Struct google_api_proto::google::cloud::aiplatform::v1::DataLabelingJob

pub struct DataLabelingJob {

    pub name: String,
    pub display_name: String,
    pub datasets: Vec<String>,
    pub annotation_labels: BTreeMap<String, String>,
    pub labeler_count: i32,
    pub instruction_uri: String,
    pub inputs_schema_uri: String,
    pub inputs: Option<Value>,
    pub state: i32,
    pub labeling_progress: i32,
    pub current_spend: Option<Money>,
    pub create_time: Option<Timestamp>,
    pub update_time: Option<Timestamp>,
    pub error: Option<Status>,
    pub labels: BTreeMap<String, String>,
    pub specialist_pools: Vec<String>,
    pub encryption_spec: Option<EncryptionSpec>,
    pub active_learning_config: Option<ActiveLearningConfig>,
}

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

Fields

name: String

Output only. Resource name of the DataLabelingJob.

display_name: String

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: Vec<String>

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

annotation_labels: BTreeMap<String, String>

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: i32

Required. Number of labelers to work on each DataItem.

instruction_uri: String

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: String

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: Option<Value>

Required. Input config parameters for the DataLabelingJob.

state: i32

Output only. The detailed state of the job.

labeling_progress: i32

Output only. Current labeling job progress percentage scaled in interval [0, 100], indicating the percentage of DataItems that has been finished.

current_spend: Option<Money>

Output only. Estimated cost(in US dollars) that the DataLabelingJob has incurred to date.

create_time: Option<Timestamp>

Output only. Timestamp when this DataLabelingJob was created.

update_time: Option<Timestamp>

Output only. Timestamp when this DataLabelingJob was updated most recently.

error: Option<Status>

Output only. DataLabelingJob errors. It is only populated when job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

labels: BTreeMap<String, String>

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][google.cloud.aiplatform.v1.DataLabelingJob.inputs_schema_uri]’s title.

specialist_pools: Vec<String>

The SpecialistPools’ resource names associated with this job.

encryption_spec: Option<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: Option<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.

Implementations

impl DataLabelingJob

pub fn state(&self) -> JobState

Returns the enum value of state, or the default if the field is set to an invalid enum value.

pub fn set_state(&mut self, value: JobState)

Sets state to the provided enum value.

Trait Implementations

impl Clone for DataLabelingJob

fn clone(&self) -> DataLabelingJob

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DataLabelingJob

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for DataLabelingJob

fn default() -> Self

Returns the “default value” for a type.

impl Message for DataLabelingJob

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for DataLabelingJob

fn eq(&self, other: &DataLabelingJob) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for DataLabelingJob