Class DataLabelingJob (1.29.0)

  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 consist of any UTF-8 characters. Display name of a DataLabelingJob.

datasets

MutableSequence[str]
Required. Dataset resource names. Right now we only support labeling from a single Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}

annotation_labels

MutableMapping[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

MutableMapping[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

MutableSequence[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.

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.