# Create a 3D point cloud semantic segmentation labeling job You can create a 3D point cloud labeling job using the SageMaker AI console or API operation, [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html). To create a labeling job for this task type you need the following: + A single-frame input manifest file. To learn how to create this type of manifest file, see [Create a Point Cloud Frame Input Manifest File](sms-point-cloud-single-frame-input-data.md). If you are a new user of Ground Truth 3D point cloud labeling modalities, we recommend that you review [Accepted Raw 3D Data Formats](sms-point-cloud-raw-data-types.md). + A work team from a private or vendor workforce. You cannot use Amazon Mechanical Turk workers for 3D point cloud labeling jobs. To learn how to create workforces and work teams, see [Workforces](sms-workforce-management.md). + A label category configuration file. For more information, see [Labeling category configuration file with label category and frame attributes reference](sms-label-cat-config-attributes.md). Additionally, make sure that you have reviewed and satisfied the [Assign IAM Permissions to Use Ground Truth](sms-security-permission.md). Use one of the following sections to learn how to create a labeling job using the console or an API. ## Create a labeling job (console) You can follow the instructions [Create a Labeling Job (Console)](sms-create-labeling-job-console.md) in order to learn how to create a 3D point cloud semantic segmentation labeling job in the SageMaker AI console. While you are creating your labeling job, be aware of the following: + Your input manifest file must be a single-frame manifest file. For more information, see [Create a Point Cloud Frame Input Manifest File](sms-point-cloud-single-frame-input-data.md). + Automated data labeling and annotation consolidation are not supported for 3D point cloud labeling tasks. + 3D point cloud semantic segmentation labeling jobs can take multiple hours to complete. You can specify a longer time limit for these labeling jobs when you select your work team (up to 7 days, or 604800 seconds). ## Create a labeling job (API) This section covers details you need to know when you create a labeling job using the SageMaker API operation `CreateLabelingJob`. This API defines this operation for all AWS SDKs. To see a list of language-specific SDKs supported for this operation, review the **See Also** section of [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html). The page, [Create a Labeling Job (API)](sms-create-labeling-job-api.md), provides an overview of the `CreateLabelingJob` operation. Follow these instructions and do the following while you configure your request: + You must enter an ARN for `HumanTaskUiArn`. Use `arn:aws:sagemaker::394669845002:human-task-ui/PointCloudSemanticSegmentation`. Replace `` with the AWS Region you are creating the labeling job in. There should not be an entry for the `UiTemplateS3Uri` parameter. + Your [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelAttributeName) must end in `-ref`. For example, `ss-labels-ref`. + Your input manifest file must be a single-frame manifest file. For more information, see [Create a Point Cloud Frame Input Manifest File](sms-point-cloud-single-frame-input-data.md). + You specify your labels and worker instructions in a label category configuration file. See [Labeling category configuration file with label category and frame attributes reference](sms-label-cat-config-attributes.md) to learn how to create this file. + You need to provide a pre-defined ARNs for the pre-annotation and post-annotation (ACS) Lambda functions. These ARNs are specific to the AWS Region you use to create your labeling job. + To find the pre-annotation Lambda ARN, refer to [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HumanTaskConfig.html#sagemaker-Type-HumanTaskConfig-PreHumanTaskLambdaArn](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HumanTaskConfig.html#sagemaker-Type-HumanTaskConfig-PreHumanTaskLambdaArn). Use the Region you are creating your labeling job in to find the correct ARN. For example, if you are creating your labeling job in us-east-1, the ARN will be `arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation`. + To find the post-annotation Lambda ARN, refer to [https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AnnotationConsolidationConfig.html#sagemaker-Type-AnnotationConsolidationConfig-AnnotationConsolidationLambdaArn](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_AnnotationConsolidationConfig.html#sagemaker-Type-AnnotationConsolidationConfig-AnnotationConsolidationLambdaArn). Use the Region you are creating your labeling job in to find the correct ARN. For example, if you are creating your labeling job in us-east-1, the ARN will be `arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation`. + The number of workers specified in `NumberOfHumanWorkersPerDataObject` should be `1`. + Automated data labeling is not supported for 3D point cloud labeling jobs. You should not specify values for parameters in `[LabelingJobAlgorithmsConfig](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateLabelingJob.html#sagemaker-CreateLabelingJob-request-LabelingJobAlgorithmsConfig)`. + 3D point cloud semantic segmentation labeling jobs can take multiple hours to complete. You can specify a longer time limit for these labeling jobs in `TaskTimeLimitInSeconds` (up to 7 days, or 604800 seconds).