Struct DataLabelingJob 

#[non_exhaustive]
pub struct DataLabelingJob {

    pub name: String,
    pub display_name: String,
    pub datasets: Vec<String>,
    pub annotation_labels: HashMap<String, String>,
    pub labeler_count: i32,
    pub instruction_uri: String,
    pub inputs_schema_uri: String,
    pub inputs: Option<Value>,
    pub state: JobState,
    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: HashMap<String, String>,
    pub specialist_pools: Vec<String>,
    pub encryption_spec: Option<EncryptionSpec>,
    pub active_learning_config: Option<ActiveLearningConfig>,
    /* private fields */
}

Available on crate feature job-service only.

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

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

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: HashMap<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: JobState

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: HashMap<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’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 new() -> Self

pub fn set_name<T: Into<String>>(self, v: T) -> Self

Sets the value of name.

Example

let x = DataLabelingJob::new().set_name("example");

pub fn set_display_name<T: Into<String>>(self, v: T) -> Self

Sets the value of display_name.

Example

let x = DataLabelingJob::new().set_display_name("example");

pub fn set_datasets<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<String>,

Sets the value of datasets.

Example

let x = DataLabelingJob::new().set_datasets(["a", "b", "c"]);

pub fn set_annotation_labels<T, K, V>(self, v: T) -> Self

where T: IntoIterator<Item = (K, V)>, K: Into<String>, V: Into<String>,

Sets the value of annotation_labels.

Example

let x = DataLabelingJob::new().set_annotation_labels([
    ("key0", "abc"),
    ("key1", "xyz"),
]);

pub fn set_labeler_count<T: Into<i32>>(self, v: T) -> Self

Sets the value of labeler_count.

Example

let x = DataLabelingJob::new().set_labeler_count(42);

pub fn set_instruction_uri<T: Into<String>>(self, v: T) -> Self

Sets the value of instruction_uri.

Example

let x = DataLabelingJob::new().set_instruction_uri("example");

pub fn set_inputs_schema_uri<T: Into<String>>(self, v: T) -> Self

Sets the value of inputs_schema_uri.

Example

let x = DataLabelingJob::new().set_inputs_schema_uri("example");

pub fn set_inputs<T>(self, v: T) -> Self

where T: Into<Value>,

Sets the value of inputs.

Example

use wkt::Value;
let x = DataLabelingJob::new().set_inputs(Value::default()/* use setters */);

pub fn set_or_clear_inputs<T>(self, v: Option<T>) -> Self

where T: Into<Value>,

Sets or clears the value of inputs.

Example

use wkt::Value;
let x = DataLabelingJob::new().set_or_clear_inputs(Some(Value::default()/* use setters */));
let x = DataLabelingJob::new().set_or_clear_inputs(None::<Value>);

pub fn set_state<T: Into<JobState>>(self, v: T) -> Self

Sets the value of state.

Example

use google_cloud_aiplatform_v1::model::JobState;
let x0 = DataLabelingJob::new().set_state(JobState::Queued);
let x1 = DataLabelingJob::new().set_state(JobState::Pending);
let x2 = DataLabelingJob::new().set_state(JobState::Running);

pub fn set_labeling_progress<T: Into<i32>>(self, v: T) -> Self

Sets the value of labeling_progress.

Example

let x = DataLabelingJob::new().set_labeling_progress(42);

pub fn set_current_spend<T>(self, v: T) -> Self

where T: Into<Money>,

Sets the value of current_spend.

Example

use gtype::model::Money;
let x = DataLabelingJob::new().set_current_spend(Money::default()/* use setters */);

pub fn set_or_clear_current_spend<T>(self, v: Option<T>) -> Self

where T: Into<Money>,

Sets or clears the value of current_spend.

Example

use gtype::model::Money;
let x = DataLabelingJob::new().set_or_clear_current_spend(Some(Money::default()/* use setters */));
let x = DataLabelingJob::new().set_or_clear_current_spend(None::<Money>);

pub fn set_create_time<T>(self, v: T) -> Self

where T: Into<Timestamp>,

Sets the value of create_time.

