Alauda AI

Install Workbench

Starting with v0.2.0, Workbench is delivered as an OLM Helm operator. Install the operator from OperatorHub, then create a Workbench custom resource that deploys and manages workspace-controller and the built-in JupyterLab and code-server WorkspaceKind resources.

TOC

PrerequisitesUpload the operatorInstall the Workbench operatorProcedureCreate a Workbench instanceVerificationFeature Gate ConfigurationProcedureAccessing the Feature Gate PageEnabling Workbench Features

Prerequisites

  • Alauda AI is installed on the target cluster.
  • Operator Lifecycle Manager (OLM) is available through the platform.
  • cert-manager is installed for the workspace-controller webhook certificate.
  • The separate aml-skipper component is installed for Workbench routing.
  • Optional: ASM (Istio) is required only for the Elyra Kubeflow Pipelines run-URL redirect. Istio integration is disabled by default, so Workbench can install without Istio CRDs.

Upload the operator

Download the Alauda AI Workbench Operator bundle from the Customer Portal / Marketplace. If it is not already available in OperatorHub, publish the downloaded bundle to the platform repository:

violet push \
  --platform-address=<platform-access-address> \
  --platform-username=<platform-admin> \
  --platform-password=<platform-admin-password> \
  <workbench-operator-bundle-package>

Install the Workbench operator

Procedure

  1. Go to the Administrator page.
  2. Click Marketplace > OperatorHub.
  3. From the Cluster dropdown, select the business cluster where Workbench will run.
  4. Find Alauda AI Workbench Operator and click Install.
  5. Keep the stable channel and verify that the version is v0.2.0.
  6. Leave the installation location at its default and choose an upgrade strategy.
  7. Click Install and wait for the ClusterServiceVersion (CSV) status to become Succeeded.

Create a Workbench instance

The operator does not deploy workspace-controller until you create a Workbench custom resource.

  1. Open the installed Alauda AI Workbench Operator.
  2. Select the All instances tab and click Create.
  3. Keep Enable Istio integration disabled unless the cluster runs ASM and you need Elyra-generated KFP run URLs to redirect through the Kubeflow Istio ingress gateway.
  4. Click Create.

You can create the same resource with kubectl:

apiVersion: workbench.alauda.io/v1alpha1
kind: Workbench
metadata:
  name: workbench
spec:
  global:
    istio:
      enabled: false

Save the manifest as workbench.yaml, then apply it:

kubectl apply -f workbench.yaml

The operator reads the cluster name, ingress class, and image registry from the kube-public/global-info ConfigMap. You normally do not need to set those values in the Workbench resource.

Verification

# The Workbench custom resource exists.
kubectl get workbench -A

# The workspace-controller operand is available.
kubectl -n kubeflow get deploy workspace-controller-controller-manager

# Built-in IDE templates are registered.
kubectl get workspacekind

After the deployment is ready, confirm that Workbench appears in Alauda AI and that you can open the Workbench list.

Feature Gate Configuration

Procedure

Accessing the Feature Gate Page

Access the feature gates at {platform-access-address}/console-platform/feature-gate. For example, if your platform access address is https://demo.example.com/console-portal/, the feature gate page would be https://demo.example.com/console-platform/feature-gate.

Enabling Workbench Features

  1. Find the gate list with the display name of your target which is ai-workbench.
  2. Turn On the switch button of it.