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Command Reference

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Synopsis

Calls the Amazon SageMaker Service CreateLabelingJob API operation.

Syntax

New-SMLabelingJob
-LabelingJobName <String>
-AnnotationConsolidationConfig_AnnotationConsolidationLambdaArn <String>
-AmountInUsd_Cent <Int32>
-DataAttributes_ContentClassifier <String[]>
-AmountInUsd_Dollar <Int32>
-LabelingJobAlgorithmsConfig_InitialActiveLearningModelArn <String>
-OutputConfig_KmsKeyId <String>
-LabelAttributeName <String>
-LabelCategoryConfigS3Uri <String>
-LabelingJobAlgorithmsConfig_LabelingJobAlgorithmSpecificationArn <String>
-S3DataSource_ManifestS3Uri <String>
-HumanTaskConfig_MaxConcurrentTaskCount <Int32>
-StoppingConditions_MaxHumanLabeledObjectCount <Int32>
-StoppingConditions_MaxPercentageOfInputDatasetLabeled <Int32>
-HumanTaskConfig_NumberOfHumanWorkersPerDataObject <Int32>
-HumanTaskConfig_PreHumanTaskLambdaArn <String>
-RoleArn <String>
-OutputConfig_S3OutputPath <String>
-Tag <Tag[]>
-HumanTaskConfig_TaskAvailabilityLifetimeInSecond <Int32>
-HumanTaskConfig_TaskDescription <String>
-HumanTaskConfig_TaskKeyword <String[]>
-HumanTaskConfig_TaskTimeLimitInSecond <Int32>
-HumanTaskConfig_TaskTitle <String>
-AmountInUsd_TenthFractionsOfACent <Int32>
-UiConfig_UiTemplateS3Uri <String>
-LabelingJobResourceConfig_VolumeKmsKeyId <String>
-HumanTaskConfig_WorkteamArn <String>
-Force <SwitchParameter>

Description

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models. You can select your workforce from one of three providers:
  • A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.
  • One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.
  • The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.
You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

