/* * Copyright 2018-2023 Amazon.com, Inc. or its affiliates. All Rights Reserved. * * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with * the License. A copy of the License is located at * * http://aws.amazon.com/apache2.0 * * or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR * CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions * and limitations under the License. */ package com.amazonaws.services.sagemaker.model; import java.io.Serializable; import javax.annotation.Generated; import com.amazonaws.AmazonWebServiceRequest; /** * * @see AWS API * Documentation */ @Generated("com.amazonaws:aws-java-sdk-code-generator") public class CreateLabelingJobRequest extends com.amazonaws.AmazonWebServiceRequest implements Serializable, Cloneable { /** *

* 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. *

*/ private String labelingJobName; /** *

* 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. *

* */ private String labelAttributeName; /** *

* 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. *

*/ private LabelingJobInputConfig inputConfig; /** *

* 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. *

*/ private LabelingJobOutputConfig outputConfig; /** *

* 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. *

*/ private String roleArn; /** *

* 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. *
*

*/ private String labelCategoryConfigS3Uri; /** *

* 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. *

*/ private LabelingJobStoppingConditions stoppingConditions; /** *

* Configures the information required to perform automated data labeling. *

*/ private LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig; /** *

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

*/ private HumanTaskConfig humanTaskConfig; /** *

* 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. *

*/ private java.util.List tags; /** *

* * @param labelingJobName * 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. */ public void setLabelingJobName(String labelingJobName) { this.labelingJobName = labelingJobName; } /** *

* * @return 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. */ public String getLabelingJobName() { return this.labelingJobName; } /** *

* * @param labelingJobName * 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. * @return Returns a reference to this object so that method calls can be chained together. */ public CreateLabelingJobRequest withLabelingJobName(String labelingJobName) { setLabelingJobName(labelingJobName); return this; } /** *

*
* 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. *
  *
*

* *

* * * @param labelAttributeName * 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. *

*/ public void setLabelAttributeName(String labelAttributeName) { this.labelAttributeName = labelAttributeName; } /** *

*
* 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. *
  *
*

* *

* * * @return 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. *

*/ public String getLabelAttributeName() { return this.labelAttributeName; } /** *

*
* 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. *
  *
*

* *

*
* 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. *

* @return Returns a reference to this object so that method calls can be chained together. */ public CreateLabelingJobRequest withLabelAttributeName(String labelAttributeName) { setLabelAttributeName(labelAttributeName); return this; } /** *

* 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. *
*

* * @param inputConfig * 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. */ public void setInputConfig(LabelingJobInputConfig inputConfig) { this.inputConfig = inputConfig; } /** *

* 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. *
*

* * @return 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. */ public LabelingJobInputConfig getInputConfig() { return this.inputConfig; } /** *

* 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. *
*

* * @param inputConfig * 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. * @return Returns a reference to this object so that method calls can be chained together. */ public CreateLabelingJobRequest withInputConfig(LabelingJobInputConfig inputConfig) { setInputConfig(inputConfig); return this; } /** *

* 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. *

* * @param outputConfig * 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. */ public void setOutputConfig(LabelingJobOutputConfig outputConfig) { this.outputConfig = outputConfig; } /** *

* 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. *

* * @return 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. */ public LabelingJobOutputConfig getOutputConfig() { return this.outputConfig; } /** *

* 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. *

* * @param outputConfig * 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. * @return Returns a reference to this object so that method calls can be chained together. */ public CreateLabelingJobRequest withOutputConfig(LabelingJobOutputConfig outputConfig) { setOutputConfig(outputConfig); return this; } /** *

* * @param roleArn * 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. */ public void setRoleArn(String roleArn) { this.roleArn = roleArn; } /** *

* * @return 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. */ public String getRoleArn() { return this.roleArn; } /** *

* * @param roleArn * 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. * @return Returns a reference to this object so that method calls can be chained together. */ public CreateLabelingJobRequest withRoleArn(String roleArn) { setRoleArn(roleArn); return this; } /** *

* The S3 URI of the file, referred to as a label category configuration file, that defines the categories * used to label the data objects. *

* { *

* "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. *
*

* * @param labelCategoryConfigS3Uri * 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. *
*

* The S3 URI of the file, referred to as a label category configuration file, that defines the categories * used to label the data objects. *

* { *

* "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. *
*

label category configuration file

* 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. *

* { *

* "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. *
*

* The S3 URI of the file, referred to as a label category configuration file, that defines the categories * used to label the data objects. *

* { *

* "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. *
*

label category configuration file

* { *

* "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. *
*

* 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. *

* Configures the information required to perform automated data labeling. *

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

* 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. *

Using Cost Allocation Tags

Amazon Web Services Billing and Cost Management User Guide

getTags() { return tags; } /** *

* 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. *

* * @param tags * 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. */ public void setTags(java.util.Collection tags) { if (tags == null) { this.tags = null; return; } this.tags = new java.util.ArrayList(tags); } /** *

* 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. *

* NOTE: This method appends the values to the existing list (if any). Use * {@link #setTags(java.util.Collection)} or {@link #withTags(java.util.Collection)} if you want to override the * existing values. *