Example

use wkt::Timestamp;
let x = DataLabelingJob::new().set_create_time(Timestamp::default()/* use setters */);

pub fn set_or_clear_create_time<T>(self, v: Option<T>) -> Self

where T: Into<Timestamp>,

Sets or clears the value of create_time.

Example

use wkt::Timestamp;
let x = DataLabelingJob::new().set_or_clear_create_time(Some(Timestamp::default()/* use setters */));
let x = DataLabelingJob::new().set_or_clear_create_time(None::<Timestamp>);

pub fn set_update_time<T>(self, v: T) -> Self

where T: Into<Timestamp>,

Sets the value of update_time.

Example

use wkt::Timestamp;
let x = DataLabelingJob::new().set_update_time(Timestamp::default()/* use setters */);

pub fn set_or_clear_update_time<T>(self, v: Option<T>) -> Self

where T: Into<Timestamp>,

Sets or clears the value of update_time.

Example

use wkt::Timestamp;
let x = DataLabelingJob::new().set_or_clear_update_time(Some(Timestamp::default()/* use setters */));
let x = DataLabelingJob::new().set_or_clear_update_time(None::<Timestamp>);

pub fn set_error<T>(self, v: T) -> Self

where T: Into<Status>,

Sets the value of error.

Example

use rpc::model::Status;
let x = DataLabelingJob::new().set_error(Status::default()/* use setters */);

pub fn set_or_clear_error<T>(self, v: Option<T>) -> Self

where T: Into<Status>,

Sets or clears the value of error.

Example

use rpc::model::Status;
let x = DataLabelingJob::new().set_or_clear_error(Some(Status::default()/* use setters */));
let x = DataLabelingJob::new().set_or_clear_error(None::<Status>);

pub fn set_labels<T, K, V>(self, v: T) -> Self

where T: IntoIterator<Item = (K, V)>, K: Into<String>, V: Into<String>,

Sets the value of labels.

Example

let x = DataLabelingJob::new().set_labels([
    ("key0", "abc"),
    ("key1", "xyz"),
]);

pub fn set_specialist_pools<T, V>(self, v: T) -> Self

where T: IntoIterator<Item = V>, V: Into<String>,

Sets the value of specialist_pools.

Example

let x = DataLabelingJob::new().set_specialist_pools(["a", "b", "c"]);

pub fn set_encryption_spec<T>(self, v: T) -> Self

where T: Into<EncryptionSpec>,

Sets the value of encryption_spec.

Example

use google_cloud_aiplatform_v1::model::EncryptionSpec;
let x = DataLabelingJob::new().set_encryption_spec(EncryptionSpec::default()/* use setters */);

pub fn set_or_clear_encryption_spec<T>(self, v: Option<T>) -> Self

where T: Into<EncryptionSpec>,

Sets or clears the value of encryption_spec.

Example

use google_cloud_aiplatform_v1::model::EncryptionSpec;
let x = DataLabelingJob::new().set_or_clear_encryption_spec(Some(EncryptionSpec::default()/* use setters */));
let x = DataLabelingJob::new().set_or_clear_encryption_spec(None::<EncryptionSpec>);

pub fn set_active_learning_config<T>(self, v: T) -> Self

where T: Into<ActiveLearningConfig>,

Sets the value of active_learning_config.

Example

use google_cloud_aiplatform_v1::model::ActiveLearningConfig;
let x = DataLabelingJob::new().set_active_learning_config(ActiveLearningConfig::default()/* use setters */);

pub fn set_or_clear_active_learning_config<T>(self, v: Option<T>) -> Self

where T: Into<ActiveLearningConfig>,

Sets or clears the value of active_learning_config.

Example

use google_cloud_aiplatform_v1::model::ActiveLearningConfig;
let x = DataLabelingJob::new().set_or_clear_active_learning_config(Some(ActiveLearningConfig::default()/* use setters */));
let x = DataLabelingJob::new().set_or_clear_active_learning_config(None::<ActiveLearningConfig>);

Trait Implementations

impl Clone for DataLabelingJob

fn clone(&self) -> DataLabelingJob

Returns a duplicate 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() -> DataLabelingJob

Returns the “default value” for a type.

impl Message for DataLabelingJob

fn typename() -> &'static str

The typename of this message.

impl PartialEq for DataLabelingJob

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for DataLabelingJob