1. Harbor as a Registry for Versioned AI Model Containers

    Python

    To use Harbor as a registry for versioned AI model containers, you would typically set up Harbor projects, where you can manage the lifecycle of Docker images representing your AI models. Harbor provides many features, such as vulnerability scanning, content trust, user management, and replication policies, which helps in automating and securing your container registry workflows.

    Here’s how you might use Pulumi to create a Harbor project with configurations that could be suitable for handling versioned AI model containers:

    1. harbor.Project: This resource is used to create a new project within Harbor. Projects in Harbor are used to group container repositories and define policies for them, such as who can access the data or how the data is scanned for vulnerabilities.

    2. harbor.User: Managing users is crucial for controlling access to projects. Harbor users can push/pull Docker images to/from your Harbor registry.

    3. harbor.Replication: An optional resource, useful if you want to set up policies to replicate images between Harbor and other registries.

    4. harbor.Tasks: This resource might be used to define vulnerability scan tasks which could be part of your CI/CD pipeline to ensure that images are scanned before being deployed.

    You should have the Pulumi CLI set up and configured to use your preferred cloud provider. Now, let's proceed with a Python program that sets up a basic Harbor project, user, and policy for managing AI model containers.

    import pulumi import pulumi_harbor as harbor # Create a new Harbor project where the AI model containers will reside. ai_models_project = harbor.Project("aiModelsProject", name="ai-models", public="false" # Set to true if you want the project repositories to be accessible without authentication. ) # Create a Harbor user who can manage the AI model containers. ai_model_user = harbor.User("aiModelUser", username="ai_model_user", email="ai_model_user@example.com", password="ChangeMe#", # Please use secrets management in a real-world scenario. admin="true" # Grants admin privileges to the user over all Harbor functionalities. ) # Optionally, set up a replication policy if needed. replication_policy = harbor.Replication("aiModelsReplicationPolicy", name="ReplicateAIModels", action="replicate", enabled=True, registry_id=1, # Assuming '1' is the ID of the registry you want to replicate with. dest_namespace="ai-models-destination", overrides=True # Image replicates override the images on the destination registry. ) # Exports the project URL which might be used to configure your CI/CD workflow. pulumi.export("project_url", pulumi.Output.concat("https://your-harbor-domain.com/", ai_models_project.name))

    This code will initialize a Pulumi stack that sets up a Harbor project for AI models, a user with admin rights to manage that project, and a replication policy.

    Here's a brief explanation of what this does:

    • Harbor Project: Sets up a private project by default. If you want the repositories in this project to be public, you can set the public property to true.
    • Harbor User: Creates a user that can push or pull images to the project. In production scenarios, you should manage the user's password as a secret (e.g., by using Pulumi’s secret management).
    • Replication Policy: Defines a policy to replicate the Docker images from this Harbor instance to another registry. This could be useful for redundancy or backup purposes.

    When running this code, Pulumi would prompt you for the details (e.g., Harbor endpoint, admin credentials) assuming these are not already set up in the environment. You will need to ensure that the Harbor provider is configured correctly with access to set up these resources.

    Remember to run pulumi up to create the stack, pulumi preview to see what operations will be performed, and pulumi destroy when you want to delete the resources managed by the stack.


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