Parameters

-AmountInUsd_Cent <Int32>
The fractional portion, in cents, of the amount.
Required?False
Position?Named
Accept pipeline input?False
AliasesHumanTaskConfig_PublicWorkforceTaskPrice_AmountInUsd_Cents
-AmountInUsd_Dollar <Int32>
The whole number of dollars in the amount.
Required?False
Position?Named
Accept pipeline input?False
AliasesHumanTaskConfig_PublicWorkforceTaskPrice_AmountInUsd_Dollars
-AmountInUsd_TenthFractionsOfACent <Int32>
Fractions of a cent, in tenths.
Required?False
Position?Named
Accept pipeline input?False
AliasesHumanTaskConfig_PublicWorkforceTaskPrice_AmountInUsd_TenthFractionsOfACent
-AnnotationConsolidationConfig_AnnotationConsolidationLambdaArn <String>
The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation.For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:
  • Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBoxarn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBoxarn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBoxarn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBoxarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBoxarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox
  • Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassarn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassarn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassarn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass
  • Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentationarn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentationarn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentationarn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentationarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentationarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation
  • Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassarn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassarn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassarn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassarn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassarn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass
For more information, see Annotation Consolidation.
Required?False
Position?Named
Accept pipeline input?False
AliasesHumanTaskConfig_AnnotationConsolidationConfig_AnnotationConsolidationLambdaArn
-DataAttributes_ContentClassifier <String[]>
Declares that your content is free of personally identifiable information or adult content. Amazon SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
Required?False
Position?Named
Accept pipeline input?False
AliasesInputConfig_DataAttributes_ContentClassifiers
-Force <SwitchParameter>
This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution.
Required?False
Position?Named
Accept pipeline input?False
-HumanTaskConfig_MaxConcurrentTaskCount <Int32>
Defines the maximum number of data objects that can be labeled by human workers at the same time. Each object may have more than one worker at one time.
Required?False
Position?Named
Accept pipeline input?False
-HumanTaskConfig_NumberOfHumanWorkersPerDataObject <Int32>
The number of human workers that will label an object.
Required?False
Position?Named
Accept pipeline input?False
-HumanTaskConfig_PreHumanTaskLambdaArn <String>
The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job.For the built-in bounding box, image classification, semantic segmentation, and text classification task types, Amazon SageMaker Ground Truth provides the following Lambda functions:US East (Northern Virginia) (us-east-1):
  • arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox
  • arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass
  • arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation
  • arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass
US East (Ohio) (us-east-2):
  • arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox
  • arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass
  • arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation
  • arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass
US West (Oregon) (us-west-2):
  • arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox
  • arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass
  • arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation
  • arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass
EU (Ireland) (eu-west-1):
  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox
  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass
  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation
  • arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass
Asia Pacific (Tokyo) (ap-northeast-1):
  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox
  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass
  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation
  • arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass
Asia Pacific (Sydney) (ap-southeast-1):
  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox
  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass
  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation
  • arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass
Required?False
Position?Named
Accept pipeline input?False
-HumanTaskConfig_TaskAvailabilityLifetimeInSecond <Int32>
The length of time that a task remains available for labelling by human workers.
Required?False
Position?Named
Accept pipeline input?False
AliasesHumanTaskConfig_TaskAvailabilityLifetimeInSeconds
-HumanTaskConfig_TaskDescription <String>
A description of the task for your human workers.
Required?False
Position?Named
Accept pipeline input?False
-HumanTaskConfig_TaskKeyword <String[]>
Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
Required?False
Position?Named
Accept pipeline input?False
AliasesHumanTaskConfig_TaskKeywords
-HumanTaskConfig_TaskTimeLimitInSecond <Int32>
The amount of time that a worker has to complete a task.
Required?False
Position?Named
Accept pipeline input?False
AliasesHumanTaskConfig_TaskTimeLimitInSeconds
-HumanTaskConfig_TaskTitle <String>
A title for the task for your human workers.
Required?False
Position?Named
Accept pipeline input?False
-HumanTaskConfig_WorkteamArn <String>
The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
Required?False
Position?Named
Accept pipeline input?False
-LabelAttributeName <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 name can't end with "-metadata". If you are running a semantic segmentation labeling job, the attribute name must end with "-ref". If you are running any other kind of labeling job, the attribute name must not end with "-ref".
Required?False
Position?Named
Accept pipeline input?False
-LabelCategoryConfigS3Uri <String>
The S3 URL of the file that defines the categories used to label the data objects.The file is a JSON structure in the following format:{ "document-version": "2018-11-28" "labels": [ { "label": "label 1" }, { "label": "label 2" }, ... { "label": "label n" } ]}
Required?False
Position?Named
Accept pipeline input?False
-LabelingJobAlgorithmsConfig_InitialActiveLearningModelArn <String>
At the end of an auto-label job Amazon SageMaker Ground Truth sends the Amazon Resource Nam (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here.
Required?False
Position?Named
Accept pipeline input?False
-LabelingJobAlgorithmsConfig_LabelingJobAlgorithmSpecificationArn <String>
Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:
  • Image classificationarn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification
  • Text classificationarn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification
  • Object detectionarn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection
Required?False
Position?Named
Accept pipeline input?False
-LabelingJobName <String>
The name of the labeling job. This name is used to identify the job in a list of labeling jobs.
Required?False
Position?1
Accept pipeline input?True (ByValue, )
-LabelingJobResourceConfig_VolumeKmsKeyId <String>
The AWS Key Management Service key ID for the key used to encrypt the output data, if any.
Required?False
Position?Named
Accept pipeline input?False
AliasesLabelingJobAlgorithmsConfig_LabelingJobResourceConfig_VolumeKmsKeyId
-OutputConfig_KmsKeyId <String>
The AWS Key Management Service ID of the key used to encrypt the output data, if any.If you use a KMS key ID or an alias of your master key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS-managed keys for LabelingJobOutputConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.The KMS key policy must grant permission to the IAM role that you specify in your CreateLabelingJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.
Required?False
Position?Named
Accept pipeline input?False
-OutputConfig_S3OutputPath <String>
The Amazon S3 location to write output data.
Required?False
Position?Named
Accept pipeline input?False
-RoleArn <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.
Required?False
Position?Named
Accept pipeline input?False
-S3DataSource_ManifestS3Uri <String>
The Amazon S3 location of the manifest file that describes the input data objects.
Required?False
Position?Named
Accept pipeline input?False
AliasesInputConfig_DataSource_S3DataSource_ManifestS3Uri
-StoppingConditions_MaxHumanLabeledObjectCount <Int32>
The maximum number of objects that can be labeled by human workers.
Required?False
Position?Named
Accept pipeline input?False
-StoppingConditions_MaxPercentageOfInputDatasetLabeled <Int32>
The maximum number of input data objects that should be labeled.
Required?False
Position?Named
Accept pipeline input?False
-Tag <Tag[]>
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Required?False
Position?Named
Accept pipeline input?False
AliasesTags
-UiConfig_UiTemplateS3Uri <String>
The Amazon S3 bucket location of the UI template. For more information about the contents of a UI template, see Creating Your Custom Labeling Task Template.
Required?False
Position?Named
Accept pipeline input?False
AliasesHumanTaskConfig_UiConfig_UiTemplateS3Uri

