--- title: Managing distributed workloads with the Leader Worker Set Operator --- # Managing distributed workloads with the Leader Worker Set Operator {#lws-managing} You can use the Leader Worker Set Operator to manage distributed inference workloads and process large-scale inference requests efficiently. ## Installing the Leader Worker Set Operator {#lws-install-operator_lws-managing} You can install the Leader Worker Set Operator through the OpenShift Container Platform web console to begin managing distributed AI workloads. **Prerequisites** - You have access to the cluster with `cluster-admin` privileges. - You have access to the OpenShift Container Platform web console. - You have installed the cert-manager Operator for Red Hat OpenShift. **Procedure** 1. Log in to the OpenShift Container Platform web console. 2. Verify that the cert-manager Operator for Red Hat OpenShift is installed. 3. Install the Leader Worker Set Operator. 1. Navigate to **Ecosystem** → **Software Catalog**. 2. Enter **Leader Worker Set Operator** into the filter box. 3. Select the **Leader Worker Set Operator** and click **Install**. 4. On the **Install Operator** page: 1. The **Update channel** is set to **stable-v1.0**, which installs the latest stable release of Leader Worker Set Operator 1.0. 2. Under **Installation mode**, select **A specific namespace on the cluster**. 3. Under **Installed Namespace**, select **Operator recommended Namespace: openshift-lws-operator**. 4. Under **Update approval**, select one of the following update strategies: - The **Automatic** strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available. - The **Manual** strategy requires a user with appropriate credentials to approve the Operator update. 5. Click **Install**. 4. Create the custom resource (CR) for the Leader Worker Set Operator: 1. Navigate to **Installed Operators** → **Leader Worker Set Operator**. 2. Under **Provided APIs**, click **Create instance** in the **LeaderWorkerSetOperator** pane. 3. Click **Create**. ## Deploying a leader worker set {#lws-config_lws-managing} You can use the Leader Worker Set Operator to deploy a leader worker set to assist with managing distributed workloads across nodes. **Prerequisites** - You have installed the Leader Worker Set Operator. **Procedure** 1. Create a new project by running the following command: ```terminal $ oc new-project my-namespace ``` 2. Create a file named `leader-worker-set.yaml` ```yaml apiVersion: leaderworkerset.x-k8s.io/v1 kind: LeaderWorkerSet metadata: generation: 1 name: my-lws namespace: my-namespace spec: leaderWorkerTemplate: leaderTemplate: metadata: {} spec: containers: - image: nginxinc/nginx-unprivileged:1.27 name: leader resources: {} restartPolicy: RecreateGroupOnPodRestart size: 3 workerTemplate: metadata: {} spec: containers: - image: nginxinc/nginx-unprivileged:1.27 name: worker ports: - containerPort: 8080 protocol: TCP resources: {} networkConfig: subdomainPolicy: Shared replicas: 2 rolloutStrategy: rollingUpdateConfiguration: maxSurge: 1 maxUnavailable: 1 type: RollingUpdate startupPolicy: LeaderCreated ``` where: `metadata.name` : Specifies the name of the leader worker set resource. `metadata.namespace` : Specifies the namespace for the leader worker set to run in. `spec.leaderWorkerTemplate.leaderTemplate` : Specifies the pod template for the leader pods. `spec.leaderWorkerTemplate.restartPolicy` : Specifies the restart policy for when pod failures occur. Allowed values are `RecreateGroupOnPodRestart` to restart the whole group or `None` to not restart the group. `spec.leaderWorkerTemplate.size` : Specifies the number of pods to create for each group, including the leader pod. For example, a value of `3` creates 1 leader pod and 2 worker pods. The default value is `1`. `spec.leaderWorkerTemplate.workerTemplate` : Specifies the pod template for the worker pods. `spec.networkConfig.subdomainPolicy` : Specifies the policy to use when creating the headless service. Allowed values are `UniquePerReplica` or `Shared`. The default value is `Shared`. `spec.replicas` : Specifies the number of replicas, or leader-worker groups. The default value is `1`. `spec.rolloutStrategy.rollingUpdateConfiguration.maxSurge` : Specifies the maximum number of replicas that can be scheduled above the `replicas` value during rolling updates. The value can be specified as an integer or a percentage. For more information on all available fields to configure, see [LeaderWorkerSet API](https://lws.sigs.k8s.io/docs/reference/leaderworkerset.v1/) upstream documentation. 1. Apply the leader worker set configuration by running the following command: ```terminal $ oc apply -f leader-worker-set.yaml ``` **Verification** 1. Verify that pods were created by running the following command: ```terminal $ oc get pods -n my-namespace ``` ```terminal {title="Example output"} NAME READY STATUS RESTARTS AGE my-lws-0 1/1 Running 0 4s my-lws-0-1 1/1 Running 0 3s my-lws-0-2 1/1 Running 0 3s my-lws-1 1/1 Running 0 7s my-lws-1-1 1/1 Running 0 6s my-lws-1-2 1/1 Running 0 6s ``` - `my-lws-0` is the leader pod for the first group. - `my-lws-1` is the leader pod for the second group. 2. Review the stateful sets by running the following command: ```terminal $ oc get statefulsets ``` ```terminal {title="Example output"} NAME READY AGE my-lws 4/4 111s my-lws-0 2/2 57s my-lws-1 2/2 60s ``` - `my-lws` is the leader stateful set for all leader-worker groups. - `my-lws-0` is the worker stateful set for the first group. - `my-lws-1` is the worker stateful set for the second group. **Additional resources** {._additional-resources} - [LeaderWorkerSet API (Kubernetes)](https://lws.sigs.k8s.io/docs/reference/leaderworkerset.v1/)