#include <CreateLabelingJobRequest.h>

Inheritance diagram for Aws::SageMaker::Model::CreateLabelingJobRequest:

Public Member Functions
	CreateLabelingJobRequest ()

virtual const char *	GetServiceRequestName () const override

Aws::String	SerializePayload () const override

Aws::Http::HeaderValueCollection	GetRequestSpecificHeaders () const override

const Aws::String &	GetLabelingJobName () const

bool	LabelingJobNameHasBeenSet () const

void	SetLabelingJobName (const Aws::String &value)

void	SetLabelingJobName (Aws::String &&value)

void	SetLabelingJobName (const char *value)

CreateLabelingJobRequest &	WithLabelingJobName (const Aws::String &value)

CreateLabelingJobRequest &	WithLabelingJobName (Aws::String &&value)

CreateLabelingJobRequest &	WithLabelingJobName (const char *value)

const Aws::String &	GetLabelAttributeName () const

bool	LabelAttributeNameHasBeenSet () const

void	SetLabelAttributeName (const Aws::String &value)

void	SetLabelAttributeName (Aws::String &&value)

void	SetLabelAttributeName (const char *value)

CreateLabelingJobRequest &	WithLabelAttributeName (const Aws::String &value)

CreateLabelingJobRequest &	WithLabelAttributeName (Aws::String &&value)

CreateLabelingJobRequest &	WithLabelAttributeName (const char *value)

const LabelingJobInputConfig &	GetInputConfig () const

bool	InputConfigHasBeenSet () const

void	SetInputConfig (const LabelingJobInputConfig &value)

void	SetInputConfig (LabelingJobInputConfig &&value)

CreateLabelingJobRequest &	WithInputConfig (const LabelingJobInputConfig &value)

CreateLabelingJobRequest &	WithInputConfig (LabelingJobInputConfig &&value)

const LabelingJobOutputConfig &	GetOutputConfig () const

bool	OutputConfigHasBeenSet () const

void	SetOutputConfig (const LabelingJobOutputConfig &value)

void	SetOutputConfig (LabelingJobOutputConfig &&value)

CreateLabelingJobRequest &	WithOutputConfig (const LabelingJobOutputConfig &value)

CreateLabelingJobRequest &	WithOutputConfig (LabelingJobOutputConfig &&value)

const Aws::String &	GetRoleArn () const

bool	RoleArnHasBeenSet () const

void	SetRoleArn (const Aws::String &value)

void	SetRoleArn (Aws::String &&value)

void	SetRoleArn (const char *value)

CreateLabelingJobRequest &	WithRoleArn (const Aws::String &value)

CreateLabelingJobRequest &	WithRoleArn (Aws::String &&value)

CreateLabelingJobRequest &	WithRoleArn (const char *value)

const Aws::String &	GetLabelCategoryConfigS3Uri () const

bool	LabelCategoryConfigS3UriHasBeenSet () const

void	SetLabelCategoryConfigS3Uri (const Aws::String &value)

void	SetLabelCategoryConfigS3Uri (Aws::String &&value)

void	SetLabelCategoryConfigS3Uri (const char *value)

CreateLabelingJobRequest &	WithLabelCategoryConfigS3Uri (const Aws::String &value)

CreateLabelingJobRequest &	WithLabelCategoryConfigS3Uri (Aws::String &&value)

CreateLabelingJobRequest &	WithLabelCategoryConfigS3Uri (const char *value)

const LabelingJobStoppingConditions &	GetStoppingConditions () const

bool	StoppingConditionsHasBeenSet () const

void	SetStoppingConditions (const LabelingJobStoppingConditions &value)

void	SetStoppingConditions (LabelingJobStoppingConditions &&value)

CreateLabelingJobRequest &	WithStoppingConditions (const LabelingJobStoppingConditions &value)

