field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.Instruction	Instruction of how to perform the labeling task for human operators. Currently only PDF instruction is supported.

get_mtls_endpoint_and_cert_source

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

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

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

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

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

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

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

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

get_transport_class

get_transport_class()

Returns an appropriate transport class.

import_data

import_data(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ImportDataRequest, dict]] = None, *, name: Optional[str] = None, input_config: Optional[google.cloud.datalabeling_v1beta1.types.dataset.InputConfig] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Imports data into dataset based on source locations defined in request. It can be called multiple times for the same dataset. Each dataset can only have one long running operation running on it. For example, no labeling task (also long running operation) can be started while importing is still ongoing. Vice versa.

from google.cloud import datalabeling_v1beta1

async def sample_import_data():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ImportDataRequest(
        name="name_value",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ImportDataRequest, dict] The request object. Request message for ImportData API.
name	str Required. Dataset resource name, format: projects/{project_id}/datasets/{dataset_id} This corresponds to the name field on the request instance; if request is provided, this should not be set.
input_config	InputConfig Required. Specify the input source of the data. This corresponds to the input_config field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.api_core.operation_async.AsyncOperation	An object representing a long-running operation. The result type for the operation will be ImportDataOperationResponse Response used for ImportData longrunning operation.

instruction_path

instruction_path(project: str, instruction: str)

Returns a fully-qualified instruction string.

label_image

label_image(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.LabelImageRequest, dict]] = None, *, parent: Optional[str] = None, basic_config: Optional[google.cloud.datalabeling_v1beta1.types.human_annotation_config.HumanAnnotationConfig] = None, feature: Optional[google.cloud.datalabeling_v1beta1.types.data_labeling_service.LabelImageRequest.Feature] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Starts a labeling task for image. The type of image labeling task is configured by feature in the request.

from google.cloud import datalabeling_v1beta1

async def sample_label_image():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    image_classification_config = datalabeling_v1beta1.ImageClassificationConfig()
    image_classification_config.annotation_spec_set = "annotation_spec_set_value"

    basic_config = datalabeling_v1beta1.HumanAnnotationConfig()
    basic_config.instruction = "instruction_value"
    basic_config.annotated_dataset_display_name = "annotated_dataset_display_name_value"

    request = datalabeling_v1beta1.LabelImageRequest(
        image_classification_config=image_classification_config,
        parent="parent_value",
        basic_config=basic_config,
        feature="SEGMENTATION",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.LabelImageRequest, dict] The request object. Request message for starting an image labeling task.
parent	str Required. Name of the dataset to request labeling task, format: projects/{project_id}/datasets/{dataset_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
basic_config	HumanAnnotationConfig Required. Basic human annotation config. This corresponds to the basic_config field on the request instance; if request is provided, this should not be set.
feature	Feature Required. The type of image labeling task. This corresponds to the feature field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.api_core.operation_async.AsyncOperation	An object representing a long-running operation. The result type for the operation will be AnnotatedDataset AnnotatedDataset is a set holding annotations for data in a Dataset. Each labeling task will generate an AnnotatedDataset under the Dataset that the task is requested for.

label_text

label_text(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.LabelTextRequest, dict]] = None, *, parent: Optional[str] = None, basic_config: Optional[google.cloud.datalabeling_v1beta1.types.human_annotation_config.HumanAnnotationConfig] = None, feature: Optional[google.cloud.datalabeling_v1beta1.types.data_labeling_service.LabelTextRequest.Feature] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Starts a labeling task for text. The type of text labeling task is configured by feature in the request.

from google.cloud import datalabeling_v1beta1

async def sample_label_text():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    text_classification_config = datalabeling_v1beta1.TextClassificationConfig()
    text_classification_config.annotation_spec_set = "annotation_spec_set_value"

    basic_config = datalabeling_v1beta1.HumanAnnotationConfig()
    basic_config.instruction = "instruction_value"
    basic_config.annotated_dataset_display_name = "annotated_dataset_display_name_value"

    request = datalabeling_v1beta1.LabelTextRequest(
        text_classification_config=text_classification_config,
        parent="parent_value",
        basic_config=basic_config,
        feature="TEXT_ENTITY_EXTRACTION",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.LabelTextRequest, dict] The request object. Request message for LabelText.
parent	str Required. Name of the data set to request labeling task, format: projects/{project_id}/datasets/{dataset_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
basic_config	HumanAnnotationConfig Required. Basic human annotation config. This corresponds to the basic_config field on the request instance; if request is provided, this should not be set.
feature	Feature Required. The type of text labeling task. This corresponds to the feature field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.api_core.operation_async.AsyncOperation	An object representing a long-running operation. The result type for the operation will be AnnotatedDataset AnnotatedDataset is a set holding annotations for data in a Dataset. Each labeling task will generate an AnnotatedDataset under the Dataset that the task is requested for.

