Kubeflow Pipelines

Overview of the Kubeflow pipelines service

Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable.

Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

The Kubeflow pipelines service has the following goals:

• End to end orchestration: enabling and simplifying the orchestration of end to end machine learning pipelines
• Easy experimentation: making it easy for you to try numerous ideas and techniques, and manage your various trials/experiments.
• Easy re-use: enabling you to re-use components and pipelines to quickly cobble together end to end solutions, without having to re-build each time.

Installation

• Kubeflow Pipelines can be installed as part of the Kubeflow Platform. Alternatively you can deploy Kubeflow Pipelines as a standalone service.

• Pipeline execution uses the Argo Emissary executor, which is independent of the Kubernetes container runtime.

Dependencies Compatibility Matrix

Dependency	Versions
Argo Workflows	v4.0, v4.1
MySQL	v8

KFP 3.0 compatibility notice: This branch supports Argo Workflows 4.0.x and 4.1.x, tested at 4.0.13 and 4.1.6 respectively. Argo 3.x is not supported on this branch; KFP 2.x release branches retain their documented compatibility coverage. Before upgrading to KFP 3.0, operators must upgrade their controller to a supported 4.x release. Follow the KFP 3.0 compatibility tracking issue for the removal and migration work.

Documentation

See Releases for what changed, upgrade guidance, compatibility, and the maintainer release runbook.

Get started with your first pipeline and read further information in the Kubeflow Pipelines overview.

See the various ways you can use the Kubeflow Pipelines SDK.

See the Kubeflow Pipelines API doc for API specification.

Consult the Python SDK reference docs when writing pipelines using the Python SDK.

Deep Wiki

Check out our AI Powered repo documentation on DeepWiki.

:warning: Please note, this is AI generated and may not have completely accurate information.

Contributing to Kubeflow Pipelines

Before you start contributing to Kubeflow Pipelines, read the guidelines in How to Contribute. To learn how to build and deploy Kubeflow Pipelines from source code, read the developer guide.

Optional just command runner

For local developer convenience, this repository includes an optional just command runner at the repo root. It provides short aliases for existing make targets and does not replace any CI or release workflows.

To use it, install just and run, for example:

just           # list available recipes
just backend-test
just backend-images

Notes:

• All just recipes are thin wrappers around existing make targets (for example, make -C backend/src/v2 test).
• There is intentionally no generic just build or just test recipe; heavy or Docker-building flows are exposed only via explicitly named recipes such as backend-images.

Kubeflow Pipelines Community

Community Meeting

The Kubeflow Pipelines Community Meeting occurs every other Wed 10-11AM (PST).

Calendar Invite

Direct Meeting Link

Meeting notes

Slack

We also have a slack channel (#kubeflow-pipelines) on the Cloud Native Computing Foundation Slack workspace. You can find more details at https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels

Architecture

Details about the KFP Architecture can be found at Architecture.md

Blog posts

• From Raw Data to Model Serving: A Blueprint for the AI/ML Lifecycle with Kubeflow (By Helber Belmiro)
• Getting started with Kubeflow Pipelines (By Amy Unruh)
• How to create and deploy a Kubeflow Machine Learning Pipeline (By Lak Lakshmanan)
  • Part 1: How to create and deploy a Kubeflow Machine Learning Pipeline
  • Part 2: How to deploy Jupyter notebooks as components of a Kubeflow ML pipeline
  • Part 3: How to carry out CI/CD in Machine Learning (“MLOps”) using Kubeflow ML pipelines

Acknowledgments

Kubeflow pipelines uses Argo Workflows by default under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful.