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Kubernetes

ecosystem.Ai can be installed on Kubernetes. This can be tested locally using Minikube. Here we give example deployment configurations for the server, workbench, notebooks, runtime and grafana components.

Environment Variables

A number of environment variables can be used when starting up the ecosystem.Ai Deployments. These can be set in a ConfigMap. In addition, you should create a secret containing your license key.

āœļø
Environment Variable Config Mapā–·
apiVersion: v1
kind: ConfigMap
metadata:
  name: ecosystem-env
  namespace: ecosystem
data:
  # Server
  ECOSYSTEM_SERVER_PORT: "3001"
  ECOSYSTEM_SERVER_IP: "http://server.ecosystem.svc.cluster.local"
  ECOSYSTEM_PROP_FILE: "/config/ecosystem.properties"
  CLI_SETTINGS: "-Dserver.port=3001"
  RESET_USER: "true"
  NO_WORKBENCH: "true"
 
  # Runtime
  MONITORING_DELAY: "240"
  ECOSYSTEM_RUNTIME_PORT: "8091"
 
  # Workbench
  WORKBENCH_IP: "http://127.0.0.1"
  WORKBENCH_PORT: "3001"
 
  # Grafana
  GF_SECURITY_ALLOW_EMBEDDING: "true"
  GF_INSTALL_PLUGINS: "marcusolsson-json-datasource,volkovlabs-echarts-panel"

Persistent Volume Claims

Ideally you should create a ReadWriteMany PVC to mount to the various ecosystem.Ai Deployments as it makes management easier. This is illustrated here. If your Kubernetes instance does not support ReadWriteMany PVCs then you will need to create multiple PVCs.

āœļø
Persistent Volume Claimā–·
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: ecosystem-data-pvc
  namespace: ecosystem
spec:
  accessModes:
    - ReadWriteMany
  resources:
    requests:
      storage: /desired storage capacity/Gi

Server Deployment

The server Deployment and services are below. Here, and in the subsequent components:

  • Images are pulled from Docker Hub. Generally, the images should be stored in a local repository and that repository should be referenced in the Deployment.
  • LoadBalancer services are created. This exposes the services externally in Minikube. The Ingress approach supported by your Kuberenetes instance should be used to expose the services.
āœļø
Server Deploymentā–·
###############################################################################
# 1) ECOSYSTEM-SERVER (SINGLE INSTANCE)
###############################################################################
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ecosystem-server
  namespace: ecosystem
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ecosystem-server
  template:
    metadata:
      labels:
        app: ecosystem-server
    spec:
      containers:
      - name: ecosystem-server
        image: docker.io/ecosystemai/ecosystem-server-mongo8:arm64
        imagePullPolicy: Always
        env:
          - name: MASTER_KEY
            valueFrom:
              secretKeyRef:
                name: master-key
                key: master-key
          - name: PORT
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: ECOSYSTEM_SERVER_PORT
          - name: IP
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: ECOSYSTEM_SERVER_IP
          - name: ECOSYSTEM_PROP_FILE
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: ECOSYSTEM_PROP_FILE
          - name: CLI_SETTINGS
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: CLI_SETTINGS
          - name: RESET_USER
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: RESET_USER
          - name: NO_WORKBENCH
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: NO_WORKBENCH
        ports:
          - containerPort: 3001
            name: http
          - containerPort: 54321
            name: htwoo
          - containerPort: 54445
            name: mongo
        volumeMounts:
          - name: ecosystem-data
            subPath: data
            mountPath: /data
          - name: ecosystem-data
            subPath: serverconfig
            mountPath: /config
      volumes:
      - name: ecosystem-data
        persistentVolumeClaim:
          claimName: ecosystem-data-pvc
 
---
###############################################################################
# 2) SERVICES
###############################################################################
 
apiVersion: v1
kind: Service
metadata:
  name: server
  namespace: ecosystem
spec:
  type: LoadBalancer
  selector:
    app: ecosystem-server
  ports:
    - name: http
      port: 3001
      targetPort: 3001
 
---
 
apiVersion: v1
kind: Service
metadata:
  name: mongo
  namespace: ecosystem
spec:
  type: LoadBalancer
  selector:
    app: ecosystem-server
  ports:
    - name: mongo
      port: 54445
      targetPort: 54445
 
---
 
apiVersion: v1
kind: Service
metadata:
  name: htwoo
  namespace: ecosystem
spec:
  type: LoadBalancer
  selector:
    app: ecosystem-server
  ports:
    - name: htwoo
      port: 54321
      targetPort: 54321
 
---

Notebooks and Grafana Deployments

āœļø
Notebooks and Grafana Deploymentsā–·
###############################################################################
# 1) ECOSYSTEM-NOTEBOOKS
###############################################################################
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ecosystem-notebooks
  namespace: ecosystem
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ecosystem-notebooks
  template:
    metadata:
      labels:
        app: ecosystem-notebooks
    spec:
      containers:
      - name: ecosystem-notebooks
        image: docker.io/ecosystemai/ecosystem-notebooks:arm64
        imagePullPolicy: Always
        ports:
          - containerPort: 8000
            name: notebooks
          - containerPort: 8010
            name: pythonserver
        volumeMounts:
          - name: ecosystem-data
            mountPath: "/app/Shared Projects"
            subPath: "notebooks-users/notebooks"
          - name: ecosystem-data
            mountPath: "/home"
            subPath: "notebooks-users"
          - name: ecosystem-data
            subPath: data
            mountPath: "/data"
      volumes:
        - name: ecosystem-data
          persistentVolumeClaim:
            claimName: ecosystem-data-pvc
      restartPolicy: Always
 
