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homelab/immich/k8s/machine-learning.yaml
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feat(reloader): enable deployment and reload runtime-config consumers
2026-10-06 14:54:45 +00:00

97 lines
2.4 KiB
YAML

apiVersion: v1
kind: Service
metadata:
name: immich-machine-learning
namespace: immich
spec:
selector:
app: immich-machine-learning
ports:
- name: http
port: 3003
targetPort: 3003
---
apiVersion: apps/v1
kind: Deployment
metadata:
annotations:
reloader.stakater.com/auto: "true"
name: immich-machine-learning-deployment
namespace: immich
labels:
app: immich-machine-learning
spec:
replicas: 1
selector:
matchLabels:
app: immich-machine-learning
strategy:
type: Recreate
template:
metadata:
labels:
app: immich-machine-learning
spec:
containers:
- name: immich-machine-learning
image: ghcr.io/immich-app/immich-machine-learning:v3
envFrom:
- configMapRef:
name: immich-config
- secretRef:
name: immich-secrets
ports:
- name: http
containerPort: 3003
volumeMounts:
- name: model-cache
mountPath: /cache
# The first request pulls a model over the internet, so a cold start
# is slower than a container start.
startupProbe:
httpGet:
path: /ping
port: http
failureThreshold: 60
periodSeconds: 5
timeoutSeconds: 5
readinessProbe:
httpGet:
path: /ping
port: http
periodSeconds: 10
timeoutSeconds: 5
livenessProbe:
httpGet:
path: /ping
port: http
initialDelaySeconds: 30
periodSeconds: 30
timeoutSeconds: 5
# Same reasoning as the server: the request covers the idle cost
# only, because the node has no spare cores to schedule against.
# Recognition is the burst - a busy import wants both cores.
resources:
requests:
cpu: "100m"
memory: "1Gi"
limits:
cpu: "2000m"
memory: "3Gi"
volumes:
- name: model-cache
persistentVolumeClaim:
claimName: immich-model-cache-pvc
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: immich-model-cache-pvc
namespace: immich
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 2Gi