CreateLabelingJobRequest &	WithStoppingConditions (LabelingJobStoppingConditions &&value)

const LabelingJobAlgorithmsConfig &	GetLabelingJobAlgorithmsConfig () const

bool	LabelingJobAlgorithmsConfigHasBeenSet () const

void	SetLabelingJobAlgorithmsConfig (const LabelingJobAlgorithmsConfig &value)

void	SetLabelingJobAlgorithmsConfig (LabelingJobAlgorithmsConfig &&value)

CreateLabelingJobRequest &	WithLabelingJobAlgorithmsConfig (const LabelingJobAlgorithmsConfig &value)

CreateLabelingJobRequest &	WithLabelingJobAlgorithmsConfig (LabelingJobAlgorithmsConfig &&value)

const HumanTaskConfig &	GetHumanTaskConfig () const

bool	HumanTaskConfigHasBeenSet () const

void	SetHumanTaskConfig (const HumanTaskConfig &value)

void	SetHumanTaskConfig (HumanTaskConfig &&value)

CreateLabelingJobRequest &	WithHumanTaskConfig (const HumanTaskConfig &value)

CreateLabelingJobRequest &	WithHumanTaskConfig (HumanTaskConfig &&value)

const Aws::Vector< Tag > &	GetTags () const

bool	TagsHasBeenSet () const

void	SetTags (const Aws::Vector< Tag > &value)

void	SetTags (Aws::Vector< Tag > &&value)

CreateLabelingJobRequest &	WithTags (const Aws::Vector< Tag > &value)

CreateLabelingJobRequest &	WithTags (Aws::Vector< Tag > &&value)

CreateLabelingJobRequest &	AddTags (const Tag &value)

CreateLabelingJobRequest &	AddTags (Tag &&value)

Public Member Functions inherited from Aws::SageMaker::SageMakerRequest
virtual	~SageMakerRequest ()

void	AddParametersToRequest (Aws::Http::HttpRequest &httpRequest) const

Aws::Http::HeaderValueCollection	GetHeaders () const override

Public Member Functions inherited from Aws::AmazonSerializableWebServiceRequest
	AmazonSerializableWebServiceRequest ()

virtual	~AmazonSerializableWebServiceRequest ()

std::shared_ptr< Aws::IOStream >	GetBody () const override

Public Member Functions inherited from Aws::AmazonWebServiceRequest
	AmazonWebServiceRequest ()

virtual	~AmazonWebServiceRequest ()=default

virtual void	AddQueryStringParameters (Aws::Http::URI &uri) const

virtual void	PutToPresignedUrl (Aws::Http::URI &uri) const

virtual bool	IsStreaming () const

virtual bool	IsEventStreamRequest () const

virtual bool	SignBody () const

virtual bool	IsChunked () const

virtual void	SetRequestSignedHandler (const RequestSignedHandler &handler)

virtual const RequestSignedHandler &	GetRequestSignedHandler () const

const Aws::IOStreamFactory &	GetResponseStreamFactory () const

void	SetResponseStreamFactory (const Aws::IOStreamFactory &factory)

virtual void	SetDataReceivedEventHandler (const Aws::Http::DataReceivedEventHandler &dataReceivedEventHandler)

virtual void	SetDataSentEventHandler (const Aws::Http::DataSentEventHandler &dataSentEventHandler)

virtual void	SetContinueRequestHandler (const Aws::Http::ContinueRequestHandler &continueRequestHandler)

virtual void	SetDataReceivedEventHandler (Aws::Http::DataReceivedEventHandler &&dataReceivedEventHandler)

virtual void	SetDataSentEventHandler (Aws::Http::DataSentEventHandler &&dataSentEventHandler)

virtual void	SetContinueRequestHandler (Aws::Http::ContinueRequestHandler &&continueRequestHandler)

virtual void	SetRequestRetryHandler (const RequestRetryHandler &handler)

virtual void	SetRequestRetryHandler (RequestRetryHandler &&handler)

virtual const Aws::Http::DataReceivedEventHandler &	GetDataReceivedEventHandler () const

virtual const Aws::Http::DataSentEventHandler &	GetDataSentEventHandler () const

virtual const Aws::Http::ContinueRequestHandler &	GetContinueRequestHandler () const

virtual const RequestRetryHandler &	GetRequestRetryHandler () const

virtual bool	ShouldComputeContentMd5 () const

Additional Inherited Members
Protected Member Functions inherited from Aws::AmazonWebServiceRequest
virtual void	DumpBodyToUrl (Aws::Http::URI &uri) const

Detailed Description

Definition at line 28 of file CreateLabelingJobRequest.h.