label_video

label_video(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.LabelVideoRequest, dict]] = None, *, parent: Optional[str] = None, basic_config: Optional[google.cloud.datalabeling_v1beta1.types.human_annotation_config.HumanAnnotationConfig] = None, feature: Optional[google.cloud.datalabeling_v1beta1.types.data_labeling_service.LabelVideoRequest.Feature] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Starts a labeling task for video. The type of video labeling task is configured by feature in the request.

from google.cloud import datalabeling_v1beta1

async def sample_label_video():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    video_classification_config = datalabeling_v1beta1.VideoClassificationConfig()
    video_classification_config.annotation_spec_set_configs.annotation_spec_set = "annotation_spec_set_value"

    basic_config = datalabeling_v1beta1.HumanAnnotationConfig()
    basic_config.instruction = "instruction_value"
    basic_config.annotated_dataset_display_name = "annotated_dataset_display_name_value"

    request = datalabeling_v1beta1.LabelVideoRequest(
        video_classification_config=video_classification_config,
        parent="parent_value",
        basic_config=basic_config,
        feature="EVENT",
    )

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

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

    response = await operation.result()

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.LabelVideoRequest, dict] The request object. Request message for LabelVideo.
parent	str Required. Name of the dataset to request labeling task, format: projects/{project_id}/datasets/{dataset_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
basic_config	HumanAnnotationConfig Required. Basic human annotation config. This corresponds to the basic_config field on the request instance; if request is provided, this should not be set.
feature	Feature Required. The type of video labeling task. This corresponds to the feature field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.api_core.operation_async.AsyncOperation	An object representing a long-running operation. The result type for the operation will be AnnotatedDataset AnnotatedDataset is a set holding annotations for data in a Dataset. Each labeling task will generate an AnnotatedDataset under the Dataset that the task is requested for.

list_annotated_datasets

list_annotated_datasets(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ListAnnotatedDatasetsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists annotated datasets for a dataset. Pagination is supported.

from google.cloud import datalabeling_v1beta1

async def sample_list_annotated_datasets():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ListAnnotatedDatasetsRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ListAnnotatedDatasetsRequest, dict] The request object. Request message for ListAnnotatedDatasets.
parent	str Required. Name of the dataset to list annotated datasets, format: projects/{project_id}/datasets/{dataset_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
filter	str Optional. Filter is not supported at this moment. This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListAnnotatedDatasetsAsyncPager	Results of listing annotated datasets for a dataset. Iterating over this object will yield results and resolve additional pages automatically.

list_annotation_spec_sets

list_annotation_spec_sets(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ListAnnotationSpecSetsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists annotation spec sets for a project. Pagination is supported.

from google.cloud import datalabeling_v1beta1

async def sample_list_annotation_spec_sets():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ListAnnotationSpecSetsRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ListAnnotationSpecSetsRequest, dict] The request object. Request message for ListAnnotationSpecSets.
parent	str Required. Parent of AnnotationSpecSet resource, format: projects/{project_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
filter	str Optional. Filter is not supported at this moment. This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListAnnotationSpecSetsAsyncPager	Results of listing annotation spec set under a project. Iterating over this object will yield results and resolve additional pages automatically.

list_data_items

list_data_items(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ListDataItemsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists data items in a dataset. This API can be called after data are imported into dataset. Pagination is supported.

from google.cloud import datalabeling_v1beta1

async def sample_list_data_items():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ListDataItemsRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ListDataItemsRequest, dict] The request object. Request message for ListDataItems.
parent	str Required. Name of the dataset to list data items, format: projects/{project_id}/datasets/{dataset_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
filter	str Optional. Filter is not supported at this moment. This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListDataItemsAsyncPager	Results of listing data items in a dataset. Iterating over this object will yield results and resolve additional pages automatically.

list_datasets

list_datasets(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ListDatasetsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists datasets under a project. Pagination is supported.

from google.cloud import datalabeling_v1beta1

async def sample_list_datasets():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ListDatasetsRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ListDatasetsRequest, dict] The request object. Request message for ListDataset.
parent	str Required. Dataset resource parent, format: projects/{project_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
filter	str Optional. Filter on dataset is not supported at this moment. This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListDatasetsAsyncPager	Results of listing datasets within a project. Iterating over this object will yield results and resolve additional pages automatically.

