Step 1: Before You Begin
Before you begin using the SageMaker console to create a labeling job, you must set up the dataset for use. Do this:
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Save two images at publicly available HTTP URLs. The images are used when creating instructions for completing a labeling task. The images should have an aspect ratio of around 2:1. For this exercise, the content of the images is not important.
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Create an Amazon S3 bucket to hold the input and output files. The bucket must be in the same Region where you are running Ground Truth. Make a note of the bucket name because you use it during step 2.
Ground Truth requires all S3 buckets that contain labeling job input image data have a CORS policy attached. To learn more about this change, see CORS Permission Requirement.
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You can create an IAM role or let SageMaker create a role with the AmazonSageMakerFullAccess IAM policy. Refer to Creating IAM roles and assign the following permissions policy to the user that is creating the labeling job:
{ "Version": "2012-10-17", "Statement": [ { "Sid": "sagemakergroundtruth", "Effect": "Allow", "Action": [ "cognito-idp:CreateGroup", "cognito-idp:CreateUserPool", "cognito-idp:CreateUserPoolDomain", "cognito-idp:AdminCreateUser", "cognito-idp:CreateUserPoolClient", "cognito-idp:AdminAddUserToGroup", "cognito-idp:DescribeUserPoolClient", "cognito-idp:DescribeUserPool", "cognito-idp:UpdateUserPool" ], "Resource": "*" } ] }