Class DataLabelingJob (1.17.1)

Version 1.17.1

• 1.48.0 (latest)
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

DataLabelingJob(mapping=None, *, ignore_unknown_fields=False, **kwargs)

DataLabelingJob is used to trigger a human labeling job on unlabeled data from the following Dataset:

Attributes

Name	Description
name	str Output only. Resource name of the DataLabelingJob.
display_name	str Required. The user-defined name of the DataLabelingJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. Display name of a DataLabelingJob.
datasets	Sequence[str] Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: ``projects/{project}/locations/{location}/datasets/{dataset}``
annotation_labels	Mapping[str, str] Labels to assign to annotations generated by this DataLabelingJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
labeler_count	int Required. Number of labelers to work on each DataItem.
instruction_uri	str Required. The Google Cloud Storage location of the instruction pdf. This pdf is shared with labelers, and provides detailed description on how to label DataItems in Datasets.
inputs_schema_uri	str Required. Points to a YAML file stored on Google Cloud Storage describing the config for a specific type of DataLabelingJob. The schema files that can be used here are found in the https://storage.googleapis.com/google-cloud-aiplatform bucket in the /schema/datalabelingjob/inputs/ folder.
inputs	google.protobuf.struct_pb2.Value Required. Input config parameters for the DataLabelingJob.
state	google.cloud.aiplatform_v1.types.JobState Output only. The detailed state of the job.
labeling_progress	int Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.
current_spend	google.type.money_pb2.Money Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.
create_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this DataLabelingJob was created.
update_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this DataLabelingJob was updated most recently.
error	google.rpc.status_pb2.Status Output only. DataLabelingJob errors. It is only populated when job's state is ``JOB_STATE_FAILED`` or ``JOB_STATE_CANCELLED``.
labels	Mapping[str, str] The labels with user-defined metadata to organize your DataLabelingJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each DataLabelingJob: - "aiplatform.googleapis.com/schema": output only, its value is the inputs_schema's title.
specialist_pools	Sequence[str] The SpecialistPools' resource names associated with this job.
encryption_spec	google.cloud.aiplatform_v1.types.EncryptionSpec Customer-managed encryption key spec for a DataLabelingJob. If set, this DataLabelingJob will be secured by this key. Note: Annotations created in the DataLabelingJob are associated with the EncryptionSpec of the Dataset they are exported to.
active_learning_config	google.cloud.aiplatform_v1.types.ActiveLearningConfig Parameters that configure the active learning pipeline. Active learning will label the data incrementally via several iterations. For every iteration, it will select a batch of data based on the sampling strategy.

Inheritance

builtins.object > proto.message.Message > DataLabelingJob

Classes

AnnotationLabelsEntry

AnnotationLabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The abstract base class for a message.

Parameters

Name	Description
kwargs	dict Keys and values corresponding to the fields of the message.
mapping	Union[dict, `.Message`] A dictionary or message to be used to determine the values for this message.
ignore_unknown_fields	Optional(bool) If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.

LabelsEntry

LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The abstract base class for a message.

Parameters

Name	Description
kwargs	dict Keys and values corresponding to the fields of the message.
mapping	Union[dict, `.Message`] A dictionary or message to be used to determine the values for this message.
ignore_unknown_fields	Optional(bool) If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.