Chapter 4. Installing the distributed workloads components

Procedure

1. In the OpenShift console, click Operators Installed Operators.
2. Search for the Red Hat OpenShift AI Operator, and then click the Operator name to open the Operator details page.
3. Click the Data Science Cluster tab.
4. Click the default instance name (for example, default-dsc) to open the instance details page.
5. Click the YAML tab to show the instance specifications.

6. Enable the required distributed workloads components. In the spec.components section, set the managementState field correctly for the required components:

   • If you want to use the CodeFlare framework to tune models, enable the codeflare, kueue, and ray components.
   • If you want to use the Kubeflow Training Operator to tune models, enable the kueue and trainingoperator components.
   • The list of required components depends on whether the distributed workload is run from a pipeline or notebook or both, as shown in the following table.

   Table 4.1. Components required for distributed workloads

   Component	Pipelines only	Notebooks only	Pipelines and notebooks
   codeflare	Managed	Managed	Managed
   dashboard	Managed	Managed	Managed
   datasciencepipelines	Managed	Removed	Managed
   kueue	Managed	Managed	Managed
   ray	Managed	Managed	Managed
   trainingoperator	Managed	Managed	Managed
   workbenches	Removed	Managed	Managed

7. Click Save. After a short time, the components with a Managed state are ready.

1. In the OpenShift console, from the Project list, select redhat-ods-applications.
2. Click Workloads Deployments.

3. Search for the codeflare-operator-manager, kubeflow-training-operator, kuberay-operator, and kueue-controller-manager deployments. In each case, check the status as follows:

   1. Click the deployment name to open the deployment details page.
   2. Click the Pods tab.

   3. Check the pod status.

      When the status of the codeflare-operator-manager-<pod-id>, kubeflow-training-operator-<pod-id>, kuberay-operator-<pod-id>, and kueue-controller-manager-<pod-id> pods is Running, the pods are ready to use.

   4. To see more information about each pod, click the pod name to open the pod details page, and then click the Logs tab.