# yaml-language-server: $schema=values.schema.json
# Default values for mlflow.
# This is a YAML-formatted file.
# Declare variables to be passed into your templates.

# -- (int) Numbers of replicas
replicaCount: 1

# -- Image of mlflow
image:
  # -- The docker image repository to use
  repository: paasup/mlflow
  # -- The docker image pull policy
  pullPolicy: IfNotPresent
  # -- The docker image tag to use. Default app version
  tag: "v3.11.1-oidc"

# -- mlflow init images
initImages:
  # -- dbchecker init container image configuration
  dbchecker:
    # -- dbchecker init container image repository to use
    repository: busybox
    # -- dbchecker init container image pull policy
    pullPolicy: IfNotPresent
    # -- dbchecker init container image tag to use
    tag: "1.37"
  # -- mlflow-db-migration init container image configuration
  mlflowDbMigration:
    # -- mlflow-db-migration init container image repository to use.
    repository: paasup/mlflow
    # -- mlflow-db-migration init container image pull policy.
    pullPolicy: IfNotPresent
    # -- mlflow-db-migration init container image tag to use. Default app version
    tag: "v3.11.1-oidc"
  # -- ini-file-initializer init container image configuration
  iniFileInitializer:
    # -- ini-file-initializer init container image repository to use
    repository: busybox
    # -- ini-file-initializer init container image pull policy
    pullPolicy: IfNotPresent
    # -- ini-file-initializer init container image tag to use
    tag: "1.37"

# -- This will set the deployment strategy more information can be found here: https://kubernetes.io/docs/concepts/workloads/controllers/deployment/#strategy
strategy:
  type: "RollingUpdate"
  rollingUpdate:
    maxSurge: "100%"
    maxUnavailable: 0

# -- Image pull secrets for private docker registry usages
imagePullSecrets: []

# -- String to override the default generated name
nameOverride: ""

# -- String to override the default generated fullname
fullnameOverride: ""

serviceAccount:
  # -- Specifies whether a ServiceAccount should be created
  create: true

  # -- Automatically mount a ServiceAccount's API credentials?
  automount: true

  # -- Annotations to add to the service account. AWS EKS users can assign role arn from here.
  # Please find more information from here:
  # https://docs.aws.amazon.com/eks/latest/userguide/associate-service-account-role.html
  annotations: {}
    # eks.amazonaws.com/role-arn: ""

  # -- The name of the ServiceAccount to use.
  # If not set and create is true, a name is generated using the fullname template
  name: ""

# -- Annotations for the pod
podAnnotations: {}

# -- Extra labels for the pod
extraPodLabels: {}

# -- This is for setting Security Context to a Pod. For more information checkout: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
podSecurityContext:
  fsGroup: 1001
  fsGroupChangePolicy: "OnRootMismatch"

# -- This is for setting Security Context to a Container. For more information checkout: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/
securityContext:
  allowPrivilegeEscalation: false
  capabilities:
    drop:
      - ALL
  readOnlyRootFilesystem: false
  runAsNonRoot: true
  privileged: false
  runAsUser: 1001
  runAsGroup: 1001

# -- This is for setting up a service more information can be found here: https://kubernetes.io/docs/concepts/services-networking/service/
service:
  # -- Specifies if you want to create a service
  enabled: true
  # -- This sets the service type more information can be found here: https://kubernetes.io/docs/concepts/services-networking/service/#publishing-services-service-types
  type: ClusterIP
  # -- This sets the ports more information can be found here: https://kubernetes.io/docs/concepts/services-networking/service/#field-spec-ports
  port: 80
  # -- Default Service name
  name: http
  # -- Default container port
  containerPort: 5000
  # -- Default container port name
  containerPortName: mlflow
  # -- Additional service annotations
  annotations: {}

# -- Mlflow logging settings
log:
  # -- Specifies if you want to enable mlflow logging.
  enabled: true
  # -- Mlflow logging level.
  level: info

# -- Mlflow Usage Tracking settings. More information can be found here: https://mlflow.org/docs/latest/community/usage-tracking/
telemetry:
  # -- Specifies if you want to enable collecting anonymized usage data about how core features of the platform are used.
  enabled: false

# -- Mlflow database connection settings
backendStore:
  # -- Specifies if you want to run database migration
  databaseMigration: false

