1. Secure Model Checkpointing with S3 Locking Mechanisms


    To enhance the security of model checkpointing to AWS S3, we're going to use several AWS resources that work together to ensure data is not only saved but also that it's immutable and protected from unwanted changes or deletions. The primary resources and services we'll leverage include:

    1. S3 Buckets: S3 Buckets are the basic containers that hold your data. Everything that you store in AWS S3 must be contained within a bucket.

    2. Bucket Versioning: This is used to keep multiple variants of an object in the same bucket. This is helpful to preserve, retrieve, and restore every version of every object stored in your S3 bucket.

    3. Bucket Object Lock: The object lock feature allows you to store objects using a "write once, read many" (WORM) model. It can help prevent objects from being deleted or modified for a fixed amount of time or indefinitely.

    4. Bucket Server-Side Encryption: This feature encrypts your data at the object level as it's written to storage, and decrypts it for you when you access it.

    5. Bucket Policies: Bucket policies provide centralized access control to buckets and objects based on a variety of conditions, including S3 operations, requesters, resources, and aspects of the request (e.g., IP address).

    The following Pulumi program demonstrates how to create an S3 bucket with these security features in place for the purpose of storing and securing model checkpoints. Educating on each step and explaining the reasons behind its use helps understand the best practices for achieving secure model checkpointing.

    import pulumi import pulumi_aws as aws # Create an S3 bucket to hold the model checkpoints. # The bucket name must be globally unique. model_checkpoint_bucket = aws.s3.Bucket("modelCheckpointBucket", # Enables versioning to keep a complete history of your bucket's objects. versioning=aws.s3.BucketVersioningArgs( status="Enabled" ) ) # Enable object lock to enforce the WORM (Write Once Read Many) model. object_lock_configuration = aws.s3.BucketObjectLockConfiguration("modelCheckpointObjectLockConfig", bucket=model_checkpoint_bucket.id, object_lock_enabled="Enabled" ) # Apply server-side encryption by default to all new objects added to the bucket. server_side_encryption_configuration = aws.s3.BucketServerSideEncryptionConfiguration("modelCheckpointServerSideEncryption", bucket=model_checkpoint_bucket.id, # AWS-managed S3 encryption. You could also use KMS keys for SSE. rules=[aws.s3.BucketServerSideEncryptionConfigurationRuleArgs( apply_server_side_encryption_by_default=aws.s3.BucketServerSideEncryptionConfigurationRuleApplyServerSideEncryptionByDefaultArgs( sse_algorithm="AES256" ) )] ) # Bucket policy to deny all actions if the request is not using HTTPS. bucket_policy = aws.s3.BucketPolicy("modelCheckpointBucketPolicy", bucket=model_checkpoint_bucket.id, policy=pulumi.Output.all(model_checkpoint_bucket.id).apply(lambda bucket_id: json.dumps({ "Version": "2012-10-17", "Statement": [{ "Effect": "Deny", "Principal": "*", "Action": "s3:*", "Resource": [ f"arn:aws:s3:::{bucket_id}", f"arn:aws:s3:::{bucket_id}/*" ], "Condition": { "Bool": {"aws:SecureTransport": "false"} } }] })) ) # The following export outputs the S3 bucket endpoint where the model checkpoints are stored. pulumi.export("model_checkpoint_bucket_endpoint", pulumi.Output.concat("https://", model_checkpoint_bucket.bucket_regional_domain_name))

    To summarize this program:

    • We've first created an S3 bucket that will store the model checkpoints.
    • We've enabled versioning for this bucket to ensure that you can access every version of a stored object.
    • We've configured the bucket with an object lock to prevent accidental or malicious deletion or modification of objects.
    • We've applied server-side encryption by default to help protect data at rest.
    • We've added a bucket policy to enforce the use of HTTPS for any request, for additional in-flight security.

    After applying this Pulumi program, you will have created a secure environment on AWS S3 for the storage of model checkpoints, making use of versioning, object locking, encryption, and access controls.