Working with model registries

Red Hat OpenShift AI Self-Managed 2.25

Working with model registries in Red Hat OpenShift AI Self-Managed

Abstract

As a data scientist in OpenShift AI, you can store, share, version, deploy, and track models using the model registry feature.

Preface
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As a data scientist in OpenShift AI, you can store, share, version, deploy, and track models using the model registry feature.

Chapter 1. Overview of model registries and model catalog
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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
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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
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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.

Chapter 2. Working with model registries
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2.1. Registering a model from the dashboard
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As a data scientist, you can register a model from the OpenShift AI dashboard 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

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that you want to register a model in.
Click Register model.
The Register model dialog opens.
In the Model details section, configure details to apply to all versions of the model:
1. In the Model name field, enter a name for the model.
2. Optional: In the Model description field, enter a description for the model.
In the Version details section, enter details to apply to the first version of the model:
1. In the Version name field, enter a name for the model version.
2. Optional: In the Version description field, enter a description for the first version of the model.
3. In the Source model format field, enter the name of the model format, for example, ONNX.
4. In the Source model format version field, enter the version of the model format.
In the Model location section, specify the location of the model by providing either object storage details, or a URI.
1. To provide object storage details, ensure that the Object storage radio button is selected.
  1. To autofill the details of an existing connection:
    Click Autofill from connection.
    In the Autofill from connection dialog that opens, from the Project drop-down list, select the data science project that contains the connection.
    From the Connection name drop-down list, select the connection that you want to use.
    This list contains only object storage types which contain a bucket.
    Click Autofill.
  2. Alternatively, manually fill out your object storage details:
    In the Endpoint field, enter the endpoint of your S3-compatible object storage bucket.
    In the Bucket field, enter the name of your S3-compatible object storage bucket.
    In the Region field, enter the region of your S3-compatible object storage account.
    In the Path field, enter a path to a model or folder. This path cannot point to a root folder.
2. To provide a URI, ensure that the URI radio button is selected.
  1. In the URI field, enter the URI for the model.
    Important
    Deployment of models that are registered by using a URI is currently supported for public OCI repositories only.
Click Register model.

Verification

The new model and version details are displayed on the Details tab for the model version.
The new model and version are displayed on the Model registry page.

2.2. Registering a model from the model catalog
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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

From the OpenShift AI dashboard, click Models → Model catalog.
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.
Use the search bar to find a model in the catalog. You can enter text to search by model name, description, or provider.
Click the name of a model to view the model details page.
Click Register model.
From the Model registry drop-down list, select the model registry that you want to register the model in.
In the Model details section, configure details to apply to all versions of the model:
1. Optional: In the Model name field, update the name of the model.
2. Optional: In the Model description field, update the description of the model.
In the Version details section, enter details to apply to the first version of the model:
1. In the Version name field, enter a name for the model version.
2. Optional: In the Version description field, enter a description for the first version of the model.
3. In the Source model format field, enter the name of the model format, for example, ONNX.
4. In the Source model format version field, enter the version of the model format.
In the Model location section, the URI of the model is displayed.
Click Register model.

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.

2.3. Registering a model version
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You can register a new model version.

Prerequisites

You have logged in to Red Hat OpenShift AI.
You have access to an available model registry in your deployment.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that you want to register a model version in.
In the Model name column, click the name of the model that you want to register a new version of.
The details page for the model opens.
Click the Versions tab, and then click Register new version.
In the Version details section, enter details to apply to this version of the model:
1. In the Version name field, enter a name for the model version.
2. Optional: In the Version description field, enter a description for this version of the model.
3. In the Source model format field, enter the name of the model format, for example, ONNX.
4. In the Source model format version field, enter the version of the model format.
In the Model location section, specify the location of the model by providing either object storage details, or a URI.
1. To provide object storage details, ensure that the Object storage radio button is selected.
  1. To autofill the details of an existing connection:
    Click Autofill from connection.
    In the Autofill from connection dialog that opens, from the Project drop-down list, select the data science project that contains the connection.
    From the Connection name drop-down list, select the connection that you want to use.
    This list contains only object storage types which contain a bucket.
    Click Autofill.
  2. Alternatively, manually fill out your object storage details:
    In the Endpoint field, enter the endpoint of your S3-compatible object storage bucket.
    In the Bucket field, enter the name of your S3-compatible object storage bucket.
    In the Region field, enter the region of your S3-compatible object storage account.
    In the Path field, enter a path to a model or folder. This path cannot point to a root folder.
2. To provide a URI, ensure that the URI radio button is selected.
  1. In the URI field, enter the URI for the model.
    Important
    Deployment of models that are registered by using a URI is currently supported for public OCI repositories only.
Click Register new version.

Verification

The new model version is displayed in the Latest versions section on the Overview tab on the model details page.
The new model version is displayed in the Latest version column on the Model registry page.

