1. Stale AI Model Image Cleanup with AWS ECR Lifecycle Policies

    Python

    AWS Elastic Container Registry (ECR) is a managed Docker container registry that allows you to store, manage, and deploy Docker container images. ECR supports lifecycle policies for managing images, allowing you to automatically clean up stale images that are no longer needed. By defining rules in these policies, you can control which images are retained and which are deleted over time.

    When working with AI models, as you iterate and improve them, you may generate numerous Docker images. Over time, some of these images become outdated and are no longer used. Stale images take up storage space and can incur unnecessary costs. To address this, you can create a lifecycle policy in AWS ECR that specifies criteria for automatic image deletion, such as keeping only the most recent images or deleting images that haven't been used in a certain period.

    Below is an example of how you might define a Pulumi program in Python to create an AWS ECR repository and apply a lifecycle policy to automatically clean up stale images.

    In this program, we'll do the following:

    1. Create an AWS ECR repository where your Docker images will be stored.
    2. Define a lifecycle policy that will automatically delete images based on specified rules. For instance, the policy might delete images that are older than a certain number of days or keep only a specific number of the most recent images.

    Let's walk through the Pulumi code that sets this up:

    import pulumi import pulumi_aws as aws # Create an ECR repository to store your AI model images. ecr_repo = aws.ecr.Repository("my_ai_models_ecr_repository") # Define the ECR lifecycle policy. # This particular policy JSON will keep only the 5 most recent images and delete others. lifecycle_policy_json = """{ "rules": [ { "rulePriority": 1, "description": "Keep only 5 most recent images", "selection": { "tagStatus": "any", "countType": "imageCountMoreThan", "countNumber": 5 }, "action": { "type": "expire" } } ] }""" # Attach the lifecycle policy to the ECR repository. ecr_lifecycle_policy = aws.ecr.LifecyclePolicy("my_ai_models_lifecycle_policy", policy=lifecycle_policy_json, repository=ecr_repo.name ) # Export the url of the repository to access it when pushing or pulling images. pulumi.export("repository_url", ecr_repo.repository_url)

    In this code:

    • We create an ECR repository called my_ai_models_ecr_repository using the aws.ecr.Repository class.
    • We define a JSON string (lifecycle_policy_json) that captures the rules for our lifecycle policy. The rule stated above ensures that only the most recent five images are retained and older images are expired.
    • We create a lifecycle policy resource (my_ai_models_lifecycle_policy) and attach the previously defined JSON policy to the ECR repository.
    • Finally, we export the URL of the ECR repository, which is used to push or pull images.

    By applying this policy to your ECR repository, you help to maintain a cleaner, more efficient registry that holds only the images you need, saving storage costs and simplifying your image management process.

    For more information about AWS ECR and lifecycle policies, you can check the ECR Lifecycle Policies documentation.


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