Update mcad kuberay example

doc/usage/examples/kuberay/config/aw-raycluster-1.yaml

@@ -0,0 +1,153 @@
+apiVersion: mcad.ibm.com/v1beta1
+kind: AppWrapper
+metadata:
+  name: raycluster-complete-1
+  namespace: default
+spec:
+  resources:
+    GenericItems:
+    - replicas: 1
+      custompodresources: # Optional section that specifies resource requirements
+                          # for non-standard k8s resources, follows same format as
+                          # that of standard k8s resources.
+      - replicas: 2 # because AppWrappers are generic they must define the resultant pods that will be needed
+                    # to fulfill a request as the request values cannot be reliably extracted from the
+                    # generictemplate below
+        requests:
+          cpu: 8
+          memory: 512Mi
+        limits:
+          cpu: 10
+          memory: 1G
+      generictemplate:
+        # The resource requests and limits in this config are too small for production!
+        # For examples with more realistic resource configuration, see
+        # ray-cluster.complete.large.yaml and
+        # ray-cluster.autoscaler.large.yaml.
+        apiVersion: ray.io/v1alpha1
+        kind: RayCluster
+        metadata:
+          labels:
+            controller-tools.k8s.io: "1.0"
+            # A unique identifier for the head node and workers of this cluster.
+          name: raycluster-complete-1
+        spec:
+          rayVersion: '2.5.0'
+          # Ray head pod configuration
+          headGroupSpec:
+            # Kubernetes Service Type. This is an optional field, and the default value is ClusterIP.
+            # Refer to https://kubernetes.io/docs/concepts/services-networking/service/#publishing-services-service-types.
+            serviceType: ClusterIP
+            # The `rayStartParams` are used to configure the `ray start` command.
+            # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
+            # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
+            rayStartParams:
+              dashboard-host: '0.0.0.0'
+            # pod template
+            template:
+              metadata:
+                # Custom labels. NOTE: To avoid conflicts with KubeRay operator, do not define custom labels start with `raycluster`.
+                # Refer to https://kubernetes.io/docs/concepts/overview/working-with-objects/labels/
+                labels: {}
+              spec:
+                containers:
+                - name: ray-head
+                  image: rayproject/ray:2.5.0
+                  ports:
+                  - containerPort: 6379
+                    name: gcs
+                  - containerPort: 8265
+                    name: dashboard
+                  - containerPort: 10001
+                    name: client
+                  lifecycle:
+                    preStop:
+                      exec:
+                        command: ["/bin/sh","-c","ray stop"]
+                  volumeMounts:
+                    - mountPath: /tmp/ray
+                      name: ray-logs
+                  # The resource requests and limits in this config are too small for production!
+                  # For an example with more realistic resource configuration, see
+                  # ray-cluster.autoscaler.large.yaml.
+                  # It is better to use a few large Ray pod than many small ones.
+                  # For production, it is ideal to size each Ray pod to take up the
+                  # entire Kubernetes node on which it is scheduled.
+                  resources:
+                    limits:
+                      cpu: "1"
+                      memory: "2G"
+                    requests:
+                      # For production use-cases, we recommend specifying integer CPU reqests and limits.
+                      # We also recommend setting requests equal to limits for both CPU and memory.
+                      # For this example, we use a 500m CPU request to accomodate resource-constrained local
+                      # Kubernetes testing environments such as KinD and minikube.
+                      cpu: "500m"
+                      memory: "2G"
+                volumes:
+                  - name: ray-logs
+                    emptyDir: {}
+          workerGroupSpecs:
+          # the pod replicas in this group typed worker
+          - replicas: 1
+            minReplicas: 1
+            maxReplicas: 10
+            # logical group name, for this called small-group, also can be functional
+            groupName: small-group
+            # If worker pods need to be added, we can increment the replicas.
+            # If worker pods need to be removed, we decrement the replicas, and populate the workersToDelete list.
+            # The operator will remove pods from the list until the desired number of replicas is satisfied.
+            # If the difference between the current replica count and the desired replicas is greater than the
+            # number of entries in workersToDelete, random worker pods will be deleted.
+            #scaleStrategy:
+            #  workersToDelete:
+            #  - raycluster-complete-worker-small-group-bdtwh
+            #  - raycluster-complete-worker-small-group-hv457
+            #  - raycluster-complete-worker-small-group-k8tj7
+            # The `rayStartParams` are used to configure the `ray start` command.
+            # See https://github.com/ray-project/kuberay/blob/master/docs/guidance/rayStartParams.md for the default settings of `rayStartParams` in KubeRay.
+            # See https://docs.ray.io/en/latest/cluster/cli.html#ray-start for all available options in `rayStartParams`.
+            rayStartParams: {}
+            #pod template
+            template:
+              spec:
+                containers:
+                - name: ray-worker
+                  image: rayproject/ray:2.5.0
+                  lifecycle:
+                    preStop:
+                      exec:
+                        command: ["/bin/sh","-c","ray stop"]
+                  # use volumeMounts.Optional.
+                  # Refer to https://kubernetes.io/docs/concepts/storage/volumes/
+                  volumeMounts:
+                    - mountPath: /tmp/ray
+                      name: ray-logs
+                  # The resource requests and limits in this config are too small for production!
+                  # For an example with more realistic resource configuration, see
+                  # ray-cluster.autoscaler.large.yaml.
+                  # It is better to use a few large Ray pod than many small ones.
+                  # For production, it is ideal to size each Ray pod to take up the
+                  # entire Kubernetes node on which it is scheduled.
+                  resources:
+                    limits:
+                      cpu: "1"
+                      memory: "1G"
+                    # For production use-cases, we recommend specifying integer CPU reqests and limits.
+                    # We also recommend setting requests equal to limits for both CPU and memory.
+                    # For this example, we use a 500m CPU request to accomodate resource-constrained local
+                    # Kubernetes testing environments such as KinD and minikube.
+                    requests:
+                      # For production use-cases, we recommend specifying integer CPU reqests and limits.
+                      # We also recommend setting requests equal to limits for both CPU and memory.
+                      # For this example, we use a 500m CPU request to accomodate resource-constrained local
+                      # Kubernetes testing environments such as KinD and minikube.
+                      cpu: "500m"
+                      # For production use-cases, we recommend allocating at least 8Gb memory for each Ray container.
+                      memory: "1G"
+                # use volumes
+                # Refer to https://kubernetes.io/docs/concepts/storage/volumes/
+                volumes:
+                  - name: ray-logs
+                    emptyDir: {}
+