#include <CreateLabelingJobRequest.h>
Inheritance diagram for Aws::SageMaker::Model::CreateLabelingJobRequest:
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]
|
inline |
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Definition at line 1053 of file CreateLabelingJobRequest.h.
◆ AddTags() [2/2]
|
inline |
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Definition at line 1061 of file CreateLabelingJobRequest.h.
◆ GetHumanTaskConfig()
|
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 966 of file CreateLabelingJobRequest.h.
◆ GetInputConfig()
|
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
SnsDataSourceto 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
S3DataSourceto specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSourceis optional if you useSnsDataSourceto 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()
|
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()
|
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 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
Definition at line 654 of file CreateLabelingJobRequest.h.
◆ GetLabelingJobAlgorithmsConfig()
|
inline |
Configures the information required to perform automated data labeling.
Definition at line 934 of file CreateLabelingJobRequest.h.
◆ GetLabelingJobName()
|
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 AWS account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
Definition at line 51 of file CreateLabelingJobRequest.h.
◆ GetOutputConfig()
|
inline |
The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
Definition at line 525 of file CreateLabelingJobRequest.h.
◆ GetRequestSpecificHeaders()
|
overridevirtual |
Reimplemented from Aws::SageMaker::SageMakerRequest.
◆ GetRoleArn()
|
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()
|
inlineoverridevirtual |
Implements Aws::AmazonWebServiceRequest.
Definition at line 37 of file CreateLabelingJobRequest.h.
◆ GetStoppingConditions()
|
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 893 of file CreateLabelingJobRequest.h.
◆ GetTags()
|
inline |
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Definition at line 1005 of file CreateLabelingJobRequest.h.
◆ HumanTaskConfigHasBeenSet()
|
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 972 of file CreateLabelingJobRequest.h.
◆ InputConfigHasBeenSet()
|
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
SnsDataSourceto 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
S3DataSourceto specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSourceis optional if you useSnsDataSourceto 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()
|
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()
|
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 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
Definition at line 687 of file CreateLabelingJobRequest.h.
◆ LabelingJobAlgorithmsConfigHasBeenSet()
|
inline |
Configures the information required to perform automated data labeling.
Definition at line 939 of file CreateLabelingJobRequest.h.
◆ LabelingJobNameHasBeenSet()
|
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 AWS account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
Definition at line 60 of file CreateLabelingJobRequest.h.
◆ OutputConfigHasBeenSet()
|
inline |
The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
Definition at line 531 of file CreateLabelingJobRequest.h.
◆ RoleArnHasBeenSet()
|
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()
|
overridevirtual |
Convert payload into String.
Implements Aws::AmazonSerializableWebServiceRequest.
◆ SetHumanTaskConfig() [1/2]
|
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 978 of file CreateLabelingJobRequest.h.
◆ SetHumanTaskConfig() [2/2]
|
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 984 of file CreateLabelingJobRequest.h.
◆ SetInputConfig() [1/2]
|
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
SnsDataSourceto 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
S3DataSourceto specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSourceis optional if you useSnsDataSourceto 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]
|
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
SnsDataSourceto 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
S3DataSourceto specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSourceis optional if you useSnsDataSourceto 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]
|
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]
|
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]
|
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]
|
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 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
Definition at line 753 of file CreateLabelingJobRequest.h.
◆ SetLabelCategoryConfigS3Uri() [2/3]
|
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 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
Definition at line 720 of file CreateLabelingJobRequest.h.
◆ SetLabelCategoryConfigS3Uri() [3/3]
|
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 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
Definition at line 786 of file CreateLabelingJobRequest.h.
◆ SetLabelingJobAlgorithmsConfig() [1/2]
|
inline |
Configures the information required to perform automated data labeling.
Definition at line 944 of file CreateLabelingJobRequest.h.
◆ SetLabelingJobAlgorithmsConfig() [2/2]
|
inline |
Configures the information required to perform automated data labeling.
Definition at line 949 of file CreateLabelingJobRequest.h.
