Chapter 4. Installing the distributed workloads components To use the distributed workloads feature in OpenShift AI, you must install several components. Prerequisites You have logged in to OpenShift with the cluster-admin role and you can access the data science cluster. You have installed Red Hat OpenShift AI. You have sufficient resources. In addition to the minimum OpenShift AI resources described in Installing and deploying OpenShift AI (for disconnected environments, see Deploying OpenShift AI in a disconnected environment ), you need 1.6 vCPU and 2 GiB memory to deploy the distributed workloads infrastructure. You have removed any previously installed instances of the CodeFlare Operator, as described in the Knowledgebase solution How to migrate from a separately installed CodeFlare Operator in your data science cluster . If you want to use graphics processing units (GPUs), you have enabled GPU support in OpenShift AI. If you use NVIDIA GPUs, see Enabling NVIDIA GPUs . If you use AMD GPUs, see AMD GPU integration . Note Red Hat supports the use of accelerators within the same cluster only. Red Hat does not support remote direct memory access (RDMA) between accelerators, or the use of accelerators across a network, for example, by using technology such as NVIDIA GPUDirect or NVLink. If you want to use self-signed certificates, you have added them to a central Certificate Authority (CA) bundle as described in Working with certificates (for disconnected environments, see Working with certificates ). No additional configuration is necessary to use those certificates with distributed workloads. The centrally configured self-signed certificates are automatically available in the workload pods at the following mount points: Cluster-wide CA bundle: /etc/pki/tls/certs/odh-trusted-ca-bundle.crt /etc/ssl/certs/odh-trusted-ca-bundle.crt Custom CA bundle: /etc/pki/tls/certs/odh-ca-bundle.crt /etc/ssl/certs/odh-ca-bundle.crt Procedure In the OpenShift console, click Operators Installed Operators . Search for the Red Hat OpenShift AI Operator, and then click the Operator name to open the Operator details page. Click the Data Science Cluster tab. Click the default instance name (for example, default-dsc ) to open the instance details page. Click the YAML tab to show the instance specifications. 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 Click Save . After a short time, the components with a Managed state are ready. Verification Check the status of the codeflare-operator-manager , kuberay-operator , and kueue-controller-manager pods, as follows: In the OpenShift console, from the Project list, select redhat-ods-applications . Click Workloads Deployments . Search for the codeflare-operator-manager , kuberay-operator , and kueue-controller-manager deployments. In each case, check the status as follows: Click the deployment name to open the deployment details page. Click the Pods tab. Check the pod status. When the status of the codeflare-operator-manager- <pod-id> , kuberay-operator- <pod-id> , and kueue-controller-manager- <pod-id> pods is Running , the pods are ready to use. To see more information about each pod, click the pod name to open the pod details page, and then click the Logs tab. Step Configure the distributed workloads feature as described in Managing distributed workloads .

Commands:
/etc/pki/tls/certs/odh-trusted-ca-bundle.crt /etc/ssl/certs/odh-trusted-ca-bundle.crt
/etc/pki/tls/certs/odh-ca-bundle.crt /etc/ssl/certs/odh-ca-bundle.crt