LabelingJob
sagemaker.services.k8s.aws/v1alpha1
| Type | Link |
|---|---|
| GoDoc | sagemaker-controller/apis/v1alpha1#LabelingJob |
Metadata
| Property | Value |
|---|---|
| Scope | Namespaced |
| Kind | LabelingJob |
| ListKind | LabelingJobList |
| Plural | labelingjobs |
| Singular | labelingjob |
Spec
humanTaskConfig:
annotationConsolidationConfig:
annotationConsolidationLambdaARN: string
maxConcurrentTaskCount: integer
numberOfHumanWorkersPerDataObject: integer
preHumanTaskLambdaARN: string
publicWorkforceTaskPrice:
amountInUsd:
cents: integer
dollars: integer
tenthFractionsOfACent: integer
taskAvailabilityLifetimeInSeconds: integer
taskDescription: string
taskKeywords:
- string
taskTimeLimitInSeconds: integer
taskTitle: string
uiConfig:
humanTaskUIARN: string
uiTemplateS3URI: string
workteamARN: string
inputConfig:
dataAttributes:
contentClassifiers:
- string
dataSource:
s3DataSource:
manifestS3URI: string
snsDataSource:
snsTopicARN: string
labelAttributeName: string
labelCategoryConfigS3URI: string
labelingJobAlgorithmsConfig:
initialActiveLearningModelARN: string
labelingJobAlgorithmSpecificationARN: string
labelingJobResourceConfig:
volumeKMSKeyID: string
vpcConfig:
securityGroupIDs:
- string
subnets:
- string
labelingJobName: string
outputConfig:
kmsKeyID: string
s3OutputPath: string
snsTopicARN: string
roleARN: string
stoppingConditions:
maxHumanLabeledObjectCount: integer
maxPercentageOfInputDatasetLabeled: integer
tags:
- key: string
value: string
| Field | Description |
|---|---|
| humanTaskConfig Required | object Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count). |
| humanTaskConfig.annotationConsolidationConfig Optional | object Configures how labels are consolidated across human workers and processes output data. |
| humanTaskConfig.annotationConsolidationConfig.annotationConsolidationLambdaARN Optional | string |
| humanTaskConfig.maxConcurrentTaskCount Optional | integer |
| humanTaskConfig.numberOfHumanWorkersPerDataObject Optional | integer |
| humanTaskConfig.preHumanTaskLambdaARN Optional | string |
| humanTaskConfig.publicWorkforceTaskPrice Optional | object Defines the amount of money paid to an Amazon Mechanical Turk worker for each task performed. Use one of the following prices for bounding box tasks. Prices are in US dollars and should be based on the complexity of the task; the longer it takes in your initial testing, the more you should offer. * 0.036 * 0.048 * 0.060 * 0.072 * 0.120 * 0.240 * 0.360 * 0.480 * 0.600 * 0.720 * 0.840 * 0.960 * 1.080 * 1.200 Use one of the following prices for image classification, text classification, and custom tasks. Prices are in US dollars. * 0.012 * 0.024 * 0.036 * 0.048 * 0.060 * 0.072 * 0.120 * 0.240 * 0.360 * 0.480 * 0.600 * 0.720 * 0.840 * 0.960 * 1.080 * 1.200 Use one of the following prices for semantic segmentation tasks. Prices are in US dollars. * 0.840 * 0.960 * 1.080 * 1.200 Use one of the following prices for Textract AnalyzeDocument Important Form Key Amazon Augmented AI review tasks. Prices are in US dollars. * 2.400 * 2.280 * 2.160 * 2.040 * 1.920 * 1.800 * 1.680 * 1.560 * 1.440 * 1.320 * 1.200 * 1.080 * 0.960 * 0.840 * 0.720 * 0.600 * 0.480 * 0.360 * 0.240 * 0.120 * 0.072 * 0.060 * 0.048 * 0.036 * 0.024 * 0.012 Use one of the following prices for Rekognition DetectModerationLabels Amazon Augmented AI review tasks. Prices are in US dollars. * 1.200 * 1.080 * 0.960 * 0.840 * 0.720 * 0.600 * 0.480 * 0.360 * 0.240 * 0.120 * 0.072 * 0.060 * 0.048 * 0.036 * 0.024 * 0.012 Use one of the following prices for Amazon Augmented AI custom human review tasks. Prices are in US dollars. * 1.200 * 1.080 * 0.960 * 0.840 * 0.720 * 0.600 * 0.480 * 0.360 * 0.240 * 0.120 * 0.072 * 0.060 * 0.048 * 0.036 * 0.024 * 0.012 |
| humanTaskConfig.publicWorkforceTaskPrice.amountInUsd Optional | object Represents an amount of money in United States dollars. |
| humanTaskConfig.publicWorkforceTaskPrice.amountInUsd.cents Optional | integer |
| humanTaskConfig.publicWorkforceTaskPrice.amountInUsd.dollars Optional | integer |
| humanTaskConfig.publicWorkforceTaskPrice.amountInUsd.tenthFractionsOfACent Optional | integer |
| humanTaskConfig.taskAvailabilityLifetimeInSeconds Optional | integer |
| humanTaskConfig.taskDescription Optional | string |
| humanTaskConfig.taskKeywords Optional | array |
| humanTaskConfig.taskKeywords.[] Required | string |
| humanTaskConfig.taskTitle Optional | string |
| humanTaskConfig.uiConfig Optional | object Provided configuration information for the worker UI for a labeling job. Provide either HumanTaskUiArn or UiTemplateS3Uri. For named entity recognition, 3D point cloud and video frame labeling jobs, use HumanTaskUiArn. For all other Ground Truth built-in task types and custom task types, use UiTemplateS3Uri to specify the location of a worker task template in Amazon S3. |