  # -- Add an additional init container, which checks for database availability
  databaseConnectionCheck: false

  # -- Specifies the default sqlite path
  defaultSqlitePath: ":memory:"

  postgres:
    # -- Specifies if you want to use postgres backend storage
    enabled: false
    # -- Postgres host address. e.g. your RDS or Azure Postgres Service endpoint
    host: "" # required
    # -- Postgres service port
    port: 5432 # required
    # -- mlflow database name created before in the postgres instance
    database: "" # required
    # -- postgres database user name which can access to mlflow database
    user: "" # required
    # -- postgres database user password which can access to mlflow database
    password: "" # required
    # -- postgres database connection driver. e.g.: "psycopg2"
    driver: ""

  mysql:
    # -- Specifies if you want to use mysql backend storage
    enabled: false
    # -- MySQL host address. e.g. your Amazon RDS for MySQL
    host: "" # required
    # -- MySQL service port
    port: 3306 # required
    # -- mlflow database name created before in the mysql instance
    database: "" # required
    # -- mysql database user name which can access to mlflow database
    user: "" # required
    # -- mysql database user password which can access to mlflow database
    password: "" # required
    # -- mysql database connection driver. e.g.: "pymysql"
    driver: "pymysql"

  mssql:
    # -- Specifies if you want to use mssql backend storage
    enabled: false
    # -- mssql host address
    host: "" # required
    # -- mssql service port
    port: 1433 # required
    # -- mlflow database name created before in the mssql instance
    database: "" # required
    # -- mssql database user name which can access to mlflow database
    user: "" # required
    # -- mssql database user password which can access to mlflow database
    password: "" # required
    # -- mssql database connection driver. e.g.: "pymssql"
    driver: "pymssql"

  # -- Specifies if you want to use an existing database secret.
  existingDatabaseSecret:
    # -- The name of the existing database secret.
    name: ""
    # -- The key of the username in the existing database secret.
    usernameKey: "username"
    # -- The key of the password in the existing database secret.
    passwordKey: "password"

# -- Bitnami PostgreSQL configuration. For more information checkout: https://github.com/bitnami/charts/tree/main/bitnami/postgresql
postgresql:
  # -- Enable postgresql
  enabled: false
  architecture: standalone

  image:
    repository: bitnamilegacy/postgresql

  primary:
    service:
      ports:
        postgresql: 5432

    persistence:
      enabled: true
      existingClaim: ""

  auth:
    username: ""
    password: ""
    # -- The name of the PostgreSQL database.
    database: "mlflow"

# -- Bitnami MySQL configuration. For more information checkout: https://github.com/bitnami/charts/tree/main/bitnami/mysql
mysql:
  # -- Enable mysql
  enabled: false
  architecture: standalone

  image:
    repository: bitnamilegacy/mysql

  primary:
    service:
      ports:
        mysql: 3306

    persistence:
      enabled: true
      existingClaim: ""

  auth:
    username: ""
    password: ""
    # -- The name of the MySQL database.
    database: "mlflow"

# Mlflow blob storage settings
artifactRoot:
  # -- Specifies if you want to enable proxied artifact storage access
  proxiedArtifactStorage: false

  # -- Specifies the default artifact root.
  defaultArtifactRoot: "./mlruns"

  # -- Specifies the default artifacts destination
  defaultArtifactsDestination: "./mlartifacts"

  # -- Specifies if you want to use Azure Blob Storage Mlflow Artifact Root
  azureBlob:
    # -- Specifies if you want to use Azure Blob Storage Mlflow Artifact Root
    enabled: false
    # -- Azure blob container name
    container: "" # required
    # -- Azure storage account name
    storageAccount: "" # required
    # -- Azure blob container folder. If you want to use root level, please don't set anything.
    path: "" # optional
    # -- Azure Cloud Connection String for the container. Only connectionString or accessKey required
    connectionString: "" # connectionString or accessKey required
    # -- Azure Cloud Storage Account Access Key for the container
    accessKey: "" # connectionString or accessKey required. Only connectionString or accessKey required

  # -- Specifies if you want to use AWS S3 Mlflow Artifact Root
  s3:
    # -- Specifies if you want to use AWS S3 Mlflow Artifact Root
    enabled: false
    # -- S3 bucket name
    bucket: "" # required
    # -- S3 bucket folder. If you want to use root level, please don't set anything.
    path: "" # optional
    # -- AWS IAM user AWS_ACCESS_KEY_ID which has attached policy for access to the S3 bucket
    awsAccessKeyId: "" # (awsAccessKeyId and awsSecretAccessKey) or roleArn serviceaccount annotation required
    # -- AWS IAM user AWS_SECRET_ACCESS_KEY which has attached policy for access to the S3 bucket
    awsSecretAccessKey: "" # (awsAccessKeyId and awsSecretAccessKey) or roleArn serviceaccount annotation required
    # -- Existing secret for AWS IAM user AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY secrets.
    existingSecret:
      # -- This is for setting up the AWS IAM user secrets existing secret name.
      name: ""
      # -- This is for setting up the key for AWS_ACCESS_KEY_ID secret. If it's set, awsAccessKeyId will be ignored.
      keyOfAccessKeyId: ""
      # -- This is for setting up the key for AWS_SECRET_ACCESS_KEY secret. If it's set, awsSecretAccessKey will be ignored.
      keyOfSecretAccessKey: ""

