DataLabelingServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.datalabeling_v1beta1.services.data_labeling_service.transports.base.DataLabelingServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)Service for the AI Platform Data Labeling API.
Inheritance
builtins.object > DataLabelingServiceClientProperties
transport
Returns the transport used by the client instance.
| Type | Description |
| DataLabelingServiceTransport | The transport used by the client instance. |
Methods
DataLabelingServiceClient
DataLabelingServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.datalabeling_v1beta1.services.data_labeling_service.transports.base.DataLabelingServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)Instantiates the data labeling service client.
| 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 |
google.api_core.client_options.ClientOptions
Custom options for the client. It won't take effect if a |
| client_info |
google.api_core.gapic_v1.client_info.ClientInfo
The client info used to send a user-agent string along with API requests. If |
| Type | Description |
| google.auth.exceptions.MutualTLSChannelError | If mutual TLS transport creation failed for any reason. |
__exit__
__exit__(type, value, traceback)Releases underlying transport's resources.
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
def sample_create_annotation_spec_set():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.CreateAnnotationSpecSetRequest(
parent="parent_value",
)
# Make the request
response = client.create_annotation_spec_set(request=request)
# Handle the response
print(response)
| 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 |
| annotation_spec_set |
google.cloud.datalabeling_v1beta1.types.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 |
| 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. |
| 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
def sample_create_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.CreateDatasetRequest(
parent="parent_value",
)
# Make the request
response = client.create_dataset(request=request)
# Handle the response
print(response)
| 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 |
| dataset |
google.cloud.datalabeling_v1beta1.types.Dataset
Required. The dataset to be created. This corresponds to the |
| 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. |
| 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
def sample_create_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.CreateEvaluationJobRequest(
parent="parent_value",
)
# Make the request
response = client.create_evaluation_job(request=request)
# Handle the response
print(response)
| 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 |
| job |
google.cloud.datalabeling_v1beta1.types.EvaluationJob
Required. The evaluation job to create. This corresponds to the |
| 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. |
| 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
def sample_create_instruction():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
| 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 |
| instruction |
google.cloud.datalabeling_v1beta1.types.Instruction
Required. Instruction of how to perform the labeling task. This corresponds to the |
| 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. |
| Type | Description |
| google.api_core.operation.Operation | 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
def sample_delete_annotated_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteAnnotatedDatasetRequest(
name="name_value",
)
# Make the request
client.delete_annotated_dataset(request=request)
| 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
def sample_delete_annotation_spec_set():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteAnnotationSpecSetRequest(
name="name_value",
)
# Make the request
client.delete_annotation_spec_set(request=request)
| 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: |
| 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
def sample_delete_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteDatasetRequest(
name="name_value",
)
# Make the request
client.delete_dataset(request=request)
| 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 |
| 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
def sample_delete_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteEvaluationJobRequest(
name="name_value",
)
# Make the request
client.delete_evaluation_job(request=request)
| 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 |
| 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
def sample_delete_instruction():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteInstructionRequest(
name="name_value",
)
# Make the request
client.delete_instruction(request=request)
| 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 |
| 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
def sample_export_data():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
| 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 |
| 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 |
| filter |
str
Optional. Filter is not supported at this moment. This corresponds to the |
| output_config |
google.cloud.datalabeling_v1beta1.types.OutputConfig
Required. Specify the output destination. This corresponds to the |
| 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. |
| Type | Description |
| google.api_core.operation.Operation | 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.
| Name | Description |
| filename |
str
The path to the service account private key json file. |
| Type | Description |
| DataLabelingServiceClient | 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.
| Name | Description |
| info |
dict
The service account private key info. |
| Type | Description |
| DataLabelingServiceClient | 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.
| Name | Description |
| filename |
str
The path to the service account private key json file. |
| Type | Description |
| DataLabelingServiceClient | 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
def sample_get_annotated_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetAnnotatedDatasetRequest(
name="name_value",
)
# Make the request
response = client.get_annotated_dataset(request=request)
# Handle the response
print(response)
| 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 |
| 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. |
| 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
def sample_get_annotation_spec_set():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetAnnotationSpecSetRequest(
name="name_value",
)
# Make the request
response = client.get_annotation_spec_set(request=request)
# Handle the response
print(response)
| 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 |
| 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. |
| 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
def sample_get_data_item():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetDataItemRequest(
name="name_value",
)
# Make the request
response = client.get_data_item(request=request)
# Handle the response
print(response)
| 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 |
| 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. |
| 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
def sample_get_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetDatasetRequest(
name="name_value",
)
# Make the request
response = client.get_dataset(request=request)
# Handle the response
print(response)
| 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 |
| 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. |
| 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
def sample_get_evaluation():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetEvaluationRequest(
name="name_value",
)
# Make the request
response = client.get_evaluation(request=request)
# Handle the response
print(response)
| 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 |
| 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. |
| 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
def sample_get_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetEvaluationJobRequest(
name="name_value",
)
# Make the request
response = client.get_evaluation_job(request=request)
# Handle the response
print(response)
| 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 |
| 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. |
| 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
def sample_get_example():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetExampleRequest(
name="name_value",
)
# Make the request
response = client.get_example(request=request)
# Handle the response
print(response)
| 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 |
| 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 |
| 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. |
| 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
def sample_get_instruction():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetInstructionRequest(
name="name_value",
)
# Make the request
response = client.get_instruction(request=request)
# Handle the response
print(response)
| 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 |
| 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. |
| 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.
