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Red Hat OpenShift AI Self-Managed

Working with the model catalog

Working with the model catalog in Red Hat OpenShift AI Self-Managed

Last Updated: 2025-10-

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Abstract

Discover and evaluate generative AI models in the model catalog and select models to register,

deploy, and customize.

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Table of Contents

PREFACE CHAPTER 1. OVERVIEW OF MODEL REGISTRIES AND MODEL CATALOG 1.1. MODEL REGISTRY 1.2. MODEL CATALOG CHAPTER 2. VIEWING MODELS IN THE MODEL CATALOG CHAPTER 3. REGISTERING A MODEL FROM THE MODEL CATALOG CHAPTER 4. DEPLOYING A MODEL FROM THE MODEL CATALOG 3 4 4 4 5 6 8 Table of Contents

PREFACE As a data scientist in OpenShift AI, you can discover and evaluate the generative AI models that are available in the model catalog. From the model catalog, you can select the models that you want to register, deploy, and customize. PREFACE

CHAPTER 1. OVERVIEW OF MODEL REGISTRIES AND MODEL CATALOG A model registry acts as a central repository for administrators and data scientists to register, version, and manage the lifecycle of AI models before configuring them for deployment. A model registry is a key component for AI model governance. The model catalog provides a curated library where data scientists can discover and evaluate the available generative AI models to find the best fit for their use cases. 1.1. MODEL REGISTRY A model registry is an important component in the lifecycle of an artificial intelligence/machine learning (AI/ML) model, and is a vital part of any machine learning operations (MLOps) platform or workflow. A model registry acts as a central repository, storing metadata related to machine learning models from development to deployment. This metadata ranges from high-level information like the deployment environment and project, to specific details like training hyperparameters, performance metrics, and deployment events. A model registry acts as a bridge between model experimentation and serving, offering a secure, collaborative metadata store interface for stakeholders in the ML lifecycle. Model registries provide a structured and organized way to store, share, version, deploy, and track models. OpenShift AI administrators can create model registries in OpenShift AI and grant model registry access to data scientists. For more information, see Managing model registries. Data scientists with access to a model registry can use it to store, share, version, deploy, and track models. For more information, see Working with model registries. 1.2. MODEL CATALOG Data scientists can use the model catalog to discover and evaluate the models that are available and ready for their organization to register, deploy, and customize. The model catalog provides models from different providers that data scientists can search and discover before they register models in a model registry and deploy them to a model serving runtime. OpenShift AI administrators can configure the available repository sources for models displayed in the model catalog. OpenShift AI provides a default model catalog, which includes models from providers such as Red Hat, IBM, Meta, Nvidia, Mistral AI, and Google. For more information about how data scientists can use the model catalog, see Working with the model catalog. Red Hat OpenShift AI Self-Managed 2.25 Working with the model catalog

CHAPTER 3. REGISTERING A MODEL FROM THE MODEL CATALOG As a data scientist, you can register models directly from the model catalog and create the first version of the new model. Prerequisites You are logged in to Red Hat OpenShift AI. You have access to an available model registry in your deployment. Procedure

1. From the OpenShift AI dashboard, click Models → Model catalog.
2. In the drop-down list, select from the available catalog sources that have been configured by your administrator. The Default Catalog is displayed by default.

NOTE

OpenShift cluster administrators can configure additional model catalog sources. For more details, see the Kubeflow Model Registry community documentation on configuring catalog sources.

3. Use the search bar to find a model in the catalog. You can enter text to search by model name, description, or provider.
4. Click the name of a model to view the model details page.
5. Click Register model.
6. From the Model registry drop-down list, select the model registry that you want to register the model in.
7. In the Model details section, configure details to apply to all versions of the model: a. Optional: In the Model name field, update the name of the model. b. Optional: In the Model description field, update the description of the model.
8. In the Version details section, enter details to apply to the first version of the model: a. In the Version name field, enter a name for the model version. b. Optional: In the Version description field, enter a description for the first version of the model. c. In the Source model format field, enter the name of the model format, for example, ONNX. d. In the Source model format version field, enter the version of the model format.
9. In the Model location section, the URI of the model is displayed.
10. Click Register model. Red Hat OpenShift AI Self-Managed 2.25 Working with the model catalog

Verification The new model details and version are displayed on the Overview tab on the model details page. The new model and version are displayed on the Model registry page. CHAPTER 3. REGISTERING A MODEL FROM THE MODEL CATALOG

This is the name of the inference service created when the model is deployed. b. Optional: Click Edit resource name, and then enter a specific resource name for the model deployment in the Resource name field. By default, the resource name matches the name of the model deployment.

IMPORTANT

Resource names are what your resources are labeled as in OpenShift. Your resource name cannot exceed 253 characters, must consist of lowercase alphanumeric characters or - , and must start and end with an alphanumeric character. Resource names are not editable after creation. The resource name must not match the name of any other model deployment resource in your OpenShift cluster. c. From the Serving runtime list, select a model-serving runtime that is installed and enabled in your OpenShift AI deployment. If project-scoped runtimes exist, the Serving runtime list includes subheadings to distinguish between global runtimes and project-scoped runtimes. d. From the Model framework list, select a framework for your model.

NOTE

The Model framework list shows only the frameworks that are supported by the model-serving runtime that you specified when you deployed your model.

8. From the Deployment mode list, select KServe RawDeployment or Knative Serverless. For more information about deployment modes, see About KServe deployment modes. a. In the Number of model server replicas to deployfield, specify a value. b. From the Model server size list, select a value. c. If you have created a hardware profile, select a hardware profile from the Hardware profile list. If project-scoped hardware profiles exist, the Hardware profile list includes subheadings to distinguish between global hardware profiles and project-scoped hardware profiles.

IMPORTANT

By default, hardware profiles are hidden from appearing in the dashboard navigation menu and user interface. In addition, user interface components associated with the deprecated accelerator profiles functionality are still displayed. To show the Settings → Hardware profiles option in the dashboard navigation menu and the user interface components associated with hardware profiles, set the disableHardwareProfiles value to false in the OdhDashboardConfig custom resource (CR) in OpenShift. For more information about setting dashboard configuration options, see Customizing the dashboard. d. In the Model route section, select the Make deployed models available through an external route checkbox to make your deployed models available to external clients. e. In the Token authentication section, select the Require token authentication checkbox to CHAPTER 4. DEPLOYING A MODEL FROM THE MODEL CATALOG

e. In the Token authentication section, select the Require token authentication checkbox to require token authentication for your model server. To finish configuring token authentication, perform the following actions: i. In the Service account name field, enter a service account name for which the token will be generated. The generated token is created and displayed in the Token secret field when the model server is configured. ii. To add an additional service account, click Add a service account and enter another service account name.

9. In the Source model location section, select Current URI to deploy the selected model from the catalog.
10. Optional: Customize the runtime parameters in the Configuration parameters section: a. Modify the values in Additional serving runtime arguments to define how the deployed model behaves. b. Modify the values in Additional environment variables to define variables in the model’s environment.
11. Click Deploy. Verification The model deployment is displayed on the Models → Model Deployments page. The model deployment is displayed in the Latest deployments section of the model details page. The model deployment is displayed on the Deployments tab for the model version. Additional resources For more information about deployment options on the single-model serving platform, see Deploying models on the single-model serving platform. Red Hat OpenShift AI Self-Managed 2.25 Working with the model catalog