2.4. Viewing registered models
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You can view the details of models registered in OpenShift AI, such as registered versions, deployments, and metadata associated with the model.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered model.
You have access to the model registry that contains the model that you want to view.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that contains the model that you want to view.
The Model registry page provides a high-level view of registered models, including the model name, latest version, deployments, labels, last modified timestamp, and owner of each model.
Models are sorted by their Last modified timestamp by default.
Use the search bar to find a model in the list. You can filter with a keyword by default by entering a model name, description, or label. Alternatively, click the search bar drop-down list and select Owner to filter by entering a model owner.
Searching by keyword performs a search across the name, description, and labels of registered models and their versions.
Click the name of a model to view the details page for the model:
1. On the Overview tab, you can view model metadata such as labels, description, owner, model ID, last modified and created timestamps, and custom properties, along with latest versions and deployments.
2. On the Versions tab, you can view the registered versions of the model.
3. On the Deployments tab, you can view deployments initiated from the model registry for this model.

Verification

You can view information about the selected model on the details page for the model.

2.5. Viewing registered model versions
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You can view the details of model versions that are registered in OpenShift AI, such as the version metadata and deployment information.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered model.
You have access to the model registry that contains the model version that you want to view.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that contains the model version that you want to view.
Click the name of a model to view Overview tab on the model details page, which includes the latest model versions and deployments.
On the Versions tab, you can view the registered versions of the model.
Versions are sorted by their Last modified timestamp by default.
Use the search bar to find a version in the list. You can filter with a keyword by default by entering a model name, description, or label. Alternatively, click the search bar drop-down list and select Author to filter by entering a model owner.
Searching by keyword performs a search across the name, description, and labels of registered models and their versions.
Click the name of a version to view the version details page.
On the Details tab, you can view the Version details metadata, such as labels, description, custom properties, version ID, author, and last modified and registered timestamps. This also includes where the model is registered from, model location, and model format information.
You can also click Model details to view non-version metadata, such as labels, description, owner, model ID, last modified and created timestamps, and custom properties.
On the Deployments tab, you can view deployments initiated from the model registry for this version.
1. Click the name of a deployment to open its metrics page.
  For information about model metrics on the single-model serving platform, see Viewing performance metrics for a deployed model. For information about model metrics on the multi-model serving platform, see Viewing model-serving runtime metrics for the multi-model serving platform.

Verification

You can view the details of registered model versions on the Model registry page.

2.6. Editing model metadata in a model registry
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You can edit the metadata of models registered in OpenShift AI, such as the model description, labels, and custom properties. Editing model metadata affects all versions of the model.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered model.
You have access to the model registry that contains the model that you want to edit.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that contains the model that you want to edit.
The Model registry page provides a high-level view of registered models, including the model name, latest version, deployments, labels, last modified timestamp, and owner of each model.
Click the name of a model to view the model details page.
On the Overview tab, you can edit metadata for the model.
1. In the Labels section, click Edit to edit the labels of the model, for example, text-to-text.
2. In the Description section, click Edit to edit the description of the model.
3. In the Properties section, click Add property to add a new property to the model, for example, Key: license, Value: apache.
  Tip
  If you enter any property value as a URL, this is displayed as a clickable link in the Properties section, for example: https://www.apache.org/licenses/LICENSE-2.0.
  1. To edit an existing property, click the action menu (⋮) beside the property, and then click Edit.
  2. To delete a property, click the action menu (⋮) beside the property, and then click Delete.

Verification

You can view the updated metadata on the details page for the model.

2.7. Editing model version metadata in a model registry
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You can edit the metadata of model versions that are registered in OpenShift AI, such as the version’s description, labels, and custom properties. Editing model version metadata affects that model version only.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered model.
You have access to the model registry that contains the model version that you want to edit.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that contains the model version that you want to edit.
Click the name of a model to view the model details page.
Click the Versions tab to view the available model versions.
Click a version name to view the version details page.
In the Version details section, you can edit the version metadata.
1. In the Labels section, click Edit to edit the labels of the version, for example, text-to-text.
2. In the Description section, click Edit to edit the description of the version.
3. In the Properties section, click Add property to add a new property to the version, for example, Key: license, Value: apache.
  Tip
  If you enter any property value as a URL, this is displayed as a clickable link in the Properties section, for example: https://www.apache.org/licenses/LICENSE-2.0.
  1. To edit an existing property, click the action menu (⋮) beside the property, and then click Edit.
  2. To delete a property, click the action menu (⋮) beside the property, and then click Delete.
4. In the Model format section, click Edit to edit the format of the model version, for example, ONNX.
5. In the Model format version section, click Edit to edit the format version of the model version.

Verification

You can view the updated metadata on the details page for the model version.