| humanTaskConfig.uiConfig.humanTaskUIARN Optional | string |
| humanTaskConfig.uiConfig.uiTemplateS3URI Optional | string |
| humanTaskConfig.workteamARN Optional | string |
| inputConfig Required | object 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 an S3DataSource is optional if you use SnsDataSource 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. |
| inputConfig.dataAttributes Optional | object Attributes of the data specified by the customer. Use these to describe the data to be labeled. |
| inputConfig.dataAttributes.contentClassifiers Optional | array |
| inputConfig.dataAttributes.contentClassifiers.[] Required | string |
| inputConfig.dataSource.s3DataSource Optional | object The Amazon S3 location of the input data objects. |
| inputConfig.dataSource.s3DataSource.manifestS3URI Optional | string |
| inputConfig.dataSource.snsDataSource Optional | object An Amazon SNS data source used for streaming labeling jobs. |
| inputConfig.dataSource.snsDataSource.snsTopicARN Optional | string |
| labelAttributeName Required | 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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html), 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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-verification-data.html). Regex Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,126}$ |
| labelCategoryConfigS3URI Optional | 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 (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-point-cloud-label-category-config.html). For named entity recognition jobs, in addition to “labels”, you must provide worker instructions in the label category configuration file using the “instructions” parameter: “instructions”: {“shortInstruction”:" Add header Add Instructions ", “fullInstruction”:" Add additional instructions. "}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-named-entity-recg.html#sms-creating-ner-api). For all other built-in task types (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html) and custom tasks (https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html), your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label_1, label_2,…,label_n with your label categories. { “document-version”: “2018-11-28”, “labels”: [{“label”: “label_1”},{“label”: “label_2”},…{“label”: “label_n”}] } 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 the LabelAttributeName (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName) of the labeling job you want to adjust or verify annotations of. Regex Pattern: `^(https |
| labelingJobAlgorithmsConfig Optional | object Configures the information required to perform automated data labeling. |
| labelingJobAlgorithmsConfig.initialActiveLearningModelARN Optional | string |
| labelingJobAlgorithmsConfig.labelingJobAlgorithmSpecificationARN Optional | string |
| labelingJobAlgorithmsConfig.labelingJobResourceConfig Optional | object Configure encryption on the storage volume attached to the ML compute instance used to run automated data labeling model training and inference. |
| labelingJobAlgorithmsConfig.labelingJobResourceConfig.volumeKMSKeyID Optional | string |
| labelingJobAlgorithmsConfig.labelingJobResourceConfig.vpcConfig Optional | object Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC (https://docs.aws.amazon.com/sagemaker/latest/dg/infrastructure-give-access.html). |
| labelingJobAlgorithmsConfig.labelingJobResourceConfig.vpcConfig.securityGroupIDs Optional | array |
| labelingJobAlgorithmsConfig.labelingJobResourceConfig.vpcConfig.securityGroupIDs.[] Required | string |
| labelingJobAlgorithmsConfig.labelingJobResourceConfig.vpcConfig.subnets.[] Required | string |
| outputConfig Required | object The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any. |
| outputConfig.kmsKeyID Optional | string |
| outputConfig.s3OutputPath Optional | string |
| outputConfig.snsTopicARN Optional | string |
| roleARN Required | 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. Regex Pattern: ^arn:aws[a-z\-]*:iam::\d{12}:role/?[a-zA-Z_0-9+=,.@\-_/]+$ |
| stoppingConditions Optional | object 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. |
| stoppingConditions.maxHumanLabeledObjectCount Optional | integer |
| stoppingConditions.maxPercentageOfInputDatasetLabeled Optional | integer |
| tags Optional | array An array of key/value pairs. For more information, see Using Cost Allocation Tags (https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-what) in the Amazon Web Services Billing and Cost Management User Guide. |
| tags.[] Required | object A tag object that consists of a key and an optional value, used to manage |
| metadata for SageMaker Amazon Web Services resources. |
You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags (https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AddTags.html).
