charts/.vib/mlflow/runtime-parameters.yaml

tracking:
  enabled: true
  auth:
    enabled: true
    username: vib-user
    password: "ComplicatedPassword123!4"
    extraOverrides:
      default_permission: WRITE
  containerPorts:
    http: 8100
  service:
    type: LoadBalancer
    ports:
      http: 80
  serviceAccount:
    create: true
  automountServiceAccountToken: true
  containerSecurityContext:
    enabled: true
    runAsUser: 1002
    runAsGroup: 1002
    runAsNonRoot: true
    readOnlyRootFilesystem: true
    allowPrivilegeEscalation: false
    capabilities:
      drop: ["ALL"]
  podSecurityContext:
    enabled: true
    fsGroup: 1002
    seccompProfile:
      type: RuntimeDefault
  persistence:
    enabled: true
    mountPath: /vib-mlflow/test
  metrics:
    enabled: true

run:
  enabled: true
  useJob: true
  source:
    launchCommand: "python vib_test.py"
    configMap:
      # Example taken from the MLFlow UI (https://mlflow.org/docs/latest/ml/tracking/tutorials/local-database#step-3-start-logging)
      vib_test.py: |
          import mlflow

          from sklearn.model_selection import train_test_split
          from sklearn.datasets import load_diabetes
          from sklearn.ensemble import RandomForestRegressor

          mlflow.sklearn.autolog()

          db = load_diabetes()
          X_train, X_test, y_train, y_test = train_test_split(db.data, db.target)

          # Create and train models.
          rf = RandomForestRegressor(n_estimators=100, max_depth=6, max_features=3)
          rf.fit(X_train, y_train)

          # Use the model to make predictions on the test dataset.
          predictions = rf.predict(X_test)