pub struct CreateLabelingJobRequest {
pub human_task_config: HumanTaskConfig,
pub input_config: LabelingJobInputConfig,
pub label_attribute_name: String,
pub label_category_config_s3_uri: Option<String>,
pub labeling_job_algorithms_config: Option<LabelingJobAlgorithmsConfig>,
pub labeling_job_name: String,
pub output_config: LabelingJobOutputConfig,
pub role_arn: String,
pub stopping_conditions: Option<LabelingJobStoppingConditions>,
pub tags: Option<Vec<Tag>>,
}
Fields
human_task_config: HumanTaskConfig
Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
input_config: LabelingJobInputConfig
Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
You must specify at least one of the following: S3DataSource
or SnsDataSource
.
-
Use
SnsDataSource
to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled. -
Use
S3DataSource
to specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSource
is optional if you useSnsDataSource
to create a streaming labeling job.
If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ContentClassifiers
to specify that your data is free of personally identifiable information and adult content.
label_attribute_name: String
The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The LabelAttributeName
must meet the following requirements.
-
The name can't end with "-metadata".
-
If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".
-
Image semantic segmentation (
SemanticSegmentation)
, and adjustment (AdjustmentSemanticSegmentation
) and verification (VerificationSemanticSegmentation
) labeling jobs for this task type. -
Video frame object detection (
VideoObjectDetection
), and adjustment and verification (AdjustmentVideoObjectDetection
) labeling jobs for this task type. -
Video frame object tracking (
VideoObjectTracking
), and adjustment and verification (AdjustmentVideoObjectTracking
) labeling jobs for this task type. -
3D point cloud semantic segmentation (
3DPointCloudSemanticSegmentation
), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation
) labeling jobs for this task type. -
3D point cloud object tracking (
3DPointCloudObjectTracking
), and adjustment and verification (Adjustment3DPointCloudObjectTracking
) labeling jobs for this task type.
-
If you are creating an adjustment or verification labeling job, you must use a different LabelAttributeName
than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels.
label_category_config_s3_uri: Option<String>
The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label1
, label2
,...
,labeln
with your label categories.
{
"document-version": "2018-11-28",
"labels": [{"label": "label1"},{"label": "label2"},...{"label": "labeln"}]
}
Note the following about the label category configuration file:
-
For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.
-
Each label category must be unique, you cannot specify duplicate label categories.
-
If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include
auditLabelAttributeName
in the label category configuration. Use this parameter to enter theLabelAttributeName
of the labeling job you want to adjust or verify annotations of.
labeling_job_algorithms_config: Option<LabelingJobAlgorithmsConfig>
Configures the information required to perform automated data labeling.
labeling_job_name: String
The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an AWS account and region. LabelingJobName
is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
output_config: LabelingJobOutputConfig
The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
role_arn: String
The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
stopping_conditions: Option<LabelingJobStoppingConditions>
A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Trait Implementations
sourceimpl Clone for CreateLabelingJobRequest
impl Clone for CreateLabelingJobRequest
sourcefn clone(&self) -> CreateLabelingJobRequest
fn clone(&self) -> CreateLabelingJobRequest
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Debug for CreateLabelingJobRequest
impl Debug for CreateLabelingJobRequest
sourceimpl Default for CreateLabelingJobRequest
impl Default for CreateLabelingJobRequest
sourcefn default() -> CreateLabelingJobRequest
fn default() -> CreateLabelingJobRequest
Returns the “default value” for a type. Read more
sourceimpl PartialEq<CreateLabelingJobRequest> for CreateLabelingJobRequest
impl PartialEq<CreateLabelingJobRequest> for CreateLabelingJobRequest
sourcefn eq(&self, other: &CreateLabelingJobRequest) -> bool
fn eq(&self, other: &CreateLabelingJobRequest) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &CreateLabelingJobRequest) -> bool
fn ne(&self, other: &CreateLabelingJobRequest) -> bool
This method tests for !=
.
sourceimpl Serialize for CreateLabelingJobRequest
impl Serialize for CreateLabelingJobRequest
impl StructuralPartialEq for CreateLabelingJobRequest
Auto Trait Implementations
impl RefUnwindSafe for CreateLabelingJobRequest
impl Send for CreateLabelingJobRequest
impl Sync for CreateLabelingJobRequest
impl Unpin for CreateLabelingJobRequest
impl UnwindSafe for CreateLabelingJobRequest
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more