Common Credential and Region Parameters

-AccessKey <String>
The AWS access key for the user account. This can be a temporary access key if the corresponding session token is supplied to the -SessionToken parameter.
Required? False
Position? Named
Accept pipeline input? False
-Credential <AWSCredentials>
An AWSCredentials object instance containing access and secret key information, and optionally a token for session-based credentials.
Required? False
Position? Named
Accept pipeline input? False
-ProfileLocation <String>

Used to specify the name and location of the ini-format credential file (shared with the AWS CLI and other AWS SDKs)

If this optional parameter is omitted this cmdlet will search the encrypted credential file used by the AWS SDK for .NET and AWS Toolkit for Visual Studio first. If the profile is not found then the cmdlet will search in the ini-format credential file at the default location: (user's home directory)\.aws\credentials. Note that the encrypted credential file is not supported on all platforms. It will be skipped when searching for profiles on Windows Nano Server, Mac, and Linux platforms.

If this parameter is specified then this cmdlet will only search the ini-format credential file at the location given.

As the current folder can vary in a shell or during script execution it is advised that you use specify a fully qualified path instead of a relative path.

Required? False
Position? Named
Accept pipeline input? False
-ProfileName <String>
The user-defined name of an AWS credentials or SAML-based role profile containing credential information. The profile is expected to be found in the secure credential file shared with the AWS SDK for .NET and AWS Toolkit for Visual Studio. You can also specify the name of a profile stored in the .ini-format credential file used with the AWS CLI and other AWS SDKs.
Required? False
Position? Named
Accept pipeline input? False
-NetworkCredential <PSCredential>
Used with SAML-based authentication when ProfileName references a SAML role profile. Contains the network credentials to be supplied during authentication with the configured identity provider's endpoint. This parameter is not required if the user's default network identity can or should be used during authentication.
Required? False
Position? Named
Accept pipeline input? False
-SecretKey <String>
The AWS secret key for the user account. This can be a temporary secret key if the corresponding session token is supplied to the -SessionToken parameter.
Required? False
Position? Named
Accept pipeline input? False
-SessionToken <String>
The session token if the access and secret keys are temporary session-based credentials.
Required? False
Position? Named
Accept pipeline input? False
-Region <String>
The system name of the AWS region in which the operation should be invoked. For example, us-east-1, eu-west-1 etc.
Required? False
Position? Named
Accept pipeline input? False
-EndpointUrl <String>

The endpoint to make the call against.

Note: This parameter is primarily for internal AWS use and is not required/should not be specified for normal usage. The cmdlets normally determine which endpoint to call based on the region specified to the -Region parameter or set as default in the shell (via Set-DefaultAWSRegion). Only specify this parameter if you must direct the call to a specific custom endpoint.

Required? False
Position? Named
Accept pipeline input? False

Inputs

You can pipe a String object to this cmdlet for the LabelingJobName parameter.

Outputs

This cmdlet returns a System.String object. The service call response (type Amazon.SageMaker.Model.CreateLabelingJobResponse) can also be referenced from properties attached to the cmdlet entry in the $AWSHistory stack.

Supported Version

AWS Tools for PowerShell: 2.x.y.z