1. Consistent Environment for AI Model Development using Harbor


    To create a consistent environment for AI model development using Harbor, you'll need to set up a Harbor instance first, which typically involves deploying Harbor to a Kubernetes cluster. Harbor is an open-source container image registry that secures images with role-based access control, ensures images are scanned and free from vulnerabilities, and signs images as trusted. It can also be used to replicate container images between registries to ensure availability.

    Harbor can be deployed in a cloud environment, which in this case could be a managed Kubernetes service, such as Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), or Azure Kubernetes Service (AKS). Once you have your Kubernetes cluster set up, you can use Pulumi to declare the desired state of Harbor in your infrastructure.

    Let’s go through a high-level overview before diving into the Pulumi Python program:

    1. Set up a Kubernetes Cluster: Before installing Harbor, you need a Kubernetes cluster. This can be done using Pulumi providers such as pulumi_aws, pulumi_azure or pulumi_gcp, depending on your preferred cloud provider.

    2. Install Harbor: Using Pulumi, we can set up Harbor by defining a harbor.Project that includes all necessary configurations, and a harbor.Registry that will store your container images.

    3. User and Group Management: Use the harbor.User and harbor.Group resources to manage users and groups within Harbor.

    4. Configure Security: Adjust security settings using harbor.ConfigSecurity to control access and permissions.

    5. Continuous Image Replication: With harbor.Replication, you can set up continuous image replication between registries.

    6. Task Scheduling: Utilize harbor.Tasks to define tasks such as vulnerability scanning.

    7. Robot Account Management: Manage robot accounts using harbor.RobotAccount, which are accounts for automated processes and services to interact with the Harbor API.

    Below is a simplified example of how you might set up Harbor with Pulumi in Python. Please note that this code assumes you already have a Kubernetes cluster running and configured with Pulumi to manage resources in it.

    import pulumi import pulumi_harbor as harbor # Create a new project in Harbor. project = harbor.Project("ai-model-dev", name="ai-model-development", public="false", registry_id=1 # Assuming '1' corresponds to the internal Harbor registry ID. ) # Create a new user in Harbor. user = harbor.User("ai-developer", username="ai-developer", email="ai-developer@example.com", password="securepassword", # You might want to use Pulumi's config for this. admin="false" ) # Set up a replication rule. This is useful if you want to replicate images from an external registry. replication = harbor.Replication("ai-model-replication", name="replicate-ai-models", action="replicate", enabled=True, registryId=2, # This ID represents the ID of an external registry you may want to replicate from. schedule="0 */2 * * *", # This example schedule runs every two hours. ) # Export the names of the resources. pulumi.export("project_name", project.name) pulumi.export("user_username", user.username) pulumi.export("replication_name", replication.name)

    This is a starting point to manage Harbor; it creates a Harbor project, a user, and a replication rule. Keep in mind that the actual deployment of Harbor requires more configuration and resources, such as persistent volume claims, service accounts, and more, which have been omitted here for brevity’s sake. Depending on your particular needs, the Harbor setup can be customized by adding more details to the resources above or by utilizing other Harbor-related Pulumi resources.