wehub-resource-sync
98 lines
2.4 KiB
YAML
98 lines
2.4 KiB
YAML
# Example values for the MLflow Helm chart (SQLite + local storage).
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# Usage:
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# helm upgrade --install mlflow ./charts --namespace mlflow --create-namespace -f charts/example-mlflow-charts.yaml
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image:
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repository: ghcr.io/mlflow/mlflow
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tag: "v3.11.1-full"
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replicaCount: 1
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server:
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value_options:
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host: "0.0.0.0"
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port: 5000
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workers: 4
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# Uncomment to enable the basic-auth plugin:
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# app_name: "basic-auth"
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# Bare flags rendered as --flag. Uncomment as needed.
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flag_options: []
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# - no_serve_artifacts
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mlflow:
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backendStoreUri: "sqlite:////mlflow/mlflow.db"
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artifactsDestination: "/mlflow/artifacts"
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# --- For production: use a real database and object store instead ---
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# Option A: inline URI (password visible in values — development only).
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# Option B: reference a Secret (recommended for production).
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#
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# Create the Secret first:
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# kubectl create secret generic mlflow-db-secret \
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# --from-literal=uri="postgresql://user:password@postgres:5432/mlflow"
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# backendStoreUri: "postgresql://user:password@postgres:5432/mlflow"
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# backendStoreUriFrom:
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# secretKeyRef:
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# name: mlflow-db-secret
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# key: uri
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# artifactsDestination: "s3://my-bucket/mlflow"
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storage:
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enabled: true
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# StorageClass to use. Set to match your cluster
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# (e.g. "gp2" for standard EKS, "standard" for Kind/minikube).
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storageClassName: "gp2"
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# S3 credentials (or other artifact store credentials):
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# kubectl create secret generic s3-credentials \
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# --from-literal=access-key-id=<key> \
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# --from-literal=secret-access-key=<secret>
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# env:
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# - name: AWS_ACCESS_KEY_ID
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# valueFrom:
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# secretKeyRef:
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# name: s3-credentials
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# key: access-key-id
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# - name: AWS_SECRET_ACCESS_KEY
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# valueFrom:
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# secretKeyRef:
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# name: s3-credentials
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# key: secret-access-key
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service:
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type: ClusterIP
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# Expose via Ingress:
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# ingress:
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# enabled: true
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# className: nginx
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# hosts:
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# - host: mlflow.example.com
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# paths:
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# - path: /
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# pathType: Prefix
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# Prometheus metrics:
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metrics:
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enabled: false
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# Prometheus ServiceMonitor (requires Prometheus Operator):
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# serviceMonitor:
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# enabled: true
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# Garbage collection (removes permanently soft-deleted resources):
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garbageCollection:
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enabled: true
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schedule: "0 2 * * 0"
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olderThan: "30d"
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resources:
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requests:
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cpu: 500m
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memory: 512Mi
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limits:
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cpu: 2000m
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memory: 2Gi
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