1. CI/CD Pipelines with Harbor for ML Ops


    To create CI/CD pipelines that incorporate Harbor for ML Ops using Pulumi, we can leverage Harbor resources to set up a project, replication, tasks, and more to manage our container images effectively. 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. Integrating Harbor into CI/CD pipelines can help in ensuring that the Machine Learning (ML) applications are securely managed and deployed.

    Here's how you might approach creating a CI/CD pipeline with Harbor:

    1. Create a Harbor Project: This is a namespace where related repository images are managed. You would have features like vulnerability scanning and content trust.
    2. Configure Tasks: You can specify tasks like image scanning policies which are essential in ML Ops to ensure your ML models are not vulnerable.
    3. Replication Policies: For ML Ops, you might want to replicate container images across multiple registries for availability and performance.
    4. User and Group Management: To control who can access your ML models and images, you can define users and groups with specific permissions.
    5. Webhooks: To trigger workflows in your CI/CD pipeline, you can set up webhooks to notify your systems about certain events like a new image push.

    Now, let's write a Pulumi program to implement a basic Harbor setup for ML Ops using Python. We'll create a project, define replication policies, and set up a task for vulnerability scanning.

    import pulumi import pulumi_harbor as harbor # Create a new project in Harbor for storing ML Ops container images ml_ops_project = harbor.Project("mlOpsProject", name="ml-ops", public="false", # Assuming you want the project to be private registry_id=1, # ID of the registry, typically '1' for the local Harbor registry storage_quota=10737418240, # 10GB storage quota, adjust as needed vulnerability_scanning=True) # Enable vulnerability scanning # Define a replication rule for the project. This will replicate all images tagged with 'prod' to another registry. replication_rule = harbor.Replication("mlOpsReplicationRule", name="replicate-prod-images", action="replicate", enabled=True, # Assuming we want to replicate images with tag 'prod' filters=[{"type": "tag", "value": "prod"}], registry_id=2, # ID of the target registry destination_namespace="ml-ops") # Define a task for scanning the images for vulnerabilities. scan_policy_task = harbor.Tasks("mlOpsScanPolicyTask", vulnerability_scan_policy="Daily", # Set scan policy to 'Daily' project_id=ml_ops_project.id) # Associate with the earlier defined project # Export the Harbor project's name and the replication rule name pulumi.export('ml_ops_project_name', ml_ops_project.name) pulumi.export('ml_ops_replication_rule_name', replication_rule.name)

    In this program:

    • We start by importing the necessary Pulumi libraries.
    • We create a new Harbor project specifically for ML Ops-related container images with vulnerability scanning enabled.
    • We set up a replication policy to replicate images tagged with 'prod' to another registry, enabling continuous delivery.
    • We define a task for daily scanning of images for vulnerabilities, ensuring security in our ML operations.

    Deploying this Pulumi program will give us a foundational Harbor configuration, which you can then integrate into your CI/CD pipeline scripts, for maintaining and deploying ML models.

    To run this program, place it within a Pulumi project, and run pulumi up. Make sure you have access to an instance of Harbor and the necessary credentials configured in your environment.