CI/CD Pipelines with Harbor for ML Ops

Question

Pulumi · Accepted Answer

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.

```python
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.