Interface CreateLabelingJobRequest.Builder

All Superinterfaces:
AwsRequest.Builder, Buildable, CopyableBuilder<CreateLabelingJobRequest.Builder,CreateLabelingJobRequest>, SageMakerRequest.Builder, SdkBuilder<CreateLabelingJobRequest.Builder,CreateLabelingJobRequest>, SdkPojo, SdkRequest.Builder
Enclosing class:
CreateLabelingJobRequest
@Mutable @NotThreadSafe public static interface CreateLabelingJobRequest.Builder extends SageMakerRequest.Builder, SdkPojo, CopyableBuilder<CreateLabelingJobRequest.Builder,CreateLabelingJobRequest>

  • Method Details

    • labelingJobName

      CreateLabelingJobRequest.Builder labelingJobName(String 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.

      Parameters:
      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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • labelAttributeName

      CreateLabelingJobRequest.Builder labelAttributeName(String 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 built-in task types or one of the following, the attribute name must end with "-ref".

        • Image semantic segmentation (SemanticSegmentation) and adjustment ( AdjustmentSemanticSegmentation) labeling jobs for this task type. One exception is that verification (VerificationSemanticSegmentation) must not end with -"ref".

        • 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.

      Parameters:
      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 built-in task types or one of the following, the attribute name must end with "-ref".

        • Image semantic segmentation (SemanticSegmentation) and adjustment ( AdjustmentSemanticSegmentation) labeling jobs for this task type. One exception is that verification (VerificationSemanticSegmentation) must not end with -"ref".

        • 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.

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • inputConfig

      CreateLabelingJobRequest.Builder inputConfig(LabelingJobInputConfig 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.

      Parameters:
      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.

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • inputConfig

      default CreateLabelingJobRequest.Builder inputConfig(Consumer<LabelingJobInputConfig.Builder> 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.

      This is a convenience method that creates an instance of the LabelingJobInputConfig.Builder avoiding the need to create one manually via LabelingJobInputConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to inputConfig(LabelingJobInputConfig).

      Parameters:
      inputConfig - a consumer that will call methods on LabelingJobInputConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • outputConfig

      CreateLabelingJobRequest.Builder outputConfig(LabelingJobOutputConfig 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.

      Parameters:
      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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • outputConfig

      default CreateLabelingJobRequest.Builder outputConfig(Consumer<LabelingJobOutputConfig.Builder> 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.

      This is a convenience method that creates an instance of the LabelingJobOutputConfig.Builder avoiding the need to create one manually via LabelingJobOutputConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to outputConfig(LabelingJobOutputConfig).

      Parameters:
      outputConfig - a consumer that will call methods on LabelingJobOutputConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • roleArn

      CreateLabelingJobRequest.Builder roleArn(String 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.

      Parameters:
      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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • labelCategoryConfigS3Uri

      CreateLabelingJobRequest.Builder labelCategoryConfigS3Uri(String 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.

      Parameters:
      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.

      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • stoppingConditions

      CreateLabelingJobRequest.Builder stoppingConditions(LabelingJobStoppingConditions stoppingConditions)

      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.

      Parameters:
      stoppingConditions - 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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • stoppingConditions

      default CreateLabelingJobRequest.Builder stoppingConditions(Consumer<LabelingJobStoppingConditions.Builder> stoppingConditions)

      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.

      This is a convenience method that creates an instance of the LabelingJobStoppingConditions.Builder avoiding the need to create one manually via LabelingJobStoppingConditions.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to stoppingConditions(LabelingJobStoppingConditions).

      Parameters:
      stoppingConditions - a consumer that will call methods on LabelingJobStoppingConditions.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • labelingJobAlgorithmsConfig

      CreateLabelingJobRequest.Builder labelingJobAlgorithmsConfig(LabelingJobAlgorithmsConfig labelingJobAlgorithmsConfig)

      Configures the information required to perform automated data labeling.

      Parameters:
      labelingJobAlgorithmsConfig - Configures the information required to perform automated data labeling.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • labelingJobAlgorithmsConfig

      default CreateLabelingJobRequest.Builder labelingJobAlgorithmsConfig(Consumer<LabelingJobAlgorithmsConfig.Builder> labelingJobAlgorithmsConfig)

      Configures the information required to perform automated data labeling.

      This is a convenience method that creates an instance of the LabelingJobAlgorithmsConfig.Builder avoiding the need to create one manually via LabelingJobAlgorithmsConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to labelingJobAlgorithmsConfig(LabelingJobAlgorithmsConfig).

      Parameters:
      labelingJobAlgorithmsConfig - a consumer that will call methods on LabelingJobAlgorithmsConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • humanTaskConfig

      CreateLabelingJobRequest.Builder humanTaskConfig(HumanTaskConfig humanTaskConfig)

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

      Parameters:
      humanTaskConfig - Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • humanTaskConfig

      default CreateLabelingJobRequest.Builder humanTaskConfig(Consumer<HumanTaskConfig.Builder> humanTaskConfig)

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

      This is a convenience method that creates an instance of the HumanTaskConfig.Builder avoiding the need to create one manually via HumanTaskConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to humanTaskConfig(HumanTaskConfig).

      Parameters:
      humanTaskConfig - a consumer that will call methods on HumanTaskConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • tags

      CreateLabelingJobRequest.Builder tags(Collection<Tag> 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.

      Parameters:
      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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • tags

      CreateLabelingJobRequest.Builder tags(Tag... 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.

      Parameters:
      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.
      Returns:
      Returns a reference to this object so that method calls can be chained together.

    • tags

      CreateLabelingJobRequest.Builder tags(Consumer<Tag.Builder>... 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.

      This is a convenience method that creates an instance of the Tag.Builder avoiding the need to create one manually via Tag.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to tags(List<Tag>).

      Parameters:
      tags - a consumer that will call methods on Tag.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • overrideConfiguration

      CreateLabelingJobRequest.Builder overrideConfiguration(AwsRequestOverrideConfiguration overrideConfiguration)
      Description copied from interface: AwsRequest.Builder
      Add an optional request override configuration.
      Specified by:
      overrideConfiguration in interface AwsRequest.Builder
      Parameters:
      overrideConfiguration - The override configuration.
      Returns:
      This object for method chaining.

    • overrideConfiguration

      CreateLabelingJobRequest.Builder overrideConfiguration(Consumer<AwsRequestOverrideConfiguration.Builder> builderConsumer)
      Description copied from interface: AwsRequest.Builder
      Add an optional request override configuration.
      Specified by:
      overrideConfiguration in interface AwsRequest.Builder
      Parameters:
      builderConsumer - A Consumer to which an empty AwsRequestOverrideConfiguration.Builder will be given.
      Returns:
      This object for method chaining.