Skip to content

kubeflow/pipelines

This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.

Folders and files

NameName
Last commit message
Last commit date

Latest commit

5190f68 · Aug 10, 2021
Jun 30, 2021
Aug 9, 2021
Aug 7, 2021
Aug 9, 2021
Mar 20, 2021
May 5, 2021
Aug 9, 2021
Jun 28, 2021
Aug 6, 2021
May 5, 2021
Aug 9, 2021
Aug 10, 2021
Aug 4, 2021
Aug 4, 2021
Jun 8, 2021
Aug 9, 2021
Aug 4, 2021
Jul 13, 2021
Jan 11, 2019
Jul 3, 2021
Nov 1, 2020
Aug 3, 2020
Aug 4, 2021
Jun 17, 2020
Jun 1, 2021
Aug 6, 2021
Feb 2, 2021
Nov 2, 2018
May 27, 2021
Jul 7, 2021
Jul 14, 2021
Jun 22, 2021
Aug 6, 2021
Jul 16, 2021
Jul 13, 2021
Aug 4, 2021
Aug 4, 2021
Jun 23, 2020
Jun 23, 2020
Nov 30, 2020
Feb 1, 2021
Feb 1, 2021

Repository files navigation

Coverage Status SDK: Documentation Status

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.

Documentation

Install Kubeflow Pipelines from an overview of several options.

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.

Refer to the versioning policy and feature stages documentation for more information about how we manage versions and feature stages (such as Alpha, Beta, and Stable).

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.

Kubeflow Pipelines Community Meeting

The meeting is happening every other Wed 10-11AM (PST) Calendar Invite or Join Meeting Directly

Meeting notes

Kubeflow Pipelines Slack Channel

#kubeflow-pipelines

Blog posts

Acknowledgments

Kubeflow pipelines uses Argo by default under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful. Additionally there is Tekton backend available as well. To access it, please refer to Kubeflow Pipelines with Tekton repository.