For more information on adding metadata to your Amazon Web Services resources
with tagging, see Tagging Amazon Web Services resources (https://docs.aws.amazon.com/general/latest/gr/aws_tagging.html).
For advice on best practices for managing Amazon Web Services resources with
tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services
Resource Tagging Strategy (https://d1.awsstatic.com/whitepapers/aws-tagging-best-practices.pdf). || tags.[].key
Optional | string
|
| tags.[].value
Optional | string
|
Status
ackResourceMetadata:
arn: string
ownerAccountID: string
region: string
conditions:
- lastTransitionTime: string
message: string
reason: string
status: string
type: string
failureReason: string
jobReferenceCode: string
labelCounters:
failedNonRetryableError: integer
humanLabeled: integer
machineLabeled: integer
totalLabeled: integer
unlabeled: integer
labelingJobOutput:
finalActiveLearningModelARN: string
outputDatasetS3URI: string
labelingJobStatus: string
| Field | Description |
|---|---|
| ackResourceMetadata Optional | object All CRs managed by ACK have a common Status.ACKResourceMetadata memberthat is used to contain resource sync state, account ownership, constructed ARN for the resource |
| ackResourceMetadata.arn Optional | string ARN is the Amazon Resource Name for the resource. This is a globally-unique identifier and is set only by the ACK service controller once the controller has orchestrated the creation of the resource OR when it has verified that an “adopted” resource (a resource where the ARN annotation was set by the Kubernetes user on the CR) exists and matches the supplied CR’s Spec field values. https://github.com/aws/aws-controllers-k8s/issues/270 |
| ackResourceMetadata.ownerAccountID Required | string OwnerAccountID is the AWS Account ID of the account that owns the backend AWS service API resource. |
| ackResourceMetadata.region Required | string Region is the AWS region in which the resource exists or will exist. |
| conditions Optional | array All CRs managed by ACK have a common Status.Conditions member thatcontains a collection of ackv1alpha1.Condition objects that describethe various terminal states of the CR and its backend AWS service API resource |
| conditions.[] Required | object Condition is the common struct used by all CRDs managed by ACK service |
| controllers to indicate terminal states of the CR and its backend AWS | |
| service API resource | |
| conditions.[].message Optional | string A human readable message indicating details about the transition. |
| conditions.[].reason Optional | string The reason for the condition’s last transition. |
| conditions.[].status Optional | string Status of the condition, one of True, False, Unknown. |
| conditions.[].type Optional | string Type is the type of the Condition |
| failureReason Optional | string If the job failed, the reason that it failed. |
| jobReferenceCode Optional | string A unique identifier for work done as part of a labeling job. Regex Pattern: ^.+$ |
| labelCounters Optional | object Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn’t be labeled, and the total number of objects labeled. |
| labelCounters.failedNonRetryableError Optional | integer |
| labelCounters.humanLabeled Optional | integer |
| labelCounters.machineLabeled Optional | integer |
| labelCounters.totalLabeled Optional | integer |
| labelCounters.unlabeled Optional | integer |
| labelingJobOutput Optional | object The location of the output produced by the labeling job. |
| labelingJobOutput.finalActiveLearningModelARN Optional | string |
| labelingJobOutput.outputDatasetS3URI Optional | string |
| labelingJobStatus Optional | string The processing status of the labeling job. |