  # -- Specifies if you want to use Google Cloud Storage Mlflow Artifact Root
  gcs:
    # -- Specifies if you want to use Google Cloud Storage Mlflow Artifact Root
    enabled: false
    # -- Google Cloud Storage bucket name
    bucket: "" # required
    # -- Google Cloud Storage bucket folder. If you want to use root level, please don't set anything.
    path: "" # optional

# -- Mlflow Flask Server Secret Key. Default: Will be auto generated.
flaskServerSecretKey: ""

# -- Mlflow authentication settings
auth:
  # -- Specifies if you want to enable mlflow authentication. auth and ldapAuth can't be enabled at same time.
  enabled: false
  # -- Mlflow admin user username
  adminUsername: ""
  # -- Mlflow admin user password
  adminPassword: ""
  # -- Specifies if you want to use an existing admin credentials secret for auth. If it's set, adminUsername and adminPassword will be ignored.
  existingAdminSecret:
    # -- The name of the existing admin credentials secret.
    name: ""
    # -- The key of the admin username in the existing admin credentials secret.
    usernameKey: "username"
    # -- The key of the admin password in the existing admin credentials secret.
    passwordKey: "password"
  # -- Default permission for all users. More details: https://mlflow.org/docs/latest/auth/index.html#permissions
  defaultPermission: READ
  # -- Default registered authentication app name. If you want to use your custom authentication function, please look at: https://mlflow.org/docs/latest/auth/index.html#custom-authentication
  appName: "basic-auth"
  # -- Default authentication function
  authorizationFunction: "mlflow.server.auth:authenticate_request_basic_auth"
  # -- SQLite database file
  sqliteFile: "basic_auth.db"
  # -- SQLite database folder. Default is user home directory.
  sqliteFullPath: ""
  # -- Mlflow authentication INI configuration file path.
  configPath: "/etc/mlflow/auth/"
  # -- Mlflow authentication INI file
  configFile: "basic_auth.ini"
  # -- PostgreSQL based centrilised authentication database
  postgres:
    # -- Specifies if you want to use postgres auth backend storage
    enabled: false
    # -- Postgres host address. e.g. your RDS or Azure Postgres Service endpoint
    host: "" # required
    # -- Postgres service port
    port: 5432 # required
    # -- mlflow authorization database name created before in the postgres instance
    database: "" # required
    # -- postgres database user name which can access to mlflow authorization database
    user: "" # required
    # -- postgres database user password which can access to mlflow authorization database
    password: "" # required
    # -- postgres database connection driver. e.g.: "psycopg2"
    driver: ""
    # -- Specifies if you want to use an existing database secret for auth. If it's set, user and password will be ignored.
    existingSecret:
      # -- The name of the existing database secret.
      name: ""
      # -- The key of the username in the existing database secret.
      usernameKey: "username"
      # -- The key of the password in the existing database secret.
      passwordKey: "password"

# -- Basic Authentication with LDAP backend
ldapAuth:
  # -- Specifies if you want to enable mlflow LDAP authentication. auth and ldapAuth can't be enabled at same time.
  enabled: false
  # -- LDAP URI. e.g.: "ldap://lldap:3890/dc=mlflow,dc=test"
  uri: ""
  # -- TLS verification mode. Options: required, optional, none
  tlsVerification: required
  # -- LDAP Loopup Bind. e.g.: "uid=%s,ou=people,dc=mlflow,dc=test"
  lookupBind: ""
  # -- LDAP group attribute.
  groupAttribute: "dn"
  # -- Optional group attribute key for Active Directory users. e.g.: "attributes"
  groupAttributeKey: ""
  # -- LDAP base DN for the search. e.g.: "ou=groups,dc=mlflow,dc=test"
  searchBaseDistinguishedName: ""
  # -- LDAP query filter for search
  searchFilter: "(&(objectclass=groupOfUniqueNames)(uniquemember=%s))"
  # -- LDAP DN for the admin group. e.g.: "cn=test-admin,ou=groups,dc=mlflow,dc=test"
  adminGroupDistinguishedName: ""
  # -- LDAP DN for the user group. e.g.: "cn=test-user,ou=groups,dc=mlflow,dc=test"
  userGroupDistinguishedName: ""
  # -- Base64 encoded trusted CA certificate for LDAP server connection.
  encodedTrustedCACertificate: ""
  # -- External secret name for trusted CA certificate for LDAP server connection.
  externalSecretForTrustedCACertificate: ""

