LocalAI/website/static/install/kubernetes.yaml

# LocalAI on Kubernetes, CPU only.
#
#   kubectl apply -f https://localai.io/install/kubernetes.yaml
#   kubectl -n local-ai rollout status deploy/local-ai
#   kubectl -n local-ai port-forward svc/local-ai 8080:8080
#   open http://localhost:8080
#
# Everything lands in its own `local-ai` namespace, so removing it again is
# `kubectl delete namespace local-ai` (which also deletes the volumes).
#
# Two volumes, because LocalAI downloads both parts on demand and you do not
# want either of them fetched again on every restart:
#   /models    the model weights you install from the gallery
#   /backends  the engine images pulled the first time a model asks for one
#
# For GPUs, add the vendor device plugin's resource to the container's
# `resources.limits` (for example nvidia.com/gpu: 1) and switch the image to a
# GPU tag. See https://localai.io/docs/getting-started/kubernetes/ for the
# Helm chart and the GPU variants.
---
apiVersion: v1
kind: Namespace
metadata:
  name: local-ai
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: local-ai-models
  namespace: local-ai
  labels:
    app.kubernetes.io/name: local-ai
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 20Gi
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: local-ai-backends
  namespace: local-ai
  labels:
    app.kubernetes.io/name: local-ai
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 20Gi
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: local-ai
  namespace: local-ai
  labels:
    app.kubernetes.io/name: local-ai
spec:
  replicas: 1
  # The volumes are ReadWriteOnce, so the old pod has to let go before the new
  # one can start.
  strategy:
    type: Recreate
  selector:
    matchLabels:
      app.kubernetes.io/name: local-ai
  template:
    metadata:
      labels:
        app.kubernetes.io/name: local-ai
    spec:
      securityContext:
        # So the mounted volumes are writable whatever the storage class does
        # with ownership.
        fsGroup: 1000
      containers:
        - name: local-ai
          image: localai/localai:latest
          imagePullPolicy: IfNotPresent
          ports:
            - name: http
              containerPort: 8080
              protocol: TCP
          env:
            - name: LOCALAI_MODELS_PATH
              value: /models
            - name: LOCALAI_BACKENDS_PATH
              value: /backends
            - name: LOCALAI_ADDRESS
              value: ":8080"
            # Uncomment to require an API key on every request.
            # - name: LOCALAI_API_KEY
            #   valueFrom:
            #     secretKeyRef:
            #       name: local-ai
            #       key: api-key
          volumeMounts:
            - name: models
              mountPath: /models
            - name: backends
              mountPath: /backends
          resources:
            requests:
              cpu: "1"
              memory: 4Gi
            limits:
              # No CPU limit on purpose: inference is CPU bound and a limit
              # only buys you throttling.
              memory: 12Gi
          # First boot writes out its configuration before the API answers, so
          # the startup probe carries the slow case and the others stay tight.
          startupProbe:
            httpGet:
              path: /readyz
              port: http
            periodSeconds: 10
            failureThreshold: 60
          readinessProbe:
            httpGet:
              path: /readyz
              port: http
            periodSeconds: 10
            timeoutSeconds: 5
            failureThreshold: 3
          livenessProbe:
            httpGet:
              path: /healthz
              port: http
            periodSeconds: 30
            timeoutSeconds: 5
            failureThreshold: 5
      volumes:
        - name: models
          persistentVolumeClaim:
            claimName: local-ai-models
        - name: backends
          persistentVolumeClaim:
            claimName: local-ai-backends
---
apiVersion: v1
kind: Service
metadata:
  name: local-ai
  namespace: local-ai
  labels:
    app.kubernetes.io/name: local-ai
spec:
  # ClusterIP by default, so this applies cleanly on any cluster. Reach it with
  # `kubectl -n local-ai port-forward svc/local-ai 8080:8080`, or change the
  # type to LoadBalancer where your cluster can provision one.
  type: ClusterIP
  selector:
    app.kubernetes.io/name: local-ai
  ports:
    - name: http
      protocol: TCP
      port: 8080
      targetPort: http