2.8. Deploying a model version from a model registry
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You can deploy a version of a registered model directly from a model registry.

Prerequisites

An available model registry exists in your deployment, and contains at least 1 registered model.
To deploy a model version by using the single-model serving platform, you have fulfilled the prerequisites described in Deploying models on the single-model serving platform.
To deploy a model version by using the multi-model serving platform, you have fulfilled the prerequisites described in Deploying a model by using the multi-model serving platform.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry from which you want to deploy a model version.
In the Model name column, click the name of the model that contains the version that you want to deploy.
The details page for the model version opens.
Click the action menu (⋮) beside the model version that you want to deploy.
Click Deploy.
In the Deploy model dialog, configure properties for deploying the model.
1. From the Project drop-down list, select a target project.
2. Click Deploy.
Configure the following properties for deploying your model:
1. From the Project drop-down list, select a project in which to deploy your model.
2. Optional: In the Model deployment name field, enter a unique name for your model deployment. This field is autofilled with a value that contains the model name by default.
  This will be the name of the inference service that is created when the model is deployed.
Configure the remaining properties for deploying your model, as described in Deploying a model by using the multi-model serving platform or Deploying models on the single-model serving platform.
1. 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 version is displayed on the Deployments tab for the model.
You can edit the model version deployment by clicking the action menu (⋮) beside it, and then clicking Edit.
You can delete the model version deployment by clicking the action menu (⋮) beside it, and then clicking Delete.

2.9. Deploying a model from the model catalog
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You can deploy models directly from the model catalog.

Note

OpenShift AI model serving deployments use the global cluster pull secret to pull models in ModelCar format from the catalog.

For more information about using pull secrets in OpenShift, see Updating the global cluster pull secret in the OpenShift documentation.

Prerequisites

You have completed the prerequisites in Deploying models on the single-model serving platform.
The model registry component is enabled in your OpenShift AI deployment. For more information, see Enabling the model registry component.

Procedure

From the OpenShift AI dashboard, click Models → Model catalog.
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.
Use the search bar to find a model in the catalog. You can enter text to search by model name, description, or provider.
Click the name of a model to view the model details page.
Click Deploy model to display the Deploy model dialog.
From the Project drop-down list, select a project in which to deploy your model.
Note
Models using OCI storage can only be deployed on the single-model serving platform. Projects using the multi-model serving platform do not appear in the project list.
In the Model deployment section:
1. Optional: In the Model deployment name field, enter a unique name for your model deployment. This field is autofilled with a value that contains the model name by default.
  This is the name of the inference service created when the model is deployed.
2. 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.
3. 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.
4. 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.
From the Deployment mode list, select KServe RawDeployment or Knative Serverless. For more information about deployment modes, see About KServe deployment modes.
1. In the Number of model server replicas to deploy field, specify a value.
2. From the Model server size list, select a value.
3. 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.
4. 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.
5. 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:
  1. 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.
  2. To add an additional service account, click Add a service account and enter another service account name.
In the Source model location section, select Current URI to deploy the selected model from the catalog.
Optional: Customize the runtime parameters in the Configuration parameters section:
1. Modify the values in Additional serving runtime arguments to define how the deployed model behaves.
2. Modify the values in Additional environment variables to define variables in the model’s environment.
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.

2.10. Editing the deployment properties of a deployed model version from a model registry
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You can edit model version deployment properties from a model registry for models that were deployed from the registry. For example, you can change the deployment name, model framework, and source model location details.

2.10.1. Editing the deployment properties of a model deployed by using the multi-model serving platform
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You can edit the deployment properties of a deployed model version from a model registry. For example, you can change the deployment name, model framework, and source model location details.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered and deployed model version.
You have access to the model registry that contains the model version deployment that you want to edit.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that contains the model deployment that you want to edit.
In the Model name column, click the name of the model that contains the deployment that you want to edit.
The details page for the model opens.
Click the name of the model version with the deployment that you want to edit.
Click Deployments
Click the action menu (⋮) beside the model deployment that you want to edit.
Click Edit.
In the Edit model dialog, edit the model deployment properties:
1. In the Model deployment name field, enter a new, unique name for your model deployment.
2. From the Model framework list, select a different 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 configured your model server.
3. Edit the connection by specifying an existing connection, or by creating a new connection.
4. Click Redeploy.

Verification

The model redeploys and is displayed with updated details on the Deployments tab for the model version.