| Name | Description |
| client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. Only the |
| Type | Description |
| google.auth.exceptions.MutualTLSChannelError | If any errors happen. |
| Type | Description |
| Tuple[str, Callable[[], Tuple[bytes, bytes]]] | returns the API endpoint and the client cert source to use. |
import_data
import_data(request: Optional[Union[google.cloud.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
def sample_import_data():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
| 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 |
| input_config |
google.cloud.datalabeling_v1beta1.types.InputConfig
Required. Specify the input source of the data. This corresponds to the |
| 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. |
| Type | Description |
| google.api_core.operation.Operation | 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
def sample_label_image():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
| 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 |
| basic_config |
google.cloud.datalabeling_v1beta1.types.HumanAnnotationConfig
Required. Basic human annotation config. This corresponds to the |
| feature |
google.cloud.datalabeling_v1beta1.types.LabelImageRequest.Feature
Required. The type of image labeling task. This corresponds to the |
| 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. |
| Type | Description |
| google.api_core.operation.Operation | 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
def sample_label_text():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
| 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 |
| basic_config |
google.cloud.datalabeling_v1beta1.types.HumanAnnotationConfig
Required. Basic human annotation config. This corresponds to the |
| feature |
google.cloud.datalabeling_v1beta1.types.LabelTextRequest.Feature
Required. The type of text labeling task. This corresponds to the |
| 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. |
| Type | Description |
| google.api_core.operation.Operation | 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
def sample_label_video():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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 = operation.result()
# Handle the response
print(response)
| 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 |
| basic_config |
google.cloud.datalabeling_v1beta1.types.HumanAnnotationConfig
Required. Basic human annotation config. This corresponds to the |
| feature |
google.cloud.datalabeling_v1beta1.types.LabelVideoRequest.Feature
Required. The type of video labeling task. This corresponds to the |
| 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. |
| Type | Description |
| google.api_core.operation.Operation | 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
def sample_list_annotated_datasets():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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
for response in page_result:
print(response)
| 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 |
| filter |
str
Optional. Filter is not supported at this moment. This corresponds to the |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListAnnotatedDatasetsPager | 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
def sample_list_annotation_spec_sets():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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
for response in page_result:
print(response)
| 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 |
| filter |
str
Optional. Filter is not supported at this moment. This corresponds to the |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListAnnotationSpecSetsPager | 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
def sample_list_data_items():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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
for response in page_result:
print(response)
| 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 |
| filter |
str
Optional. Filter is not supported at this moment. This corresponds to the |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListDataItemsPager | 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
def sample_list_datasets():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListDatasetsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_datasets(request=request)
# Handle the response
for response in page_result:
print(response)
| 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 |
| filter |
str
Optional. Filter on dataset is not supported at this moment. This corresponds to the |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListDatasetsPager | 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
def sample_list_evaluation_jobs():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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
for response in page_result:
print(response)
| 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 |
| 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 |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListEvaluationJobsPager | 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
def sample_list_examples():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListExamplesRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_examples(request=request)
# Handle the response
for response in page_result:
print(response)
| 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 |
| 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 |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListExamplesPager | 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
def sample_list_instructions():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListInstructionsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_instructions(request=request)
# Handle the response
for response in page_result:
print(response)
| 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 |
| filter |
str
Optional. Filter is not supported at this moment. This corresponds to the |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListInstructionsPager | 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
def sample_pause_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.PauseEvaluationJobRequest(
name="name_value",
)
# Make the request
client.pause_evaluation_job(request=request)
| 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 |
| 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
def sample_resume_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ResumeEvaluationJobRequest(
name="name_value",
)
# Make the request
client.resume_evaluation_job(request=request)
| 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 |
| 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
def sample_search_evaluations():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.SearchEvaluationsRequest(
parent="parent_value",
)
# Make the request
page_result = client.search_evaluations(request=request)
# Handle the response
for response in page_result:
print(response)
| 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 |
| 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 |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.SearchEvaluationsPager | 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
def sample_search_example_comparisons():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# 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
for response in page_result:
print(response)
| 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 |
| 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. |
| Type | Description |
| google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.SearchExampleComparisonsPager | 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
def sample_update_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.UpdateEvaluationJobRequest(
)
# Make the request
response = client.update_evaluation_job(request=request)
# Handle the response
print(response)
| Name | Description |
| request |
Union[google.cloud.datalabeling_v1beta1.types.UpdateEvaluationJobRequest, dict]
The request object. Request message for UpdateEvaluationJob. |
| evaluation_job |
google.cloud.datalabeling_v1beta1.types.EvaluationJob
Required. Evaluation job that is going to be updated. This corresponds to the |
| update_mask |
google.protobuf.field_mask_pb2.FieldMask
Optional. Mask for which fields to update. You can only provide the following fields: - |
| 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. |
| 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. |