Integrate Private Registry with Model Library

This guide explains how to integrate your organization's private container registry with the AI Factory Model Library.

Once integrated, your custom-built or internally-approved model images will appear in the Model Library UI, ready to deploy into Model Serving.

Who should use this guide?

• AI platform admins responsible for container registry governance.
• DevOps engineers managing private registries for AI images.
• Developers needing to deploy private AI models through AI Factory.

What this enables

• You can register private registries with Hybrid Control Plane (HCP).
• HCP can discover AI model images in your registry.
• These images will be available in the Model Library for deployment to Model Serving.
• You can control visibility and usage of private model images across your AI Factory workloads.

Estimated time to complete

10–15 minutes per registry configuration.

Prerequisites

Before you begin:

• You must have admin access to your private container registry (AWS ECR, GCP GCR, Azure ACR, Harbor, or similar).
• You must have admin access to HCP (to configure registry integration).
• You must know the required credentials (username/password or token) for your registry.
• Your registry must expose a compatible Docker Registry API v2 endpoint.

Supported registry types

• AWS Elastic Container Registry (ECR)
• Google Container Registry (GCR) / Artifact Registry
• Azure Container Registry (ACR)
• Harbor
• Generic registries supporting Docker Registry API v2

Integration Steps

1. Prepare registry credentials

• For public registries → no authentication required.

• For private registries → prepare one of:

  • Username/password pair
  • Personal access token
  • Robot account credentials (Harbor)

Confirm you can perform a docker pull of your model images locally using these credentials.

2. Register your registry in HCP

• In the HCP UI, go to:

AI Factory > Model Library > Manage Repositories > Add Registry

You will see a dialog prompting for:

• Fill in the required fields.
• Click Add Registry.

3. Verify registry integration

After adding:

• HCP will attempt to connect to the registry and validate credentials.
• If successful, your registry will appear in the Manage Repositories list.
• HCP will begin periodic sync of repository tags from this registry.

4. Define Repository Rules

To control which repositories/tags are discovered:

• After adding the registry, configure Repository Rules.
• See: Define Repository Rules for Model Library

5. Validate image availability

After sync completes:

• Go to AI Factory > Model Library.
• Select the Registry scope or Repository filter.
• Confirm your private model images appear with correct tags.

You can now deploy these images via the normal Deploy to Model Serving flow.

Tips & Best Practices

• Use robot accounts or token-based auth when possible to avoid exposing personal credentials.
• Limit discovery scope via Repository Rules — avoid syncing the entire registry.
• Tag images clearly with version info to aid selection in Model Library.
• For multi-tenant environments, segment registry visibility carefully.

Troubleshooting

Registry connection failed

• Check Registry URL (must not include https://, just the hostname).
• Validate credentials by testing docker login manually.
• Ensure Registry API v2 is enabled.

Images not appearing

• Check that Repository Rules allow the relevant repository.
• Verify image has a valid tag.
• Confirm periodic sync has completed.

Authentication errors

• For AWS ECR → ensure IAM permissions allow ecr:GetAuthorizationToken.
• For Azure ACR → ensure token has acrPull role.
• For GCR → use a service account with Artifact Registry access.

Summary

• You can integrate private registries with Model Library.
• Your private model images become available to deploy via Model Serving.
• You can govern visibility via Repository Rules.
• Model Library unifies both public and private image sources for your AI workloads.

Related Links

• Model Library Explained
• Define Repository Rules for Model Library
• Image and Model Library Explained
• Deploy AI Models from Model Library