# -- Autoscaling settings. Can be enabled only when backendStore is not sqlite and artifactRoot is one of blob storage systems.
autoscaling:
  # -- If true, the number of replicas will be automatically scaled based on default metrics. On default, it will scale based on CPU and memory. For more information can be found here: https://kubernetes.io/docs/concepts/workloads/autoscaling/"
  enabled: false

  # -- The minimum number of replicas.
  minReplicas: 1

  # -- The maximum number of replicas.
  maxReplicas: 5

  # -- The metrics to use for autoscaling.
  metrics:
    - type: Resource
      resource:
        name: memory
        target:
          type: Utilization
          averageUtilization: 80
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 80

  # -- The behavior of the autoscaler. Only supported on K8s 1.18.0 or later.
  behavior: {}

# -- A map of arguments and values to pass to the `mlflow server` command. Keys must be camelcase. Helm will turn them to kebabcase style.
extraArgs: {}
  # workers: TEXT
  # staticPrefix: TEXT
  # gunicornOpts: TEXT
  # waitressOpts: TEXT

# -- A list of flags to pass to `mlflow server` command. Items must be camelcase. Helm will turn them to kebabcase style.
extraFlags: []
  # serveArtifacts
  # artifactsOnly

# -- Extra environment variables
extraEnvVars: {}
  # MLFLOW_S3_IGNORE_TLS: true
  # MLFLOW_S3_ENDPOINT_URL: http://1.2.3.4:9000
  # AWS_DEFAULT_REGION: my_region

# -- Extra secrets for environment variables
extraSecretNamesForEnvFrom: []

ingress:
  # -- Specifies if you want to create an ingress access
  enabled: false
  # -- New style ingress class name. Only possible if you use K8s 1.18.0 or later version
  className: ""
  # -- Additional ingress annotations
  annotations: {}
  hosts:
    - host: chart-example.local
      paths:
        - path: /
          # -- Ingress path type
          pathType: ImplementationSpecific
  # -- Ingress tls configuration for https access
  tls: []

# -- This block is for setting up the resource management for the pod more information can be found here: https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/
resources: {}

serviceMonitor:
  # -- When set true then use a ServiceMonitor to configure scraping
  enabled: false
  # -- When set true then use a service port. On default use a pod port.
  useServicePort: false
  # -- Set the namespace the ServiceMonitor should be deployed
  namespace: monitoring
  # -- Set how frequently Prometheus should scrape
  interval: 30s
  # -- Set path to mlflow telemetry-path
  telemetryPath: /metrics
  # -- Set labels for the ServiceMonitor, use this to define your scrape label for Prometheus Operator
  labels:
    release: prometheus
  # -- Set timeout for scrape
  timeout: 10s
  # -- Set of labels to transfer on the Kubernetes Service onto the target.
  targetLabels: []
  # -- Set of rules to relabel your exist metric labels
  metricRelabelings: []

# -- For more information checkout: https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#nodeselector
nodeSelector: {}

# -- For more information checkout: https://kubernetes.io/docs/concepts/scheduling-eviction/taint-and-toleration/
tolerations: []

# -- For more information checkout: https://kubernetes.io/docs/concepts/scheduling-eviction/assign-pod-node/#affinity-and-anti-affinity
affinity: {}

# -- Init Containers for Mlflow Pod
initContainers: []

# -- Extra containers for the mlflow pod
extraContainers: []

# -- Extra Volumes for the pod
extraVolumes: []

# -- Extra Volume Mounts for the mlflow container
extraVolumeMounts: []

# -- Liveness probe configurations. Please look to [here](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/#configure-probes).
livenessProbe:
  initialDelaySeconds: 10
  periodSeconds: 30
  timeoutSeconds: 3
  failureThreshold: 5

# -- Readiness probe configurations. Please look to [here](https://kubernetes.io/docs/tasks/configure-pod-container/configure-liveness-readiness-startup-probes/#configure-probes).
readinessProbe:
  initialDelaySeconds: 10
  periodSeconds: 30
  timeoutSeconds: 3
  failureThreshold: 5