Constructor & Destructor Documentation

◆ CreateLabelingJobRequest()

Aws::SageMaker::Model::CreateLabelingJobRequest::CreateLabelingJobRequest ( )

Member Function Documentation

◆ AddTags() [1/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::AddTags ( const Tag & value )

inline

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Definition at line 1109 of file CreateLabelingJobRequest.h.

◆ AddTags() [2/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::AddTags ( Tag && value )

inline

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Definition at line 1117 of file CreateLabelingJobRequest.h.

◆ GetHumanTaskConfig()

const HumanTaskConfig& Aws::SageMaker::Model::CreateLabelingJobRequest::GetHumanTaskConfig ( ) const

inline

Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

Definition at line 1022 of file CreateLabelingJobRequest.h.

◆ GetInputConfig()

const LabelingJobInputConfig& Aws::SageMaker::Model::CreateLabelingJobRequest::GetInputConfig ( ) const

inline

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.

Definition at line 423 of file CreateLabelingJobRequest.h.

◆ GetLabelAttributeName()

const Aws::String& Aws::SageMaker::Model::CreateLabelingJobRequest::GetLabelAttributeName ( ) const

inline

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.

Definition at line 151 of file CreateLabelingJobRequest.h.

◆ GetLabelCategoryConfigS3Uri()

const Aws::String& Aws::SageMaker::Model::CreateLabelingJobRequest::GetLabelCategoryConfigS3Uri ( ) const

inline

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 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":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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 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 of the labeling job you want to adjust or verify annotations of.

Definition at line 661 of file CreateLabelingJobRequest.h.

◆ GetLabelingJobAlgorithmsConfig()

const LabelingJobAlgorithmsConfig& Aws::SageMaker::Model::CreateLabelingJobRequest::GetLabelingJobAlgorithmsConfig ( ) const

inline

Configures the information required to perform automated data labeling.

Definition at line 990 of file CreateLabelingJobRequest.h.

◆ GetLabelingJobName()

const Aws::String& Aws::SageMaker::Model::CreateLabelingJobRequest::GetLabelingJobName ( ) const

inline

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 Amazon Web Services 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.

Definition at line 51 of file CreateLabelingJobRequest.h.

◆ GetOutputConfig()

const LabelingJobOutputConfig& Aws::SageMaker::Model::CreateLabelingJobRequest::GetOutputConfig ( ) const

inline

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.

Definition at line 525 of file CreateLabelingJobRequest.h.

◆ GetRequestSpecificHeaders()

Aws::Http::HeaderValueCollection Aws::SageMaker::Model::CreateLabelingJobRequest::GetRequestSpecificHeaders ( ) const

overridevirtual

Reimplemented from Aws::SageMaker::SageMakerRequest.

◆ GetRoleArn()

const Aws::String& Aws::SageMaker::Model::CreateLabelingJobRequest::GetRoleArn ( ) const

inline

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.

Definition at line 564 of file CreateLabelingJobRequest.h.

◆ GetServiceRequestName()

virtual const char* Aws::SageMaker::Model::CreateLabelingJobRequest::GetServiceRequestName ( ) const

inlineoverridevirtual

Implements Aws::AmazonWebServiceRequest.

Definition at line 37 of file CreateLabelingJobRequest.h.

◆ GetStoppingConditions()

const LabelingJobStoppingConditions& Aws::SageMaker::Model::CreateLabelingJobRequest::GetStoppingConditions ( ) const

inline

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.

Definition at line 949 of file CreateLabelingJobRequest.h.

