Erich Blume
5a9596c7d9
- argocd/manifests/immich-ringtail/: full port of the immich stack
(server, ML, valkey, services, ingress, pvc-ml-cache) from
argocd/manifests/immich/, with ringtail-specific tweaks:
- deployment-ml: runtimeClassName=nvidia, nvidia.com/gpu:1 limit,
-cuda image tag
- deployment-valkey + kustomization: drop the
registry.ops.eblu.me/blumeops/valkey mirror (arm64-only), use
upstream docker.io/valkey/valkey:8.1.6 (multi-arch)
- ingress-tailscale: tls.hosts=[photos-ringtail] for staging
- argocd/apps/immich-ringtail.yaml: new ArgoCD app (manual sync,
ringtail destination)
- argocd/manifests/nvidia-device-plugin/time-slicing-config.yaml:
bump replicas 2 -> 4 so the ringtail GPU can be shared by
frigate + ollama + immich-ml
The immich-db Secret in the immich namespace is created manually
(matching minikube pattern) — see argocd/apps/immich-ringtail.yaml
header for the procedure.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
69 lines
1.9 KiB
YAML
69 lines
1.9 KiB
YAML
---
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apiVersion: apps/v1
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kind: Deployment
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metadata:
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name: immich-machine-learning
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namespace: immich
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spec:
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replicas: 1
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selector:
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matchLabels:
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app: immich
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component: machine-learning
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template:
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metadata:
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labels:
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app: immich
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component: machine-learning
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spec:
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runtimeClassName: nvidia
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securityContext:
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seccompProfile:
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type: RuntimeDefault
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containers:
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- name: machine-learning
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# ringtail uses the -cuda tag (set in kustomization.yaml)
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# to take advantage of the RTX 4080 via the nvidia
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# device plugin. Time-slicing is configured for 4 replicas
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# so frigate + ollama + this pod can share.
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image: ghcr.io/immich-app/immich-machine-learning:kustomized
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ports:
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- name: http
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containerPort: 3003
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env:
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- name: TZ
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value: "America/Los_Angeles"
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- name: TRANSFORMERS_CACHE
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value: /cache
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- name: HF_XET_CACHE
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value: /cache/huggingface-xet
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- name: MPLCONFIGDIR
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value: /cache/matplotlib-config
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volumeMounts:
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- name: cache
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mountPath: /cache
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livenessProbe:
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httpGet:
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path: /ping
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port: 3003
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initialDelaySeconds: 30
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periodSeconds: 30
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timeoutSeconds: 5
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readinessProbe:
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httpGet:
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path: /ping
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port: 3003
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initialDelaySeconds: 15
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periodSeconds: 10
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timeoutSeconds: 5
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resources:
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requests:
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memory: "512Mi"
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cpu: "100m"
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limits:
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memory: "4Gi"
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nvidia.com/gpu: "1"
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volumes:
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- name: cache
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persistentVolumeClaim:
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claimName: immich-ml-cache
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