Create a Labeling Job (Console) - Amazon SageMaker AI
Next Steps

Create a Labeling Job (Console)

You can use the Amazon SageMaker AI console to create a labeling job for all of the Ground Truth built-in task types and custom labeling workflows. For built-in task types, we recommend that you use this page alongside the page for your task type. Each task type page includes specific details on creating a labeling job using that task type.

You need to provide the following to create a labeling job in the SageMaker AI console:

  • An input manifest file in Amazon S3. You can place your input dataset in Amazon S3 and automatically generate a manifest file using the Ground Truth console (not supported for 3D point cloud labeling jobs).

    Alternatively, you can manually create an input manifest file. To learn how, see Input data.

  • An Amazon S3 bucket to store your output data.

  • An IAM role with permission to access your resources in Amazon S3 and with a SageMaker AI execution policy attached. For a general solution, you can attach the managed policy, AmazonSageMakerFullAccess, to an IAM role and include sagemaker in your bucket name.

    For more granular policies, see Assign IAM Permissions to Use Ground Truth.

    3D point cloud task types have additional security considerations. Learn more.

  • A work team. You create a work team from a workforce made up of Amazon Mechanical Turk workers, vendors, or your own private workers.To lean more, see Workforces.

    You cannot use the Mechanical Turk workforce for 3D point cloud or video frame labeling jobs.

  • If you are using a custom labeling workflow, you must save a worker task template in Amazon S3 and provide an Amazon S3 URI for that template. For more information, see Creating a custom worker task template.

  • (Optional) An AWS KMS key ARN if you want SageMaker AI to encrypt the output of your labeling job using your own AWS KMS encryption key instead of the default Amazon S3 service key.

  • (Optional) Existing labels for the dataset you use for your labeling job. Use this option if you want workers to adjust, or approve and reject labels.

  • If you want to create an adjustment or verification labeling job, you must have an output manifest file in Amazon S3 that contains the labels you want adjusted or verified. This option is only supported for bounding box and semantic segmentation image labeling jobs and 3D point cloud and video frame labeling jobs. It is recommended that you use the instructions on Label verification and adjustment to create a verification or adjustment labeling job.

Important

Your work team, input manifest file, output bucket, and other resources in Amazon S3 must be in the same AWS Region you use to create your labeling job.

When you create a labeling job using the SageMaker AI console, you add worker instructions and labels to the worker UI that Ground Truth provides. You can preview and interact with the worker UI while creating your labeling job in the console. You can also see a preview of the worker UI on your built-in task type page.

To create a labeling job (console)

  1. Sign in to the SageMaker AI console at https://console.aws.amazon.com/sagemaker/.

  2. In the left navigation pane, choose Labeling jobs.

  3. On the Labeling jobs page, choose Create labeling job.

  4. For Job name, enter a name for your labeling job.

  5. (Optional) If you want to identify your labels with a key, select I want to specify a label attribute name different from the labeling job name. If you do not select this option, the labeling job name you specified in the previous step will be used to identify your labels in your output manifest file.

  6. Choose a data setup to create a connection between your input dataset and Ground Truth.

    • For Automated data setup:

    • For Manual data setup:

      • For Input dataset location, provide the location in Amazon S3 in which your input manifest file is located. For example, if your input manifest file, manifest.json, is located in example-bucket, enter s3://example-bucket/manifest.json.

      • For Output dataset location, provide the location in Amazon S3 where you want Ground Truth to store the output data from your labeling job.

  7. For IAM Role, choose an existing IAM role or create an IAM role with permission to access your resources in Amazon S3, to write to the output Amazon S3 bucket specified above, and with a SageMaker AI execution policy attached.

  8. (Optional) For Additional configuration, you can specify how much of your dataset you want workers to label, and if you want SageMaker AI to encrypt the output data for your labeling job using an AWS KMS encryption key. To encrypt your output data, you must have the required AWS KMS permissions attached to the IAM role you provided in the previous step. For more details, see Assign IAM Permissions to Use Ground Truth.

  9. In the Task type section, under Task category, use the dropdown list to select your task category.

  10. In Task selection, choose your task type.

  11. (Optional) Provide tags for your labeling job to make it easier to find in the console later.

  12. Choose Next.

  13. In the Workers section, choose the type of workforce you would like to use. For more details about your workforce options see Workforces.

  14. (Optional) After you've selected your workforce, specify the Task timeout. This is the maximum amount of time a worker has to work on a task.

    For 3D point cloud annotation tasks, the default task timeout is 3 days. The default timeout for text and image classification and label verification labeling jobs is 5 minutes. The default timeout for all other labeling jobs is 60 minutes.

  15. (Optional) For bounding box, semantic segmentation, video frame, and 3D point cloud task types, you can select Display existing labels if you want to display labels for your input data set for workers to verify or adjust.

    For bounding box and semantic segmentation labeling jobs, this will create an adjustment labeling job.

    For 3D point cloud and video frame labeling jobs:

    • Select Adjustment to create an adjustment labeling job. When you select this option, you can add new labels but you cannot remove or edit existing labels from the previous job. Optionally, you can choose label category attributes and frame attributes that you want workers to edit. To make an attribute editable, select the check box Allow workers to edit this attribute for that attribute.

      Optionally, you can add new label category and frame attributes.

    • Select Verification to create an adjustment labeling job. When you select this option, you cannot add, modify, or remove existing labels from the previous job. Optionally, you can choose label category attributes and frame attributes that you want workers to edit. To make an attribute editable, select the check box Allow workers to edit this attribute for that attribute.

      We recommend that you can add new label category attributes to the labels that you want workers to verify, or add one or more frame attributes to have workers provide information about the entire frame.

    For more information, see Label verification and adjustment.

  16. Configure your workers' UI:

    • If you are using a built-in task type, specify workers instructions and labels.

      • For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other built-in task types, you must specify at least one label category.

      • (Optional) If you are creating a 3D point cloud or video frame labeling job, you can specify label category attributes (not supported for 3D point cloud semantic segmentation) and frame attributes. Label category attributes can be assigned to one or more labels. Frame attributes will appear on each point cloud or video frame workers label. To learn more, see Worker user interface (UI) for 3D point cloud and Worker user interface (UI) for video frame.

      • (Optional) Add Additional instructions to help your worker complete your task.

    • If you are creating a custom labeling workflow you must :

      • Enter a custom template in the code box. Custom templates can be created using a combination of HTML, the Liquid templating language and our pre-built web components. Optionally, you can choose a base-template from the drop-down menu to get started.

      • Specify pre-annotation and post-annotation lambda functions. To learn how to create these functions, see Processing data in a custom labeling workflow with AWS Lambda.

  17. (Optional) You can select See preview to preview your worker instructions, labels, and interact with the worker UI. Make sure the pop-up blocker of the browser is disabled before generating the preview.

  18. Choose Create.

After you've successfully created your labeling job, you are redirected to the Labeling jobs page. The status of the labeling job you just created is In progress. This status progressively updates as workers complete your tasks. When all tasks are successfully completed, the status changes to Completed.

If an issue occurs while creating the labeling job, its status changes to Failed.

To view more details about the job, choose the labeling job name.

Next Steps

After your labeling job status changes to Completed, you can view your output data in the Amazon S3 bucket that you specified while creating that labeling job. For details about the format of your output data, see Labeling job output data.