◆ GetTags()

const Aws::Vector<Tag>& Aws::SageMaker::Model::CreateLabelingJobRequest::GetTags ( ) const

inline

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Definition at line 1061 of file CreateLabelingJobRequest.h.

◆ HumanTaskConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::HumanTaskConfigHasBeenSet ( ) const

inline

Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

Definition at line 1028 of file CreateLabelingJobRequest.h.

◆ InputConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::InputConfigHasBeenSet ( ) const

inline

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.

Definition at line 442 of file CreateLabelingJobRequest.h.

◆ LabelAttributeNameHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::LabelAttributeNameHasBeenSet ( ) const

inline

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.

Definition at line 187 of file CreateLabelingJobRequest.h.

◆ LabelCategoryConfigS3UriHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::LabelCategoryConfigS3UriHasBeenSet ( ) const

inline

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 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":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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 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 of the labeling job you want to adjust or verify annotations of.

Definition at line 701 of file CreateLabelingJobRequest.h.

◆ LabelingJobAlgorithmsConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::LabelingJobAlgorithmsConfigHasBeenSet ( ) const

inline

Configures the information required to perform automated data labeling.

Definition at line 995 of file CreateLabelingJobRequest.h.

◆ LabelingJobNameHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::LabelingJobNameHasBeenSet ( ) const

inline

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 Amazon Web Services 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.

Definition at line 60 of file CreateLabelingJobRequest.h.

◆ OutputConfigHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::OutputConfigHasBeenSet ( ) const

inline

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.

Definition at line 531 of file CreateLabelingJobRequest.h.

◆ RoleArnHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::RoleArnHasBeenSet ( ) const

inline

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.

Definition at line 572 of file CreateLabelingJobRequest.h.

◆ SerializePayload()

Aws::String Aws::SageMaker::Model::CreateLabelingJobRequest::SerializePayload ( ) const

overridevirtual

Convert payload into String.

Implements Aws::AmazonSerializableWebServiceRequest.

◆ SetHumanTaskConfig() [1/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetHumanTaskConfig ( const HumanTaskConfig & value )

inline

Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

Definition at line 1034 of file CreateLabelingJobRequest.h.

◆ SetHumanTaskConfig() [2/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetHumanTaskConfig ( HumanTaskConfig && value )

inline

Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

Definition at line 1040 of file CreateLabelingJobRequest.h.

◆ SetInputConfig() [1/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetInputConfig ( const LabelingJobInputConfig & value )

inline

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.

Definition at line 461 of file CreateLabelingJobRequest.h.

◆ SetInputConfig() [2/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetInputConfig ( LabelingJobInputConfig && value )

inline

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.

Definition at line 480 of file CreateLabelingJobRequest.h.

◆ SetLabelAttributeName() [1/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelAttributeName ( Aws::String && value )

inline

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.

Definition at line 259 of file CreateLabelingJobRequest.h.

◆ SetLabelAttributeName() [2/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelAttributeName ( const Aws::String & value )

inline

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.

Definition at line 223 of file CreateLabelingJobRequest.h.

◆ SetLabelAttributeName() [3/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelAttributeName ( const char * value )

inline

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.

Definition at line 295 of file CreateLabelingJobRequest.h.

◆ SetLabelCategoryConfigS3Uri() [1/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelCategoryConfigS3Uri ( Aws::String && value )

inline

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 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":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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 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 of the labeling job you want to adjust or verify annotations of.

Definition at line 781 of file CreateLabelingJobRequest.h.

◆ SetLabelCategoryConfigS3Uri() [2/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelCategoryConfigS3Uri ( const Aws::String & value )

inline

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 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":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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 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 of the labeling job you want to adjust or verify annotations of.

Definition at line 741 of file CreateLabelingJobRequest.h.

◆ SetLabelCategoryConfigS3Uri() [3/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelCategoryConfigS3Uri ( const char * value )

inline

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 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":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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 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 of the labeling job you want to adjust or verify annotations of.

Definition at line 821 of file CreateLabelingJobRequest.h.

