#Upgrade Alauda AI

#Install Alauda AI Cluster Components

Please visit Alauda AI Cluster for:

1. Downloading operator bundle packages for Alauda AI Cluster and KServeless.
2. Uploading operator bundle packages to the destination cluster.
3. To upgrade, follow the process described below.

#Pre-Upgrade Operations

#Annotating Stopped Inference Services

Starting from version 2.0, the platform adopts the community-native stop capability provided by KServe. To ensure a smooth upgrade, all inference services that are currently in a stopped state must be explicitly annotated before upgrading.

1. List all inference services that are currently stopped:

   kubectl get inferenceservices --all-namespaces

2. For each stopped inference service, add the following annotation:

   kubectl annotate inferenceservice <name> -n <namespace> serving.kserve.io/stop='true'

   Alternatively, you can edit the resource directly and add the annotation under metadata.annotations:

   apiVersion: serving.kserve.io/v1beta1
   kind: InferenceService
   metadata:
     name: <name>
     annotations:
       serving.kserve.io/stop: 'true'

3. Repeat this step for all stopped inference services across all namespaces.

#Upgrading

The following procedure describes how to upgrade from Alauda AI 1.5 to 2.0.

#Upgrade Alauda AI Essentials

Upgrade Alauda AI Essentials in the Functional Components tab of the global cluster.

In the Administrator view, navigate to Clusters / Cluster in the left navigation, find the global cluster in the right panel, and click the Upgrade button on the Functional Components tab page to trigger the upgrade.

After the upgrade is complete, please confirm that the status of Alauda AI Essentials in the list is Running and the version is v2.0.x.

#Upgrading Alauda AI Operators

The procedure for upgrading both operators is nearly identical, with only the target component being different.

#Upgrading Cluster Plugins

The procedure to upgrade cluster plugins involves uploading new version packages and then upgrading them from the Web Console. The following plugins require upgrading:

• Alauda AI Workbench
• Alauda AI MLflow
• Alauda AI Volcano

#Uploading Cluster Plugins

Upload the new cluster plugin packages to the global cluster. For detailed instructions on how to upload cluster plugins, refer to Uploading Cluster Plugins.

Repeat the upload process for each plugin that you have deployed.

#Verifying the New Version

Navigate to Administrator > Marketplace > Upload Packages and switch to the Cluster Plugin tab. Locate each uploaded plugin to verify that the new version is displayed.

#Upgrading from Web Console

1. Navigate to Administrator > Clusters > Clusters.
2. Click on the cluster where the plugins are deployed.
3. Switch to the Functional Components tab in the cluster details page.
4. Locate the cluster plugins that have available upgrades—an upgrade icon will be displayed next to them.
5. Click the Upgrade button for the plugin you want to upgrade.
6. Review the upgrade information in the popup window, confirm the plugin version to be upgraded, and click the Upgrade button to start the upgrade process.

Repeat this process for each plugin that you have deployed.

#Post-Upgrade Operations

#Updating Existing Inference Services

Due to breaking changes in KServe's product mode definition and the InferenceService resource introduced in version 2.0, all inference services that existed before the upgrade must be manually updated.

For each existing inference service, perform the following steps:

1. Navigate to the inference service details page.

2. Click Update Inference Service.

3. In the update page, click the YAML toggle button in the upper-right corner to switch to the YAML view.

4. Locate the spec.predictor.model.name field.

5. Delete the name field and its value entirely.

   For example, if the YAML contains:

   spec:
     predictor:
       model:
         name: kserve-container
         modelFormat:
           name: sklearn

   After deletion, it should look like:

   spec:
     predictor:
       model:
         modelFormat:
           name: sklearn

6. Click Save to apply the changes.

7. Repeat this process for all inference services that existed before the upgrade.

#Verification

kubectl get amlcluster default

NAME      READY   REASON
default   True    Succeeded

kubectl get KnativeServing.components.aml.dev default-knative-serving

NAME                      DEPLOYED   REASON
default-knative-serving   True       UpgradeSuccessful