1. Secure Model Artifact Storage with AWS ECR


    Amazon Elastic Container Registry (ECR) is a fully-managed container image registry service. It allows users to store, manage, and deploy Docker container images. It is secure, scalable, and integrated with other AWS services.

    In the context of machine learning, "model artifacts" refer to the outputs of the training process, which can include the trained model itself, any supporting scripts, and additional data and metadata. Storing these artifacts in a secure and managed environment like ECR ensures that they are safe, versioned, and can be easily deployed to production systems.

    To use ECR for storing model artifacts, you would typically:

    1. Create an ECR repository to store your container images.
    2. Configure authentication to push to and pull from the repository.
    3. Push your Docker images to the ECR repository.
    4. Optionally, set up a repository policy to manage permissions and control access to your images.

    Below is a Pulumi program in Python that sets up an ECR repository with a policy to enable automated scanning of your container images for vulnerabilities on 'push' events. The program will also output the repository URL, which can be used in your CI/CD pipelines to push and pull images.

    import pulumi import pulumi_aws as aws # Create an ECR repository to store images repository = aws.ecr.Repository("my_model_artifacts_repository", image_scanning_configuration=aws.ecr.RepositoryImageScanningConfigurationArgs( scan_on_push=True, ), image_tag_mutability='MUTABLE') # Define an ECR repository policy to manage access to the repository. # For demonstration purposes, this policy allows full access for all actions to AWS authenticated users. # This policy should be modified to suit your organization's security requirements. # See the ECR Repository Policy documentation for more detailed examples: # https://www.pulumi.com/docs/reference/pkg/aws/ecr/repositorypolicy/ repository_policy = aws.ecr.RepositoryPolicy("my_model_artifacts_repository_policy", repository=repository.name, policy=repository.arn.apply( lambda arn: json.dumps({ "Version": "2008-10-17", "Statement": [ { "Sid": "AllowPushPull", "Effect": "Allow", "Principal": "*", "Action": [ "ecr:GetDownloadUrlForLayer", "ecr:BatchGetImage", "ecr:BatchCheckLayerAvailability", "ecr:PutImage", "ecr:InitiateLayerUpload", "ecr:UploadLayerPart", "ecr:CompleteLayerUpload" ], "Condition": { "StringEquals": { "aws:SourceArn": arn } } } ] }) ) ) # Export the repository URL for later use in CI/CD pipelines. pulumi.export("repository_url", repository.repository_url)

    This program does the following:

    • Imports the necessary libraries (pulumi for Pulumi's core functionality and pulumi_aws for AWS).
    • Creates a new ECR repository called my_model_artifacts_repository, with image scanning enabled.
    • Defines a repository policy which, in this example, grants full access to all actions for AWS authenticated users on this repository. You should create a policy that aligns with your organization's requirements.
    • Exports the ECR repository URL as repository_url.

    You can use the repository_url output of this Pulumi program in your CI/CD pipeline scripts to push Docker images containing your machine learning model artifacts to ECR. Once the images are pushed, they will be automatically scanned for vulnerabilities if this feature has been enabled.

    Please remember to adjust the repository policy to match your security requirements. The demonstration policy here is too permissive for a production environment.