---
 
###############################################################################
# 2) ECOSYSTEM-GRAFANA
###############################################################################
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ecosystem-grafana
  namespace: ecosystem
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ecosystem-grafana
  template:
    metadata:
      labels:
        app: ecosystem-grafana
    spec:
      containers:
      - name: ecosystem-grafana
        image: docker.io/ecosystemai/ecosystem-grafana:nojwt
        imagePullPolicy: IfNotPresent
        env:
          - name: GF_SECURITY_ALLOW_EMBEDDING
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: GF_SECURITY_ALLOW_EMBEDDING
        ports:
          - containerPort: 3000
            name: grafana
        volumeMounts:
          - name: ecosystem-data
            mountPath: /var/lib/grafana
      volumes:
        - name: ecosystem-data
          persistentVolumeClaim:
            claimName: ecosystem-data-pvc
      restartPolicy: Always
 
---
 
###############################################################################
# 3) SERVICES
###############################################################################
 
apiVersion: v1
kind: Service
metadata:
  name: notebooks
  namespace: ecosystem
spec:
  type: LoadBalancer
  selector:
    app: ecosystem-notebooks
  ports:
    - name: notebooks
      port: 8000
      targetPort: 8000
 
---
 
apiVersion: v1
kind: Service
metadata:
  name: pythonserver
  namespace: ecosystem
spec:
  type: LoadBalancer
  selector:
    app: ecosystem-notebooks
  ports:
    - name: pythonserver
      port: 8010
      targetPort: 8010
 
---
 
apiVersion: v1
kind: Service
metadata:
  name: grafana
  namespace: ecosystem
spec:
  type: LoadBalancer
  selector:
    app: ecosystem-grafana
  ports:
    - name: grafana
      port: 3000
      targetPort: 3000
 
---

Workbench and Runtime Deployments

The workbench Deployment utilises a ConfigMap which sets the port on which the workbench starts up. In this case we start up the workbench on port 8008.

A single runtime Deployment is created. Separate Deployments should be created for each use case that needs to be pushed to a runtime. The replica count is not specified in the runtime Deployment, this assumes that a HorizontalPodAutoscaler will be created for the runtime. Alternatively the replica count can be set at a volume that can handle the anticipated load.

āœļø
Workbench and Runtime Deploymentsā–·
###############################################################################
# 1) ECOSYSTEM-WORKBENCH (SINGLE INSTANCE)
###############################################################################
 
apiVersion: v1
kind: ConfigMap
metadata:
  name: nginx-config
  namespace: ecosystem
data:  
  default.conf: |
    server {
        listen 8008 default_server;
        listen [::]:8008 default_server;
 
        root /usr/share/nginx/html;
 
        index index.html;
 
        server_name _;
 
        location / {
            try_files $uri$args $uri$args/ /index.html;
        }
 
        location ~* .(js|css|ttf|ttc|otf|eot|woff|woff2)$ {
                add_header access-control-allow-origin "*";
                expires max;
        }
    }
 
---
 
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ecosystem-workbench
  namespace: ecosystem
spec:
  replicas: 1
  selector:
    matchLabels:
      app: ecosystem-workbench
  template:
    metadata:
      labels:
        app: ecosystem-workbench
    spec:
      containers:
      - name: ecosystem-workbench
        image: docker.io/ecosystemai/ecosystem-workbench:arm64
        volumeMounts:
        - name: nginx-config
          mountPath: "/etc/nginx/conf.d"
        securityContext:
          runAsGroup: 0
        imagePullPolicy: IfNotPresent
        env:
          - name: IP
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: WORKBENCH_IP
          - name: PORT
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: WORKBENCH_PORT
        ports:
          - containerPort: 8008
            name: http
      volumes:
      - name: nginx-config
        configMap:
          name: nginx-config
      restartPolicy: Always
 
---
 
###############################################################################
# 2) ecosystem-runtime
###############################################################################
 
apiVersion: apps/v1
kind: Deployment
metadata:
  name: ecosystem-runtime1
  namespace: ecosystem
spec:
  #replicas: 1
  selector:
    matchLabels:
      app: ecosystem-runtime1
  template:
    metadata:
      labels:
        app: ecosystem-runtime1
    spec:
      containers:
      - name: ecosystem-runtime1
        image: docker.io/ecosystemai/ecosystem-runtime-solo:arm64
        imagePullPolicy: IfNotPresent
        volumeMounts:
          - name: ecosystem-data
            subPath: data
            mountPath: /data
        env:
          - name: MASTER_KEY
            valueFrom:
              secretKeyRef:
                name: master-key
                key: master-key
          - name: MONITORING_DELAY
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: MONITORING_DELAY
          - name: PORT
            valueFrom:
              configMapKeyRef:
                name: ecosystem-env
                key: ECOSYSTEM_RUNTIME_PORT
        ports:
          - containerPort: 8091
            name: http
        resources:
          requests:
            cpu: "1"
            memory: "2Gi"
          limits:
            cpu: "1"
            memory: "2Gi"
      volumes:
      - name: ecosystem-data
        persistentVolumeClaim:
          claimName: ecosystem-data-pvc
      restartPolicy: Always
 
---
 
###############################################################################
# 3) SERVICES
###############################################################################
 
apiVersion: v1
kind: Service
metadata:
  name: workbench
  namespace: ecosystem
spec:
  type: LoadBalancer
  selector:
    app: ecosystem-workbench
  ports:
    - name: http
      port: 8008
      targetPort: 8008
 
---
 
apiVersion: v1
kind: Service
metadata:
  name: runtime1
  namespace: ecosystem
spec:
  type: LoadBalancer
  selector:
    app: ecosystem-runtime1
  ports:
    - name: runtime1
      port: 8091
      targetPort: 8091
 
---

Conclusion

Thatā€™s it! You have now configured your ecosystem.Ai instance on Kubernetes.

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