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Implementation:Kubeflow Pipelines Install Config

From Leeroopedia
Knowledge Sources
Domains Deployment, Configuration
Last Updated 2026-02-13 14:00 GMT

Overview

Central ConfigMap defining database settings, caching behavior, artifact storage, and deployment-wide parameters for KFP installation.

Description

The pipeline-install-config ConfigMap (109 lines) is the single source of truth for KFP deployment configuration. It contains: MySQL connection info (dbHost, dbPort: 3306), database names (mlmdDb, cacheDb, pipelineDb), artifact bucket name, default pipeline root, cache staleness limits, cache image, cron timezone, connection lifetime, log level, artifact proxy toggle, and artifact retention days. Referenced by multiple components via environment variable injection.

Usage

Applied during KFP deployment. Operators modify this ConfigMap to configure database connections, storage backends, and caching behavior for their specific environment.

Code Reference

Source Location

Signature

apiVersion: v1
kind: ConfigMap
metadata:
  name: pipeline-install-config
data:
  dbHost: "mysql"
  dbPort: "3306"
  mlmdDb: "metadb"
  cacheDb: "cachedb"
  pipelineDb: "mlpipeline"
  bucketName: "mlpipeline"
  defaultPipelineRoot: ""
  cacheImage: "ghcr.io/containerd/busybox"
  ConMaxLifeTime: "120s"
  ARTIFACT_COPY_STEP_CABUNDLE_CONFIGMAP_NAME: ""
  ARTIFACT_COPY_STEP_CABUNDLE_CONFIGMAP_KEY: ""
  ARTIFACT_COPY_STEP_CABUNDLE_MOUNTPATH: ""

Import

kubectl apply -f pipeline-install-config.yaml

I/O Contract

Inputs

Name Type Required Description
Database credentials ConfigMap data Yes MySQL host, port, database names
Storage config ConfigMap data Yes Bucket name, pipeline root path

Outputs

Name Type Description
Environment variables env injection Consumed by cache server, API server, metadata writer via ConfigMap refs

Usage Examples

Customizing for Production

apiVersion: v1
kind: ConfigMap
metadata:
  name: pipeline-install-config
data:
  dbHost: "cloudsql-proxy"
  dbPort: "3306"
  mlmdDb: "prod_metadb"
  cacheDb: "prod_cachedb"
  pipelineDb: "prod_mlpipeline"
  bucketName: "my-prod-bucket"
  defaultPipelineRoot: "gs://my-prod-bucket/pipeline-root"

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