How can I use a custom UI template with AWS-provided Lambda functions in Ground Truth?
I want to use an Amazon SageMaker Ground Truth custom UI template and AWS Lambda functions for a labeling job.
Resolution
1. Create a custom UI template for the labeling job, as shown in the following example. For semantic segmentation jobs, set the name variable to crowd-semantic-segmentation, as shown in the following example. For bounding box jobs, set the name variable to boundingBox. For a full list of enhanced HTML elements for custom templates, see Crowd HTML elements reference.
<script src="https://assets.crowd.aws/crowd-html-elements.js"></script> <crowd-form> <crowd-semantic-segmentation name="crowd-semantic-segmentation" src="{{ task.input.taskObject | grant_read_access }}" header= "{{ task.input.header }}" labels="{{ task.input.labels | to_json | escape }}"> <full-instructions header= "Segmentation Instructions"> <ol> <li>Read the task carefully and inspect the image.</li> <li>Read the options and review the examples provided to understand more about the labels.</li> <li>Choose the appropriate label that best suits the image.</li> </ol> </full-instructions> <short-instructions> <p>Use the tools to label the requested items in the image</p> </short-instructions> </crowd-semantic-segmentation> </crowd-form>
2. Create a JSON file for the labels. Example:
{ "labels": [ { "label": "Chair" }, ... { "label": "Oven" } ] }
3. Create an input manifest file for the images. Example:
{"source-ref":"s3://awsdoc-example-bucket/input_manifest/apartment-chair.jpg"} {"source-ref":"s3://awsdoc-example-bucket/input_manifest/apartment-carpet.jpg"}
4. Upload the HTML, manifest, and JSON files to Amazon Simple Storage Service (Amazon S3). Example:
import boto3 import os bucket = 'awsdoc-example-bucket' prefix = 'GroundTruthCustomUI' boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'customUI.html')).upload_file('customUI.html') boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'input.manifest')).upload_file('input.manifest') boto3.Session().resource('s3').Bucket(bucket).Object(os.path.join(prefix, 'testLabels.json')).upload_file('testLabels.json')
5. Retrieve the Amazon Resource Names (ARNs) for the pre-processing and annotation consolidation Lambda functions. For example, here are the semantic segmentation ARNs:
arn:aws:lambda:eu-west-1:111122223333:function:PRE-SemanticSegmentation
arn:aws:lambda:eu-west-1:111122223333:function:ACS-SemanticSegmentation
6. Create the labeling job using an AWS SDK, such as boto3. Replace these values in the following example:
INPUT_MANIFEST_IN_S3 S3_OUTPUT_PATH IAM_ROLE_ARN LABELS_JSON_FILE_IN_S3 WORKTEAM_ARN HTML_TEMPLATE_IN_S3
import boto3 client = boto3.client('sagemaker') client.create_labeling_job(LabelingJobName='SemanticSeg-CustomUI', LabelAttributeName='output-ref', InputConfig={ 'DataSource': { 'S3DataSource': { 'ManifestS3Uri': 'INPUT_MANIFEST_IN_S3' } }, 'DataAttributes': { 'ContentClassifiers' : [ 'FreeOfPersonallyIdentifiableInformation', ] } }, OutputConfig={ 'S3OutputPath' : 'S3_OUTPUT_PATH' }, RoleArn='IAM_ROLE_ARN', LabelCategoryConfigS3Uri='LABELS_JSON_FILE_IN_S3', StoppingConditions={ 'MaxPercentageOfInputDatasetLabeled': 100 }, HumanTaskConfig={ 'WorkteamArn': 'WORKTEAM_ARN', 'UiConfig': { 'UiTemplateS3Uri' : 'HTML_TEMPLATE_IN_S3' }, 'PreHumanTaskLambdaArn' : 'arn:aws:lambda:eu-west-1:111122223333:function:PRE-SemanticSegmentation', 'TaskKeywords': [ 'SemanticSegmentation', ], 'TaskTitle': 'Semantic Segmentation', 'TaskDescription': 'Draw around the specified labels using the tools', 'NumberOfHumanWorkersPerDataObject': 1, 'TaskTimeLimitInSeconds': 3600, 'TaskAvailabilityLifetimeInSeconds': 1800, 'MaxConcurrentTaskCount': 1, 'AnnotationConsolidationConfig': { 'AnnotationConsolidationLambdaArn': 'arn:aws:lambda:eu-west-1:111122223333:function:ACS-SemanticSegmentation' } }, Tags=[ { 'Key': 'reason', 'Value': 'CustomUI' } ])
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