2.10.2. Editing the deployment properties of a model deployed by using the single-model serving platform
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You can edit the deployment properties of a deployed model version from a model registry. For example, you can change the deployment name, model framework, number of model server replicas, model server size, and source model location details.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered and deployed model version.
You have access to the model registry that contains the model version deployment that you want to edit.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that contains the model deployment that you want to edit.
In the Model name column, click the name of the model that contains the deployment that you want to edit.
The details page for the model opens.
Click the name of the model version with the deployment that you want to edit.
Click Deployments
Click the action menu (⋮) beside the model deployment that you want to edit.
Click Edit.
In the Edit model dialog, edit the model deployment properties:
1. In the Model deployment name field, enter a new, unique name for your model deployment.
2. From the Model framework list, select a different 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.
3. In the Number of model server replicas to deploy field, specify a value.
4. From the Model server size list, select a value.
5. 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.
6. 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:
  1. 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.
  2. To add an additional service account, click Add a service account and enter another service account name.
7. Edit the connection by specifying an existing connection, or by creating a new connection.
8. Customize the runtime parameters in the Configuration parameters section:
  1. Modify the values in Additional serving runtime arguments to define how the deployed model behaves.
  2. Modify the values in Additional environment variables to define variables in the model’s environment.
    The Configuration parameters section shows predefined serving runtime parameters, if any are available.
    Note
    Do not modify the port or model serving runtime arguments, because they require specific values to be set. Overwriting these parameters can cause the deployment to fail.
9. Click Redeploy.

Verification

The model redeploys and is displayed with updated details on the Deployments tab for the model version.

2.11. Deleting a deployed model version from a model registry
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You can delete the deployments of model versions from a model registry.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered model with a deployed model version.
You have access to the model registry that contains the model version deployment that you want to delete.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that contains the deployment that you want to delete.
Click the name of a model to view more details.
The details page for the model opens.
Click the name of the model version with the deployment that you want to delete.
The details page for the model version opens.
Click Deployments.
To delete a deployment, click the action menu (⋮) beside the deployment, and then click Delete.
The Delete deployed model? dialog opens.
Enter the name of the model deployment in the text field to confirm that you intend to delete it.
Click Delete deployed model.

Verification

The model deployment is no longer displayed on the Deployments tab for the model version.

2.12. Archiving a model
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You can archive a model that you no longer require. The model and all of its versions will be archived and unavailable for use unless it is restored.

Important

Models with deployed versions cannot be archived. To archive a model, you must first delete all deployments of its registered versions from the Models → Model deployments page.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered model.
You have access to the model registry that contains the model that you want to archive.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that you want to archive a model in.
Click the action menu (⋮) beside the model that you want to archive.
Click Archive model.
In the Archive model? dialog that is displayed, enter the name of the model in the text field to confirm that you intend to archive it.
Click Archive.

Verification

The model is no longer visible on the Model registry page.
The model is displayed on the archived models page for the model registry.

2.13. Archiving a model version
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You can archive a model version that you no longer require. The model version will be archived and unavailable for use unless it is restored.

Important

Deployed model versions cannot be archived. To archive a model version, you must first delete all deployments of the version from the Models → Model deployments page.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least 1 registered model.
You have access to the model registry that contains the model version that you want to archive.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that you want to archive a model in.
In the Model name column, click the name of the model that contains the version that you want to archive.
The details page for the model version opens.
Click the action menu (⋮) beside the version that you want to archive.
Click Archive model version.
In the Archive version? dialog that opens, enter the name of the model version in the text field to confirm that you intend to archive it.
Click Archive.

Verification

The model version is no longer visible on the details page for the model.
The model version is displayed on the archived versions page for the model.

2.14. Restoring a model
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You can restore an archived model. The model and all of its versions will be restored and returned to the registered models list.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least one archived model.
You have access to the model registry that contains the model that you want to restore.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that you want to restore a model in.
Click the action menu (⋮) beside the Register model button, and then click View archived models.
The archived models page for the model registry opens.
Click the action menu (⋮) beside the model that you want to restore.
Click Restore model.
In the Restore model? dialog that is displayed, click Restore.

Verification

The model is displayed on the Model registry page.
The model is no longer displayed on the archived models page for the model registry.

2.15. Restoring a model version
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You can restore an archived model version. The model version will be restored and returned to the versions list for the model.

Prerequisites

You have logged in to Red Hat OpenShift AI.
An available model registry exists in your deployment, and contains at least one archived model version.
You have access to the model registry that contains the model version that you want to restore.

Procedure

From the OpenShift AI dashboard, click Models → Model registry.
From the Model registry drop-down list, select the model registry that you want to restore a model version in.
In the Model name column, click the name of the model that contains the version that you want to restore.
The details page for the model version opens.
Click the action menu (⋮) beside the Register new version button, and then click View archived versions.
The archived versions page for the model opens.
Click the action menu (⋮) beside the version that you want to restore.
Click Restore version.
In the Restore version? dialog that opens, click Restore.
The details page for the version opens.

Verification

The model version is displayed on the details page for the model.
The model is no longer displayed on the archived versions page for the model.

Legal Notice
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Working with model registries