list_evaluation_jobs

list_evaluation_jobs(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ListEvaluationJobsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists all evaluation jobs within a project with possible filters. Pagination is supported.

from google.cloud import datalabeling_v1beta1

async def sample_list_evaluation_jobs():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ListEvaluationJobsRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ListEvaluationJobsRequest, dict] The request object. Request message for ListEvaluationJobs.
parent	str Required. Evaluation job resource parent. Format: "projects/{project_id}" This corresponds to the parent field on the request instance; if request is provided, this should not be set.
filter	str Optional. You can filter the jobs to list by model_id (also known as model_name, as described in EvaluationJob.modelVersion) or by evaluation job state (as described in EvaluationJob.state). To filter by both criteria, use the AND operator or the OR operator. For example, you can use the following string for your filter: "evaluation\ job.model_id = {model_name} AND evaluation\ job.state = {evaluation_job_state}" This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListEvaluationJobsAsyncPager	Results for listing evaluation jobs. Iterating over this object will yield results and resolve additional pages automatically.

list_examples

list_examples(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ListExamplesRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists examples in an annotated dataset. Pagination is supported.

from google.cloud import datalabeling_v1beta1

async def sample_list_examples():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ListExamplesRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ListExamplesRequest, dict] The request object. Request message for ListExamples.
parent	str Required. Example resource parent. This corresponds to the parent field on the request instance; if request is provided, this should not be set.
filter	str Optional. An expression for filtering Examples. For annotated datasets that have annotation spec set, filter by annotation_spec.display_name is supported. Format "annotation_spec.display_name = {display_name}" This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListExamplesAsyncPager	Results of listing Examples in and annotated dataset. Iterating over this object will yield results and resolve additional pages automatically.

list_instructions

list_instructions(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ListInstructionsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists instructions for a project. Pagination is supported.

from google.cloud import datalabeling_v1beta1

async def sample_list_instructions():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ListInstructionsRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ListInstructionsRequest, dict] The request object. Request message for ListInstructions.
parent	str Required. Instruction resource parent, format: projects/{project_id} This corresponds to the parent field on the request instance; if request is provided, this should not be set.
filter	str Optional. Filter is not supported at this moment. This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListInstructionsAsyncPager	Results of listing instructions under a project. Iterating over this object will yield results and resolve additional pages automatically.

parse_annotated_dataset_path

parse_annotated_dataset_path(path: str)

Parses a annotated_dataset path into its component segments.

parse_annotation_spec_set_path

parse_annotation_spec_set_path(path: str)

Parses a annotation_spec_set path into its component segments.

parse_common_billing_account_path

parse_common_billing_account_path(path: str)

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str)

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str)

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str)

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str)

Parse a project path into its component segments.

parse_data_item_path

parse_data_item_path(path: str)

Parses a data_item path into its component segments.

parse_dataset_path

parse_dataset_path(path: str)

Parses a dataset path into its component segments.

parse_evaluation_job_path

parse_evaluation_job_path(path: str)

Parses a evaluation_job path into its component segments.

parse_evaluation_path

parse_evaluation_path(path: str)

Parses a evaluation path into its component segments.

parse_example_path

parse_example_path(path: str)

Parses a example path into its component segments.

parse_instruction_path

parse_instruction_path(path: str)

Parses a instruction path into its component segments.

pause_evaluation_job

pause_evaluation_job(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.PauseEvaluationJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Pauses an evaluation job. Pausing an evaluation job that is already in a PAUSED state is a no-op.

from google.cloud import datalabeling_v1beta1

async def sample_pause_evaluation_job():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.PauseEvaluationJobRequest(
        name="name_value",
    )

    # Make the request
    await client.pause_evaluation_job(request=request)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.PauseEvaluationJobRequest, dict] The request object. Request message for PauseEvaluationJob.
name	str Required. Name of the evaluation job that is going to be paused. Format: "projects/{project_id}/evaluationJobs/{evaluation_job_id}" 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.

resume_evaluation_job

resume_evaluation_job(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.ResumeEvaluationJobRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Resumes a paused evaluation job. A deleted evaluation job can't be resumed. Resuming a running or scheduled evaluation job is a no-op.

from google.cloud import datalabeling_v1beta1

async def sample_resume_evaluation_job():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.ResumeEvaluationJobRequest(
        name="name_value",
    )

