Amazon Web Services – Using Custom UI Templates in Sagemaker
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' } ]) |
This will result on the following:
Step 6: Now navigate to the ground truth console to see the newly created labeling job.
Now you know how to use a custom UI template with AWS-provided lambda functions in ground truth.
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