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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: 14 days ago
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Machine Learning Pipelines for Kubeflow

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README 

        
# Kubeflow Pipelines



[![Coverage Status](https://coveralls.io/repos/github/kubeflow/pipelines/badge.svg?branch=master)](https://coveralls.io/github/kubeflow/pipelines?branch=master)

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[![SDK Package version](https://img.shields.io/pypi/v/kfp?color=%2334D058&label=pypi%20package)](https://pypi.org/project/kfp)

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[![OpenSSF Best Practices](https://www.bestpractices.dev/projects/9938/badge)](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://medium.com/data-science/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://medium.com/data-science/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.