* 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. *

* * @param tags * 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. * @return Returns a reference to this object so that method calls can be chained together. */ public CreateLabelingJobRequest withTags(java.util.Collection tags) { setTags(tags); return this; } /** * Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be * redacted from this string using a placeholder value. * * @return A string representation of this object. * * @see java.lang.Object#toString() */ @Override public String toString() { StringBuilder sb = new StringBuilder(); sb.append("{"); if (getLabelingJobName() != null) sb.append("LabelingJobName: ").append(getLabelingJobName()).append(","); if (getLabelAttributeName() != null) sb.append("LabelAttributeName: ").append(getLabelAttributeName()).append(","); if (getInputConfig() != null) sb.append("InputConfig: ").append(getInputConfig()).append(","); if (getOutputConfig() != null) sb.append("OutputConfig: ").append(getOutputConfig()).append(","); if (getRoleArn() != null) sb.append("RoleArn: ").append(getRoleArn()).append(","); if (getLabelCategoryConfigS3Uri() != null) sb.append("LabelCategoryConfigS3Uri: ").append(getLabelCategoryConfigS3Uri()).append(","); if (getStoppingConditions() != null) sb.append("StoppingConditions: ").append(getStoppingConditions()).append(","); if (getLabelingJobAlgorithmsConfig() != null) sb.append("LabelingJobAlgorithmsConfig: ").append(getLabelingJobAlgorithmsConfig()).append(","); if (getHumanTaskConfig() != null) sb.append("HumanTaskConfig: ").append(getHumanTaskConfig()).append(","); if (getTags() != null) sb.append("Tags: ").append(getTags()); sb.append("}"); return sb.toString(); } @Override public boolean equals(Object obj) { if (this == obj) return true; if (obj == null) return false; if (obj instanceof CreateLabelingJobRequest == false) return false; CreateLabelingJobRequest other = (CreateLabelingJobRequest) obj; if (other.getLabelingJobName() == null ^ this.getLabelingJobName() == null) return false; if (other.getLabelingJobName() != null && other.getLabelingJobName().equals(this.getLabelingJobName()) == false) return false; if (other.getLabelAttributeName() == null ^ this.getLabelAttributeName() == null) return false; if (other.getLabelAttributeName() != null && other.getLabelAttributeName().equals(this.getLabelAttributeName()) == false) return false; if (other.getInputConfig() == null ^ this.getInputConfig() == null) return false; if (other.getInputConfig() != null && other.getInputConfig().equals(this.getInputConfig()) == false) return false; if (other.getOutputConfig() == null ^ this.getOutputConfig() == null) return false; if (other.getOutputConfig() != null && other.getOutputConfig().equals(this.getOutputConfig()) == false) return false; if (other.getRoleArn() == null ^ this.getRoleArn() == null) return false; if (other.getRoleArn() != null && other.getRoleArn().equals(this.getRoleArn()) == false) return false; if (other.getLabelCategoryConfigS3Uri() == null ^ this.getLabelCategoryConfigS3Uri() == null) return false; if (other.getLabelCategoryConfigS3Uri() != null && other.getLabelCategoryConfigS3Uri().equals(this.getLabelCategoryConfigS3Uri()) == false) return false; if (other.getStoppingConditions() == null ^ this.getStoppingConditions() == null) return false; if (other.getStoppingConditions() != null && other.getStoppingConditions().equals(this.getStoppingConditions()) == false) return false; if (other.getLabelingJobAlgorithmsConfig() == null ^ this.getLabelingJobAlgorithmsConfig() == null) return false; if (other.getLabelingJobAlgorithmsConfig() != null && other.getLabelingJobAlgorithmsConfig().equals(this.getLabelingJobAlgorithmsConfig()) == false) return false; if (other.getHumanTaskConfig() == null ^ this.getHumanTaskConfig() == null) return false; if (other.getHumanTaskConfig() != null && other.getHumanTaskConfig().equals(this.getHumanTaskConfig()) == false) return false; if (other.getTags() == null ^ this.getTags() == null) return false; if (other.getTags() != null && other.getTags().equals(this.getTags()) == false) return false; return true; } @Override public int hashCode() { final int prime = 31; int hashCode = 1; hashCode = prime * hashCode + ((getLabelingJobName() == null) ? 0 : getLabelingJobName().hashCode()); hashCode = prime * hashCode + ((getLabelAttributeName() == null) ? 0 : getLabelAttributeName().hashCode()); hashCode = prime * hashCode + ((getInputConfig() == null) ? 0 : getInputConfig().hashCode()); hashCode = prime * hashCode + ((getOutputConfig() == null) ? 0 : getOutputConfig().hashCode()); hashCode = prime * hashCode + ((getRoleArn() == null) ? 0 : getRoleArn().hashCode()); hashCode = prime * hashCode + ((getLabelCategoryConfigS3Uri() == null) ? 0 : getLabelCategoryConfigS3Uri().hashCode()); hashCode = prime * hashCode + ((getStoppingConditions() == null) ? 0 : getStoppingConditions().hashCode()); hashCode = prime * hashCode + ((getLabelingJobAlgorithmsConfig() == null) ? 0 : getLabelingJobAlgorithmsConfig().hashCode()); hashCode = prime * hashCode + ((getHumanTaskConfig() == null) ? 0 : getHumanTaskConfig().hashCode()); hashCode = prime * hashCode + ((getTags() == null) ? 0 : getTags().hashCode()); return hashCode; } @Override public CreateLabelingJobRequest clone() { return (CreateLabelingJobRequest) super.clone(); } }