    # Make the request
    await client.resume_evaluation_job(request=request)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.ResumeEvaluationJobRequest, dict] The request object. Request message ResumeEvaluationJob.
name	str Required. Name of the evaluation job that is going to be resumed. Format: "projects/{project_id}/evaluationJobs/{evaluation_job_id}" 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.

search_evaluations

search_evaluations(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.SearchEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, filter: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Searches xref_evaluations within a project.

from google.cloud import datalabeling_v1beta1

async def sample_search_evaluations():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.SearchEvaluationsRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.SearchEvaluationsRequest, dict] The request object. Request message for SearchEvaluation.
parent	str Required. Evaluation search parent (project ID). Format: "projects/{project_id}" This corresponds to the parent field on the request instance; if request is provided, this should not be set.
filter	str Optional. To search evaluations, you can filter by the following: - evaluation_job.evaluation_job_id (the last part of EvaluationJob.name) - evaluation_job.model_id (the {model_name} portion of EvaluationJob.modelVersion) - evaluation_job.evaluation_job_run_time_start (Minimum threshold for the evaluationJobRunTime that created the evaluation) - evaluation_job.evaluation_job_run_time_end (Maximum threshold for the evaluationJobRunTime that created the evaluation) - evaluation_job.job_state (EvaluationJob.state) - annotation_spec.display_name (the Evaluation contains a metric for the annotation spec with this displayName) To filter by multiple critiera, use the AND operator or the OR operator. The following examples shows a string that filters by several critiera: "evaluation\ job.evaluation_job_id = {evaluation_job_id} AND evaluation\ job.model_id = {model_name} AND evaluation\ job.evaluation_job_run_time_start = {timestamp_1} AND evaluation\ job.evaluation_job_run_time_end = {timestamp_2} AND annotation_spec.display_name = {display_name}" This corresponds to the filter field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.SearchEvaluationsAsyncPager	Results of searching evaluations. Iterating over this object will yield results and resolve additional pages automatically.

search_example_comparisons

search_example_comparisons(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.SearchExampleComparisonsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Searches example comparisons from an evaluation. The return format is a list of example comparisons that show ground truth and prediction(s) for a single input. Search by providing an evaluation ID.

from google.cloud import datalabeling_v1beta1

async def sample_search_example_comparisons():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.SearchExampleComparisonsRequest(
        parent="parent_value",
    )

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

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

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.SearchExampleComparisonsRequest, dict] The request object. Request message of SearchExampleComparisons.
parent	str Required. Name of the Evaluation resource to search for example comparisons from. Format: "projects/{project_id}/datasets/{dataset_id}/evaluations/{evaluation_id}" This corresponds to the parent field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.SearchExampleComparisonsAsyncPager	Results of searching example comparisons. Iterating over this object will yield results and resolve additional pages automatically.

update_evaluation_job

update_evaluation_job(request: Optional[Union[google.cloud.datalabeling_v1beta1.types.data_labeling_service.UpdateEvaluationJobRequest, dict]] = None, *, evaluation_job: Optional[google.cloud.datalabeling_v1beta1.types.evaluation_job.EvaluationJob] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Updates an evaluation job. You can only update certain fields of the job's xref_EvaluationJobConfig: humanAnnotationConfig.instruction, exampleCount, and exampleSamplePercentage.

If you want to change any other aspect of the evaluation job, you must delete the job and create a new one.

from google.cloud import datalabeling_v1beta1

async def sample_update_evaluation_job():
    # Create a client
    client = datalabeling_v1beta1.DataLabelingServiceAsyncClient()

    # Initialize request argument(s)
    request = datalabeling_v1beta1.UpdateEvaluationJobRequest(
    )

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

    # Handle the response
    print(response)

Parameters
Name	Description
request	Union[google.cloud.datalabeling_v1beta1.types.UpdateEvaluationJobRequest, dict] The request object. Request message for UpdateEvaluationJob.
evaluation_job	EvaluationJob Required. Evaluation job that is going to be updated. This corresponds to the evaluation_job field on the request instance; if request is provided, this should not be set.
update_mask	google.protobuf.field_mask_pb2.FieldMask Optional. Mask for which fields to update. You can only provide the following fields: - evaluationJobConfig.humanAnnotationConfig.instruction - evaluationJobConfig.exampleCount - evaluationJobConfig.exampleSamplePercentage You can provide more than one of these fields by separating them with commas. This corresponds to the update_mask field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns
Type	Description
google.cloud.datalabeling_v1beta1.types.EvaluationJob	Defines an evaluation job that runs periodically to generate Evaluations. [Creating an evaluation job](/ml-engine/docs/continuous-evaluation/create-job) is the starting point for using continuous evaluation.