Harbor as a Container Registry for AI Model Serving

Question

Pulumi · Accepted Answer

Harbor is an open-source container image registry that secures images with role-based access control, scans images for vulnerabilities, and signs images as trusted. It facilitates better management and deployment of containers, which is vital for modern cloud-native applications. Pulumi provides resources to manage Harbor, allowing you to define, deploy, and manage Harbor as a container registry through code.

Below, we'll write a Pulumi program in Python to deploy a Harbor instance that can be used as a Container Registry. We will create a Harbor project, define users, and set replication policies.

1. **Harbor Project**: A project in Harbor is a grouping of repositories of container images. We'll start by creating a project that will hold our AI model serving container images.

2. **Harbor User**: For managing access, we'll define users that can push or pull images from our Harbor project.

3. **Harbor Replication**: Replication policies in Harbor allow the synchronization of images from one Harbor registry to another, enabling image backup or multi-site deployments.

4. **Storage and Security Settings**: We will also configure storage quotas and vulnerability scanning for our Harbor project.

Let's start by writing our Pulumi program:

```python
import pulumi
import pulumi_harbor as harbor

# Create a Harbor project for AI model serving images.
# A project in Harbor is the organizational unit grouping container repositories.
ai_project = harbor.Project("aiModelServing",
                            name="ai-model-serving",  # Name of the Harbor project
                            public=True,  # Defines whether the project is public
                            storageQuota=-1)  # No limit on storage quota, can be set to a specific value in MB.

# Create a user in Harbor with access to the project.
ai_user = harbor.User("aiUser",
                      username="modeluser",
                      password="VeryS3cr3tP@ssw0rd",  # Ideally, use secret management for passwords.
                      email="modeluser@example.com",
                      realname="AI Model User",
                      admin=False)  # The user is not an admin.

# Set up replication from our Harbor project to another registry (assuming another registry is set up).
replication_rule = harbor.Replication("aiModelServingReplication",
                                      name="replicate-ai-models",
                                      action="push",  # Define the replication action, can be "push" or "pull".
                                      registryId=1,  # ID of the target registry for replication.
                                      destNamespace="backup-namespace",  # Namespace in the target registry.
                                      override=True,  # Whether to override the replicate resources if they exist.
                                      enabled=True)  # Enable the replication rule.

# Export the Harbor project and user information, including project ID and user ID.
# These outputs can be used for further automation or integration with CI/CD pipelines.
pulumi.export("project_id", ai_project.id)
pulumi.export("user_id", ai_user.id)
pulumi.export("replication_id", replication_rule.id)
```

This program uses Pulumi to set up a Harbor container registry with necessary configurations for AI model serving. The `pulumi.export` statements at the end of the program make it easy to obtain IDs for our project, user, and replication rule, which can be handy for integration.

**Further notes**:
- This example assumes you have your cloud environment and Pulumi set up.
- Store sensitive information like passwords securely, preferably using Pulumi's secret management or an external secrets provider.
- The `registryId` in `harbor.Replication` refers to another registry we'd replicate to. Update this to match your specific target registry's ID.
- Ensure to review and adhere to best practices for permissions and user management, especially when handling production environments.
- The default for `public` is set to `True` for demonstration purposes. Adjust this based on your use case, setting it to `False` for private projects.