# This section is only required for deploying Redis on Kubernetes for the purpose of enabling Ray 
# to write GCS metadata to an external Redis for fault tolerance. If you have already deployed Redis 
# on Kubernetes, this section can be removed.
kind: ConfigMap
apiVersion: v1
metadata:
  name: redis-config
  labels:
    app: redis
data:
  redis.conf: |-
    dir /data
    port 6379
    bind 0.0.0.0
    appendonly yes
    protected-mode no
    pidfile /data/redis-6379.pid
---
apiVersion: v1
kind: Service
metadata:
  name: redis
  labels:
    app: redis
spec:
  type: ClusterIP
  ports:
    - name: redis
      port: 6379
  selector:
    app: redis
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: redis
  labels:
    app: redis
spec:
  replicas: 1
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
    spec:
      containers:
        - name: redis
          image: redis:5.0.8
          command:
            - "sh"
            - "-c"
            - "redis-server /usr/local/etc/redis/redis.conf"
          ports:
            - containerPort: 6379
          volumeMounts:
            - name: config
              mountPath: /usr/local/etc/redis/redis.conf
              subPath: redis.conf
      volumes:
        - name: config
          configMap:
            name: redis-config
---
# Ray head node service, allowing worker pods to discover the head node to perform the bidirectional communication.
# More contexts can be found at [the Ports configurations doc](https://docs.ray.io/en/latest/ray-core/configure.html#ports-configurations).
apiVersion: v1
kind: Service
metadata:
  name: service-ray-cluster
  labels:
    app: ray-cluster-head
spec:
  clusterIP: None
  ports:
  - name: client
    protocol: TCP
    port: 10001
    targetPort: 10001
  - name: dashboard
    protocol: TCP
    port: 8265
    targetPort: 8265
  - name: gcs-server
    protocol: TCP
    port: 6380
    targetPort: 6380
  selector:
    app: ray-cluster-head
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: deployment-ray-head
  labels:
    app: ray-cluster-head
spec:
  # Do not change this - Ray currently only supports one head node per cluster.
  replicas: 1
  selector:
    matchLabels:
      component: ray-head
      type: ray
      app: ray-cluster-head
  template:
    metadata:
      labels:
        component: ray-head
        type: ray
        app: ray-cluster-head
    spec:
      # If the head node goes down, the entire cluster (including all worker
      # nodes) will go down as well. If you want Kubernetes to bring up a new
      # head node in this case, set this to "Always," else set it to "Never."
      restartPolicy: Always

      # This volume allocates shared memory for Ray to use for its plasma
      # object store. If you do not provide this, Ray will fall back to
      # /tmp which cause slowdowns if it's not a shared memory volume.
      volumes:
      - name: dshm
        emptyDir:
          medium: Memory
      containers:
        - name: ray-head
          image: rayproject/ray:2.3.0
          imagePullPolicy: Always
          command: [ "/bin/bash", "-c", "--" ]
          # if there is no password for Redis, set --redis-password=''
          args:
            - "ray start --head --port=6380 --num-cpus=$MY_CPU_REQUEST --dashboard-host=0.0.0.0 --object-manager-port=8076 --node-manager-port=8077 --dashboard-agent-grpc-port=8078 --dashboard-agent-listen-port=52365 --redis-password='' --block"
          ports:
            - containerPort: 6380 # GCS server
            - containerPort: 10001 # Used by Ray Client
            - containerPort: 8265 # Used by Ray Dashboard
          # This volume allocates shared memory for Ray to use for its plasma
          # object store. If you do not provide this, Ray will fall back to
          # /tmp which cause slowdowns if it's not a shared memory volume.
          volumeMounts:
            - mountPath: /dev/shm
              name: dshm
          env:
            # RAY_REDIS_ADDRESS lets ray use external Redis for fault tolerance
            - name: RAY_REDIS_ADDRESS
              value: redis:6379 # ip address for the external Redis, which is "redis:6379" in this example
            # This is used in the ray start command so that Ray can spawn the
            # correct number of processes. Omitting this may lead to degraded
            # performance.
            - name: MY_CPU_REQUEST
              valueFrom:
                resourceFieldRef:
                  resource: requests.cpu
          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"
              # The rest state memory usage of the Ray head node is around 1Gb. We do not
              # recommend allocating less than 2Gb memory for the Ray head pod.
              # For production use-cases, we recommend allocating at least 8Gb memory for each Ray container.
              memory: "2G"
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: deployment-ray-worker
  labels:
    app: ray-cluster-worker
spec:
  # Change this to scale the number of worker nodes started in the Ray cluster.
  replicas: 2
  selector:
    matchLabels:
      component: ray-worker
      type: ray
      app: ray-cluster-worker
  template:
    metadata:
      labels:
        component: ray-worker
        type: ray
        app: ray-cluster-worker
    spec:
      restartPolicy: Always
      volumes:
      - name: dshm
        emptyDir:
          medium: Memory
      containers:
      - name: ray-worker
        image: rayproject/ray:2.3.0
        imagePullPolicy: Always
        command: ["/bin/bash", "-c", "--"]
        args:
          - "ray start --num-cpus=$MY_CPU_REQUEST --address=service-ray-cluster:6380 --object-manager-port=8076 --node-manager-port=8077 --dashboard-agent-grpc-port=8078 --dashboard-agent-listen-port=52365 --block"

        # This volume allocates shared memory for Ray to use for its plasma
        # object store. If you do not provide this, Ray will fall back to
        # /tmp which cause slowdowns if it's not a shared memory volume.
        volumeMounts:
          - mountPath: /dev/shm
            name: dshm
        env:
          # This is used in the ray start command so that Ray can spawn the
          # correct number of processes. Omitting this may lead to degraded
          # performance.
          - name: MY_CPU_REQUEST
            valueFrom:
              resourceFieldRef:
                resource: requests.cpu
          # The resource requests and limits in this config are too small for production!
          # It is better to use a few large Ray pods 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:
            cpu: "500m"
            memory: "1G"