◆ SetLabelingJobAlgorithmsConfig() [1/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelingJobAlgorithmsConfig ( const LabelingJobAlgorithmsConfig & value )

inline

Configures the information required to perform automated data labeling.

Definition at line 1000 of file CreateLabelingJobRequest.h.

◆ SetLabelingJobAlgorithmsConfig() [2/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelingJobAlgorithmsConfig ( LabelingJobAlgorithmsConfig && value )

inline

Configures the information required to perform automated data labeling.

Definition at line 1005 of file CreateLabelingJobRequest.h.

◆ SetLabelingJobName() [1/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelingJobName ( Aws::String && value )

inline

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 Amazon Web Services 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.

Definition at line 78 of file CreateLabelingJobRequest.h.

◆ SetLabelingJobName() [2/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelingJobName ( const Aws::String & value )

inline

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 Amazon Web Services 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.

Definition at line 69 of file CreateLabelingJobRequest.h.

◆ SetLabelingJobName() [3/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetLabelingJobName ( const char * value )

inline

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 Amazon Web Services 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.

Definition at line 87 of file CreateLabelingJobRequest.h.

◆ SetOutputConfig() [1/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetOutputConfig ( const LabelingJobOutputConfig & value )

inline

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.

Definition at line 537 of file CreateLabelingJobRequest.h.

◆ SetOutputConfig() [2/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetOutputConfig ( LabelingJobOutputConfig && value )

inline

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.

Definition at line 543 of file CreateLabelingJobRequest.h.

◆ SetRoleArn() [1/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetRoleArn ( Aws::String && value )

inline

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.

Definition at line 588 of file CreateLabelingJobRequest.h.

◆ SetRoleArn() [2/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetRoleArn ( const Aws::String & value )

inline

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.

Definition at line 580 of file CreateLabelingJobRequest.h.

◆ SetRoleArn() [3/3]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetRoleArn ( const char * value )

inline

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.

Definition at line 596 of file CreateLabelingJobRequest.h.

◆ SetStoppingConditions() [1/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetStoppingConditions ( const LabelingJobStoppingConditions & value )

inline

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.

Definition at line 963 of file CreateLabelingJobRequest.h.

◆ SetStoppingConditions() [2/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetStoppingConditions ( LabelingJobStoppingConditions && value )

inline

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.

Definition at line 970 of file CreateLabelingJobRequest.h.

◆ SetTags() [1/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetTags ( Aws::Vector< Tag > && value )

inline

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Definition at line 1085 of file CreateLabelingJobRequest.h.

◆ SetTags() [2/2]

void Aws::SageMaker::Model::CreateLabelingJobRequest::SetTags ( const Aws::Vector< Tag > & value )

inline

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Definition at line 1077 of file CreateLabelingJobRequest.h.

◆ StoppingConditionsHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::StoppingConditionsHasBeenSet ( ) const

inline

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.

Definition at line 956 of file CreateLabelingJobRequest.h.

◆ TagsHasBeenSet()

bool Aws::SageMaker::Model::CreateLabelingJobRequest::TagsHasBeenSet ( ) const

inline

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Definition at line 1069 of file CreateLabelingJobRequest.h.

◆ WithHumanTaskConfig() [1/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithHumanTaskConfig ( const HumanTaskConfig & value )

inline

Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

Definition at line 1046 of file CreateLabelingJobRequest.h.

◆ WithHumanTaskConfig() [2/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithHumanTaskConfig ( HumanTaskConfig && value )

inline

Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

Definition at line 1052 of file CreateLabelingJobRequest.h.

◆ WithInputConfig() [1/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithInputConfig ( const LabelingJobInputConfig & value )

inline

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.

Definition at line 499 of file CreateLabelingJobRequest.h.

◆ WithInputConfig() [2/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithInputConfig ( LabelingJobInputConfig && value )

inline

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.

Definition at line 518 of file CreateLabelingJobRequest.h.

