https://github.com/kubeflow/pipelines
Machine Learning Pipelines for Kubeflow
https://github.com/kubeflow/pipelines
data-science kubeflow kubeflow-pipelines kubernetes machine-learning mlops pipeline
Last synced: 4 days ago
JSON representation
Machine Learning Pipelines for Kubeflow
- Host: GitHub
- URL: https://github.com/kubeflow/pipelines
- Owner: kubeflow
- License: apache-2.0
- Created: 2018-05-12T00:31:47.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2025-04-30T20:52:03.000Z (5 days ago)
- Last Synced: 2025-05-01T10:06:09.751Z (4 days ago)
- Topics: data-science, kubeflow, kubeflow-pipelines, kubernetes, machine-learning, mlops, pipeline
- Language: Python
- Homepage: https://www.kubeflow.org/docs/components/pipelines/
- Size: 327 MB
- Stars: 3,806
- Watchers: 99
- Forks: 1,719
- Open Issues: 274
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Security: SECURITY.md
- Roadmap: ROADMAP.md
- Authors: AUTHORS
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README
# Kubeflow Pipelines
[](https://coveralls.io/github/kubeflow/pipelines?branch=master)
[](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest)
[](https://pypi.org/project/kfp)
[](https://pypi.org/project/kfp)
[](https://www.bestpractices.dev/projects/9938)## Overview of the Kubeflow pipelines service
[Kubeflow](https://www.kubeflow.org/) 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](https://www.kubeflow.org/docs/started/installing-kubeflow/#kubeflow-platform). Alternatively you can deploy [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/operator-guides/installation/) as a standalone service.
* The Docker container runtime has been deprecated on Kubernetes 1.20+. Kubeflow Pipelines has switched to use [Emissary Executor](https://www.kubeflow.org/docs/components/pipelines/legacy-v1/installation/choose-executor/#emissary-executor) by default from Kubeflow Pipelines 1.8. Emissary executor is Container runtime agnostic, meaning you are able to run Kubeflow Pipelines on Kubernetes cluster with any [Container runtimes](https://kubernetes.io/docs/setup/production-environment/container-runtimes/).
## Documentation
Get started with your first pipeline and read further information in the [Kubeflow Pipelines overview](https://www.kubeflow.org/docs/components/pipelines/overview/).
See the various ways you can [use the Kubeflow Pipelines SDK](https://kubeflow-pipelines.readthedocs.io/en/stable/).
See the Kubeflow [Pipelines API doc](https://www.kubeflow.org/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/) for API specification.
Consult the [Python SDK reference docs](https://kubeflow-pipelines.readthedocs.io/en/stable/) when writing pipelines using the Python SDK.
## Contributing to Kubeflow Pipelines
Before you start contributing to Kubeflow Pipelines, read the guidelines in [How to Contribute](./CONTRIBUTING.md). To learn how to build and deploy Kubeflow Pipelines from source code, read the [developer guide](./developer_guide.md).
## Kubeflow Pipelines Community
### Community Meeting
The Kubeflow Pipelines Community Meeting occurs every other Wed 10-11AM (PST).
[Calendar Invite](https://calendar.google.com/event?action=TEMPLATE&tmeid=NTdoNG5uMDBtcnJlYmdlOWt1c2lkY25jdmlfMjAxOTExMTNUMTgwMDAwWiBqZXNzaWV6aHVAZ29vZ2xlLmNvbQ&tmsrc=jessiezhu%40google.com&scp=ALL)
[Direct Meeting Link](https://zoom.us/j/92607298595?pwd%3DVlKLUbiguGkbT9oKbaoDmCxrhbRop7.1&sa=D&source=calendar&ust=1736264977415448&usg=AOvVaw1EIkjFsKy0d4yQPptIJS3x)
[Meeting notes](http://bit.ly/kfp-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](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels)
## Architecture
Details about the KFP Architecture can be found at [Architecture.md](docs/Architecture.md)
## Blog posts
* [Getting started with Kubeflow Pipelines](https://cloud.google.com/blog/products/ai-machine-learning/getting-started-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](https://towardsdatascience.com/how-to-create-and-deploy-a-kubeflow-machine-learning-pipeline-part-1-efea7a4b650f)
* [Part 2: How to deploy Jupyter notebooks as components of a Kubeflow ML pipeline](https://towardsdatascience.com/how-to-deploy-jupyter-notebooks-as-components-of-a-kubeflow-ml-pipeline-part-2-b1df77f4e5b3)
* [Part 3: How to carry out CI/CD in Machine Learning (“MLOps”) using Kubeflow ML pipelines](https://medium.com/google-cloud/how-to-carry-out-ci-cd-in-machine-learning-mlops-using-kubeflow-ml-pipelines-part-3-bdaf68082112)## Acknowledgments
Kubeflow pipelines uses [Argo Workflows](https://github.com/argoproj/argo-workflows) by default under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful.