Machine learning
Amazon SageMaker Unified Studio is a unified development experience for building analytics, AI/ML, and generative AI applications at scale. This chapter describes the Amazon SageMaker AI capabilities that you can use in Amazon SageMaker Unified Studio.
Note
When you add a custom tag to a SageMaker AI resource (such as a training job, inference
endpoint, model, or pipeline), add the prefix ProjectUserTag
to the tag
name. For example:
ProjectUserTagMyCustomTag
Note
ECR repositories must be created with the AmazonDataZoneProject
tag with
the project ID (which can be found under project details in the project overview page or
from the page URL) as the tag value. If you want to add your own tags, they must be
prefixed with ProjectUserTag
.
For example, with AWS CLI:
aws ecr create-repository \ --repository-name my-repo \ --tags \ Key=AmazonDataZoneProject,Value=5blxelum5cmckb \ Key=ProjectUserTagMyTag,Value=MyTagValue \
Example using Jupyterlab notebook:
import boto3 # Create ECR client ecr_client = boto3.client('ecr') # Define repository name repository_name = 'my-ecr-repo' # Define tags tags = [ { 'Key': 'AmazonDataZoneProject', 'Value': '5blxelum5cmckb' }, { 'Key': 'ProjectUserTagMyTag', 'Value': 'MyTagValue' }, ] try: # Create the repository with tags response = ecr_client.create_repository( repositoryName=repository_name, imageScanningConfiguration={ 'scanOnPush': True }, encryptionConfiguration={ 'encryptionType': 'AES256' }, tags=tags ) repository_uri = response['repository']['repositoryUri'] print(f"Repository created successfully!") print(f"Repository URI: {repository_uri}") except ecr_client.exceptions.RepositoryAlreadyExistsException: print(f"Repository {repository_name} already exists") # Add tags to existing repository ecr_client.tag_resource( resourceArn=f"arn:aws:ecr:{ecr_client.meta.region_name}:{boto3.client('sts').get_caller_identity()['Account']}:repository/{repository_name}", tags=tags ) # Get the repository URI response = ecr_client.describe_repositories(repositoryNames=[repository_name]) repository_uri = response['repositories'][0]['repositoryUri'] print(f"Added tags to existing repository") print(f"Repository URI: {repository_uri}") except Exception as e: print(f"Error creating repository: {str(e)}")
ECR repositories without the AmazonDataZoneProject
cannot be used. You
must create new ECR repositories with the AmazonDataZoneProject
tag. Once
tagged with the AmazonDataZoneProject
tag, this tag cannot be modified or
removed from your ECR repositories. For more information about ECR repositories, see
https://docs.aws.amazon.com/AmazonECR/latest/userguide/what-is-ecr.html.