1. Expiration of Outdated AI Model Artifacts in S3

    Python

    To ensure that outdated AI model artifacts in an S3 bucket are automatically deleted after a certain period, you can use the AWS S3 Bucket Lifecycle Configuration. The lifecycle configuration allows you to define rules for managing objects' lifecycle, such as how long to keep the objects and when to delete them.

    Below is a Python program for Pulumi, designed to create an S3 bucket and apply lifecycle rules to expire objects after a specified number of days. It demonstrates how to:

    1. Create an S3 bucket using aws.s3.Bucket.
    2. Apply lifecycle rules with aws.s3.BucketLifecycleConfigurationV2.

    The lifecycle rule in this example specifies that objects with the prefix 'ai-models/' will expire 30 days after creation. You can adjust the prefix and the number of days according to your needs.

    Let's walk through the code:

    import pulumi import pulumi_aws as aws # Create an S3 bucket to store AI model artifacts. ai_model_bucket = aws.s3.Bucket("aiModelBucket") # Define the lifecycle rules for the S3 Bucket. # In this example, AI model artifacts that are older than 30 days are removed from the bucket. # The expiration in days is set by the 'days' parameter under the 'expiration' property. lifecycle_rule = aws.s3.BucketLifecycleConfigurationV2("aiModelBucketLifecycleRules", bucket=ai_model_bucket.id, rules=[aws.s3.BucketLifecycleConfigurationV2RuleArgs( id="expireAIArtifacts", filter=aws.s3.BucketLifecycleConfigurationV2FilterArgs( prefix="ai-models/", # Only applies to objects prefixed with 'ai-models/'. ), status="Enabled", expiration=aws.s3.BucketLifecycleConfigurationV2ExpirationArgs( days=30 # Set the expiration to 30 days. ), )] ) # Export the name of the bucket and the ID of the lifecycle rule. pulumi.export("bucket_name", ai_model_bucket.bucket) pulumi.export("lifecycle_rule_id", lifecycle_rule.id)

    In this program:

    • We first create an S3 bucket named aiModelBucket, which will hold our AI model artifacts.
    • We then define a lifecycle rule for the bucket using aws.s3.BucketLifecycleConfigurationV2. We name the rule expireAIArtifacts and set it to target objects with the prefix ai-models/. The status is set to Enabled, meaning the rule is active.
    • The important part of this rule is the expiration setting, which is configured to automatically delete files 30 days after they've been uploaded.

    This Pulumi program provides a scalable and automated way to manage AI artifacts and assist in the housekeeping of your S3 buckets to avoid excessive storage costs for outdated models.

    For more details on how to use the AWS provider and S3 lifecycle configurations in Pulumi, you can refer to the Pulumi AWS S3 Lifecycle Documentation.


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