◆ SetLabelingJobName() [1/3]
|
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 AWS account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
Definition at line 78 of file CreateLabelingJobRequest.h.
◆ SetLabelingJobName() [2/3]
|
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 AWS account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
Definition at line 69 of file CreateLabelingJobRequest.h.
◆ SetLabelingJobName() [3/3]
|
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 AWS account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
Definition at line 87 of file CreateLabelingJobRequest.h.
◆ SetOutputConfig() [1/2]
|
inline |
The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
Definition at line 537 of file CreateLabelingJobRequest.h.
◆ SetOutputConfig() [2/2]
|
inline |
The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
Definition at line 543 of file CreateLabelingJobRequest.h.
◆ SetRoleArn() [1/3]
|
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]
|
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]
|
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]
|
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 907 of file CreateLabelingJobRequest.h.
◆ SetStoppingConditions() [2/2]
|
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 914 of file CreateLabelingJobRequest.h.
◆ SetTags() [1/2]
|
inline |
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Definition at line 1029 of file CreateLabelingJobRequest.h.
◆ SetTags() [2/2]
|
inline |
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Definition at line 1021 of file CreateLabelingJobRequest.h.
◆ StoppingConditionsHasBeenSet()
|
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 900 of file CreateLabelingJobRequest.h.
◆ TagsHasBeenSet()
|
inline |
An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Definition at line 1013 of file CreateLabelingJobRequest.h.
◆ WithHumanTaskConfig() [1/2]
|
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 990 of file CreateLabelingJobRequest.h.
◆ WithHumanTaskConfig() [2/2]
|
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 996 of file CreateLabelingJobRequest.h.
◆ WithInputConfig() [1/2]
|
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
SnsDataSourceto 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
S3DataSourceto specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSourceis optional if you useSnsDataSourceto 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]
|
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
SnsDataSourceto 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
S3DataSourceto specify an input manifest file for both streaming and one-time labeling jobs. Adding anS3DataSourceis optional if you useSnsDataSourceto 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]
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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.
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The name can't end with "-metadata".
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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]
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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]
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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]
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The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects.
For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.
For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
Definition at line 852 of file CreateLabelingJobRequest.h.
◆ WithLabelCategoryConfigS3Uri() [2/3]
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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 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
Definition at line 819 of file CreateLabelingJobRequest.h.
◆ WithLabelCategoryConfigS3Uri() [3/3]
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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 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
auditLabelAttributeNamein the label category configuration. Use this parameter to enter theLabelAttributeNameof the labeling job you want to adjust or verify annotations of.
Definition at line 885 of file CreateLabelingJobRequest.h.
◆ WithLabelingJobAlgorithmsConfig() [1/2]
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Configures the information required to perform automated data labeling.
Definition at line 954 of file CreateLabelingJobRequest.h.
◆ WithLabelingJobAlgorithmsConfig() [2/2]
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Configures the information required to perform automated data labeling.
Definition at line 959 of file CreateLabelingJobRequest.h.
◆ WithLabelingJobName() [1/3]
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The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an AWS account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
Definition at line 105 of file CreateLabelingJobRequest.h.
◆ WithLabelingJobName() [2/3]
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The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an AWS account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
Definition at line 96 of file CreateLabelingJobRequest.h.
◆ WithLabelingJobName() [3/3]
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The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an AWS account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
Definition at line 114 of file CreateLabelingJobRequest.h.
◆ WithOutputConfig() [1/2]
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The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
Definition at line 549 of file CreateLabelingJobRequest.h.
◆ WithOutputConfig() [2/2]
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The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.
Definition at line 555 of file CreateLabelingJobRequest.h.
◆ WithRoleArn() [1/3]
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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]
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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]
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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]
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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 921 of file CreateLabelingJobRequest.h.
◆ WithStoppingConditions() [2/2]
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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 928 of file CreateLabelingJobRequest.h.
◆ WithTags() [1/2]
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An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Definition at line 1045 of file CreateLabelingJobRequest.h.
◆ WithTags() [2/2]
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An array of key/value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.
Definition at line 1037 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
