PNG  IHDR;IDATxܻn0K )(pA 7LeG{ §㻢|ذaÆ 6lذaÆ 6lذaÆ 6lom$^yذag5bÆ 6lذaÆ 6lذa{ 6lذaÆ `}HFkm,mӪôô! x|'ܢ˟;E:9&ᶒ}{v]n&6 h_tڠ͵-ҫZ;Z$.Pkž)!o>}leQfJTu іچ\X=8Rن4`Vwl>nG^is"ms$ui?wbs[m6K4O.4%/bC%t Mז -lG6mrz2s%9s@-k9=)kB5\+͂Zsٲ Rn~GRC wIcIn7jJhۛNCS|j08yiHKֶۛkɈ+;SzL/F*\Ԕ#"5m2[S=gnaPeғL lذaÆ 6l^ḵaÆ 6lذaÆ 6lذa; _ذaÆ 6lذaÆ 6lذaÆ RIENDB` package Paws::SageMaker::CreateLabelingJob; use Moose; has HumanTaskConfig => (is => 'ro', isa => 'Paws::SageMaker::HumanTaskConfig', required => 1); has InputConfig => (is => 'ro', isa => 'Paws::SageMaker::LabelingJobInputConfig', required => 1); has LabelAttributeName => (is => 'ro', isa => 'Str', required => 1); has LabelCategoryConfigS3Uri => (is => 'ro', isa => 'Str'); has LabelingJobAlgorithmsConfig => (is => 'ro', isa => 'Paws::SageMaker::LabelingJobAlgorithmsConfig'); has LabelingJobName => (is => 'ro', isa => 'Str', required => 1); has OutputConfig => (is => 'ro', isa => 'Paws::SageMaker::LabelingJobOutputConfig', required => 1); has RoleArn => (is => 'ro', isa => 'Str', required => 1); has StoppingConditions => (is => 'ro', isa => 'Paws::SageMaker::LabelingJobStoppingConditions'); has Tags => (is => 'ro', isa => 'ArrayRef[Paws::SageMaker::Tag]'); use MooseX::ClassAttribute; class_has _api_call => (isa => 'Str', is => 'ro', default => 'CreateLabelingJob'); class_has _returns => (isa => 'Str', is => 'ro', default => 'Paws::SageMaker::CreateLabelingJobResponse'); class_has _result_key => (isa => 'Str', is => 'ro'); 1; ### main pod documentation begin ### =head1 NAME Paws::SageMaker::CreateLabelingJob - Arguments for method CreateLabelingJob on L =head1 DESCRIPTION This class represents the parameters used for calling the method CreateLabelingJob on the L service. Use the attributes of this class as arguments to method CreateLabelingJob. You shouldn't make instances of this class. Each attribute should be used as a named argument in the call to CreateLabelingJob. =head1 SYNOPSIS my $api.sagemaker = Paws->service('SageMaker'); my $CreateLabelingJobResponse = $api . sagemaker->CreateLabelingJob( HumanTaskConfig => { AnnotationConsolidationConfig => { AnnotationConsolidationLambdaArn => 'MyLambdaFunctionArn', # max: 2048 }, NumberOfHumanWorkersPerDataObject => 1, # min: 1, max: 9 PreHumanTaskLambdaArn => 'MyLambdaFunctionArn', # max: 2048 TaskDescription => 'MyTaskDescription', # min: 1, max: 255 TaskTimeLimitInSeconds => 1, # min: 30 TaskTitle => 'MyTaskTitle', # min: 1, max: 128 UiConfig => { HumanTaskUiArn => 'MyHumanTaskUiArn', # max: 1024; OPTIONAL UiTemplateS3Uri => 'MyS3Uri', # max: 1024; OPTIONAL }, WorkteamArn => 'MyWorkteamArn', # max: 256 MaxConcurrentTaskCount => 1, # min: 1, max: 1000; OPTIONAL PublicWorkforceTaskPrice => { AmountInUsd => { Cents => 1, # max: 99; OPTIONAL Dollars => 1, # max: 2; OPTIONAL TenthFractionsOfACent => 1, # max: 9; OPTIONAL }, # OPTIONAL }, # OPTIONAL TaskAvailabilityLifetimeInSeconds => 1, # min: 60; OPTIONAL TaskKeywords => [ 'MyTaskKeyword', ... # min: 1, max: 30 ], # min: 1, max: 5; OPTIONAL }, InputConfig => { DataSource => { S3DataSource => { ManifestS3Uri => 'MyS3Uri', # max: 1024; OPTIONAL }, # OPTIONAL SnsDataSource => { SnsTopicArn => 'MySnsTopicArn', # max: 2048 }, # OPTIONAL }, DataAttributes => { ContentClassifiers => [ 