◆ WithLabelAttributeName() [1/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelAttributeName ( Aws::String && value )

inline

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.

Definition at line 367 of file CreateLabelingJobRequest.h.

◆ WithLabelAttributeName() [2/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelAttributeName ( const Aws::String & value )

inline

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.

Definition at line 331 of file CreateLabelingJobRequest.h.

◆ WithLabelAttributeName() [3/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelAttributeName ( const char * value )

inline

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.

Definition at line 403 of file CreateLabelingJobRequest.h.

◆ WithLabelCategoryConfigS3Uri() [1/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelCategoryConfigS3Uri ( Aws::String && value )

inline

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 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":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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 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 of the labeling job you want to adjust or verify annotations of.

Definition at line 901 of file CreateLabelingJobRequest.h.

◆ WithLabelCategoryConfigS3Uri() [2/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelCategoryConfigS3Uri ( const Aws::String & value )

inline

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 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":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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 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 of the labeling job you want to adjust or verify annotations of.

Definition at line 861 of file CreateLabelingJobRequest.h.

◆ WithLabelCategoryConfigS3Uri() [3/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelCategoryConfigS3Uri ( const char * value )

inline

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 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":"<h1>Add header</h1><p>Add Instructions</p>", "fullInstruction":"<p>Add additional instructions.</p>"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) .

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 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 of the labeling job you want to adjust or verify annotations of.

Definition at line 941 of file CreateLabelingJobRequest.h.

◆ WithLabelingJobAlgorithmsConfig() [1/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelingJobAlgorithmsConfig ( const LabelingJobAlgorithmsConfig & value )

inline

Configures the information required to perform automated data labeling.

Definition at line 1010 of file CreateLabelingJobRequest.h.

◆ WithLabelingJobAlgorithmsConfig() [2/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelingJobAlgorithmsConfig ( LabelingJobAlgorithmsConfig && value )

inline

Configures the information required to perform automated data labeling.

Definition at line 1015 of file CreateLabelingJobRequest.h.

◆ WithLabelingJobName() [1/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelingJobName ( Aws::String && value )

inline

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 Amazon Web Services 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.

Definition at line 105 of file CreateLabelingJobRequest.h.

◆ WithLabelingJobName() [2/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelingJobName ( const Aws::String & value )

inline

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 Amazon Web Services 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.

Definition at line 96 of file CreateLabelingJobRequest.h.

◆ WithLabelingJobName() [3/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithLabelingJobName ( const char * value )

inline

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 Amazon Web Services 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.

Definition at line 114 of file CreateLabelingJobRequest.h.

◆ WithOutputConfig() [1/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithOutputConfig ( const LabelingJobOutputConfig & value )

inline

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.

Definition at line 549 of file CreateLabelingJobRequest.h.

◆ WithOutputConfig() [2/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithOutputConfig ( LabelingJobOutputConfig && value )

inline

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.

Definition at line 555 of file CreateLabelingJobRequest.h.

◆ WithRoleArn() [1/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithRoleArn ( Aws::String && value )

inline

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.

Definition at line 612 of file CreateLabelingJobRequest.h.

◆ WithRoleArn() [2/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithRoleArn ( const Aws::String & value )

inline

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.

Definition at line 604 of file CreateLabelingJobRequest.h.

◆ WithRoleArn() [3/3]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithRoleArn ( const char * value )

inline

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.

Definition at line 620 of file CreateLabelingJobRequest.h.

◆ WithStoppingConditions() [1/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithStoppingConditions ( const LabelingJobStoppingConditions & value )

inline

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.

Definition at line 977 of file CreateLabelingJobRequest.h.

◆ WithStoppingConditions() [2/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithStoppingConditions ( LabelingJobStoppingConditions && value )

inline

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.

Definition at line 984 of file CreateLabelingJobRequest.h.

◆ WithTags() [1/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithTags ( Aws::Vector< Tag > && value )

inline

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Definition at line 1101 of file CreateLabelingJobRequest.h.

