Plugin maintains the configuration in the conf/base/kubeflow.yaml file. Sample configuration can be generated using kedro kubeflow init:

# Base url of the Kubeflow Pipelines, should include the schema (http/https)

# Configuration used to run the pipeline

  # Name of the image to run as the pipeline steps
  image: kubeflow-plugin-demo

  # Pull pilicy to be used for the steps. Use Always if you push the images
  # on the same tag, or Never if you use only local images
  image_pull_policy: IfNotPresent
  # Location of Vertex AI GCS root, required only for vertex ai pipelines configuration
  root: bucket_name/gcs_suffix

  # Name of the kubeflow experiment to be created
  experiment_name: Kubeflow Plugin Demo

  # Name of the run for run-once
  run_name: Kubeflow Plugin Demo Run

  # Optional pipeline description
  description: Very Important Pipeline

  # Flag indicating if the run-once should wait for the pipeline to finish
  wait_for_completion: False

  # How long to keep underlying Argo workflow (together with pods and data
  # volume after pipeline finishes) [in seconds]. Default: 1 week
  ttl: 604800

  # Optional volume specification

    # Storage class - use null (or no value) to use the default storage
    # class deployed on the Kubernetes cluster
    storageclass: # default

    # The size of the volume that is created. Applicable for some storage
    # classes
    size: 1Gi

    # Access mode of the volume used to exchange data. ReadWriteMany is
    # preferred, but it is not supported on some environements (like GKE)
    # Default value: ReadWriteOnce
    #access_modes: [ReadWriteMany]

    # Flag indicating if the data-volume-init step (copying raw data to the
    # fresh volume) should be skipped
    skip_init: False

    # Allows to specify user executing pipelines within containers
    # Default: root user (to avoid issues with volumes in GKE)
    owner: 0

    # Flak indicating if volume for inter-node data exchange should be
    # kept after the pipeline is deleted
    keep: False

  # Optional section allowing adjustment of the resources
  # reservations and limits for the nodes

    # For nodes that require more RAM you can increase the "memory"
      memory: 2Gi

    # Training nodes can utilize more than one CPU if the algoritm
    # supports it
      cpu: 8
      memory: 1Gi

    # GPU-capable nodes can request 1 GPU slot
    tensorflow_step: 1

    # Default settings for the nodes
      cpu: 200m
      memory: 64Mi

Dynamic configuration support

kedro-kubeflow contains hook that enables TemplatedConfigLoader. It allows passing environment variables to configuration files. It reads all environment variables following KEDRO_CONFIG_<NAME> pattern, which you can later inject in configuration file using ${name} syntax.

There are two special variables KEDRO_CONFIG_COMMIT_ID, KEDRO_CONFIG_BRANCH_NAME with support specifying default when variable is not set, e.g. ${commit_id|dirty}