'FreeOfPersonallyIdentifiableInformation', ... # values: FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent ], # max: 256; OPTIONAL }, # OPTIONAL }, LabelAttributeName => 'MyLabelAttributeName', LabelingJobName => 'MyLabelingJobName', OutputConfig => { S3OutputPath => 'MyS3Uri', # max: 1024; OPTIONAL KmsKeyId => 'MyKmsKeyId', # max: 2048; OPTIONAL SnsTopicArn => 'MySnsTopicArn', # max: 2048 }, RoleArn => 'MyRoleArn', LabelCategoryConfigS3Uri => 'MyS3Uri', # OPTIONAL LabelingJobAlgorithmsConfig => { LabelingJobAlgorithmSpecificationArn => 'MyLabelingJobAlgorithmSpecificationArn', # max: 2048 InitialActiveLearningModelArn => 'MyModelArn', # min: 20, max: 2048; OPTIONAL LabelingJobResourceConfig => { VolumeKmsKeyId => 'MyKmsKeyId', # max: 2048; OPTIONAL }, # OPTIONAL }, # OPTIONAL StoppingConditions => { MaxHumanLabeledObjectCount => 1, # min: 1; OPTIONAL MaxPercentageOfInputDatasetLabeled => 1, # min: 1, max: 100; OPTIONAL }, # OPTIONAL Tags => [ { Key => 'MyTagKey', # min: 1, max: 128 Value => 'MyTagValue', # max: 256 }, ... ], # OPTIONAL ); # Results: my $LabelingJobArn = $CreateLabelingJobResponse->LabelingJobArn; # Returns a L object. Values for attributes that are native types (Int, String, Float, etc) can passed as-is (scalar values). Values for complex Types (objects) can be passed as a HashRef. The keys and values of the hashref will be used to instance the underlying object. For the AWS API documentation, see L =head1 ATTRIBUTES =head2 B HumanTaskConfig => L Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count). =head2 B InputConfig => L 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: C or C. =over =item * Use C 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. =item * Use C to specify an input manifest file for both streaming and one-time labeling jobs. Adding an C is optional if you use C to create a streaming labeling job. =back If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use C to specify that your data is free of personally identifiable information and adult content. =head2 B LabelAttributeName => Str 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 C must meet the following requirements. =over =item * The name can't end with "-metadata". =item * 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 I end with "-ref". If the task type you are using is not listed below, the attribute name I end with "-ref". =over =item * Image semantic segmentation (C, and adjustment (C) and verification (C) labeling jobs for this task type. =item * Video frame object detection (C), and adjustment and verification (C) labeling jobs for this task type. =item * Video frame object tracking (C), and adjustment and verification (C) labeling jobs for this task type. =item * 3D point cloud semantic segmentation (C<3DPointCloudSemanticSegmentation>), and adjustment and verification (C) labeling jobs for this task type. =item * 3D point cloud object tracking (C<3DPointCloudObjectTracking>), and adjustment and verification (C) labeling jobs for this task type. =back =back If you are creating an adjustment or verification labeling job, you must use a I C 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). =head2 LabelCategoryConfigS3Uri => Str The S3 URI of the file, referred to as a I