◆ WithTags() [2/2]

CreateLabelingJobRequest& Aws::SageMaker::Model::CreateLabelingJobRequest::WithTags ( const Aws::Vector< Tag > & value )

inline

An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.

Definition at line 1093 of file CreateLabelingJobRequest.h.

The documentation for this class was generated from the following file:

aws-cpp-sdk-sagemaker/include/aws/sagemaker/model/CreateLabelingJobRequest.h

Public Member Functions

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ CreateLabelingJobRequest()

Member Function Documentation

◆ AddTags() [1/2]

◆ AddTags() [2/2]

◆ GetHumanTaskConfig()

◆ GetInputConfig()

◆ GetLabelAttributeName()

◆ GetLabelCategoryConfigS3Uri()

◆ GetLabelingJobAlgorithmsConfig()

◆ GetLabelingJobName()

◆ GetOutputConfig()

◆ GetRequestSpecificHeaders()

◆ GetRoleArn()

◆ GetServiceRequestName()

◆ GetStoppingConditions()

◆ GetTags()

◆ HumanTaskConfigHasBeenSet()

◆ InputConfigHasBeenSet()

◆ LabelAttributeNameHasBeenSet()

◆ LabelCategoryConfigS3UriHasBeenSet()

◆ LabelingJobAlgorithmsConfigHasBeenSet()

◆ LabelingJobNameHasBeenSet()

◆ OutputConfigHasBeenSet()

◆ RoleArnHasBeenSet()

◆ SerializePayload()

◆ SetHumanTaskConfig() [1/2]

◆ SetHumanTaskConfig() [2/2]

◆ SetInputConfig() [1/2]

◆ SetInputConfig() [2/2]

◆ SetLabelAttributeName() [1/3]

◆ SetLabelAttributeName() [2/3]

◆ SetLabelAttributeName() [3/3]

◆ SetLabelCategoryConfigS3Uri() [1/3]

◆ SetLabelCategoryConfigS3Uri() [2/3]

◆ SetLabelCategoryConfigS3Uri() [3/3]

◆ SetLabelingJobAlgorithmsConfig() [1/2]

◆ SetLabelingJobAlgorithmsConfig() [2/2]

◆ SetLabelingJobName() [1/3]

◆ SetLabelingJobName() [2/3]

◆ SetLabelingJobName() [3/3]

◆ SetOutputConfig() [1/2]

◆ SetOutputConfig() [2/2]

◆ SetRoleArn() [1/3]

◆ SetRoleArn() [2/3]

◆ SetRoleArn() [3/3]

◆ SetStoppingConditions() [1/2]

◆ SetStoppingConditions() [2/2]

◆ SetTags() [1/2]

◆ SetTags() [2/2]

◆ StoppingConditionsHasBeenSet()

◆ TagsHasBeenSet()

◆ WithHumanTaskConfig() [1/2]

◆ WithHumanTaskConfig() [2/2]

◆ WithInputConfig() [1/2]

◆ WithInputConfig() [2/2]

◆ WithLabelAttributeName() [1/3]

◆ WithLabelAttributeName() [2/3]

◆ WithLabelAttributeName() [3/3]

◆ WithLabelCategoryConfigS3Uri() [1/3]

◆ WithLabelCategoryConfigS3Uri() [2/3]

◆ WithLabelCategoryConfigS3Uri() [3/3]

◆ WithLabelingJobAlgorithmsConfig() [1/2]

◆ WithLabelingJobAlgorithmsConfig() [2/2]

◆ WithLabelingJobName() [1/3]

◆ WithLabelingJobName() [2/3]

◆ WithLabelingJobName() [3/3]

◆ WithOutputConfig() [1/2]

◆ WithOutputConfig() [2/2]

◆ WithRoleArn() [1/3]

◆ WithRoleArn() [2/3]

◆ WithRoleArn() [3/3]

◆ WithStoppingConditions() [1/2]

◆ WithStoppingConditions() [2/2]

◆ WithTags() [1/2]

◆ WithTags() [2/2]