Amazon Web Services - Using Custom UI Templates in Sagemaker

Last Updated : 28 Mar, 2023

In this article, we will look into how you can use a custom UI template with AWS Lambda functions in Amazon Sagemaker ground truth.

Here we will be using a Jupyter notebook to create a ground truth labeling job using a custom UI template with AWS-provided lambda functions using an AWS SDK such as boto3. To do so follow the below steps:

Step 1: First create a custom UI template file named customui.html to create a semantic segmentation job. A sample code has been given below:

HTML

<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>Inspect the Image</li> <li>Add appropriate label for 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>

Step 2: Now create a JSON file named testlabels.json for the labels and an input manifest file named input.manifest for the images in Amazon Simple Storage Service.

Step 3: Upload the HTML, manifest, and JSON files to amazon simple storage service by executing this python code:

Python3

import boto3 import os bucket = 'labelingjobdemoinputz' prefix ='GroundTruthCustom' 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')

Step 4: Retrieve the Amazon Resource Names(ARNs) for the pre-processing and annotation consolidation Lambda functions provided by AWS. For example here is the semantic segmentation ARNs:

Step 5: Now create a labeling job using an AWS SDK. Make sure to replace the default variables in the script with the corresponding amazon S3 file paths and ARNs.

Python3

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' : 'YOUR_ARNs_HERE', 'TaskKeywords': [ 'SemanticSegmentation', ], 'TaskTitle': 'Semantic Segmentation', 'TaskDescription': 'Draw around the specified labels using the tools', 'NumberOfHumanWorkersPerDataObject': 1, 'TaskTimeLimitInSeconds': 3600, 'TaskAvailabilityLifetimeInSeconds': 1800, 'MaxConcurrentTaskCount': 1, 'AnnotationConsolidationConfig': { 'AnnotationConsolidationLambdaArn': 'YOUR_ARNs_HERE } }, Tags=[ { 'Key': 'reason', 'Value': 'CustomUI' } ])