# pipelines

**Repository Path**: likang02/pipelines

## Basic Information

- **Project Name**: pipelines
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-12-07
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[![Coverage Status](https://coveralls.io/repos/github/kubeflow/pipelines/badge.svg?branch=master)](https://coveralls.io/github/kubeflow/pipelines?branch=master)
SDK: [![Documentation Status](https://readthedocs.org/projects/kubeflow-pipelines/badge/?version=latest)](https://kubeflow-pipelines.readthedocs.io/en/stable/?badge=latest)

## 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.

## Documentation

Install Kubeflow Pipelines from [an overview of several options](https://www.kubeflow.org/docs/pipelines/installation/overview/).

Get started with your first pipeline and read further information in the [Kubeflow Pipelines overview](https://www.kubeflow.org/docs/pipelines/overview/pipelines-overview/).

See the various ways you can [use the Kubeflow Pipelines SDK](https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/).

See the Kubeflow [Pipelines API doc](https://www.kubeflow.org/docs/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.

Refer to the [versioning policy](./docs/release/versioning-policy.md) and [feature stages](./docs/release/feature-stages.md) documentation for more information about how we manage versions and feature stages (such as Alpha, Beta, and Stable).

## Kubeflow Pipelines Community Meeting

The meeting is happening every other Wed 10-11AM (PST) 
[Calendar Invite](https://calendar.google.com/event?action=TEMPLATE&tmeid=NTdoNG5uMDBtcnJlYmdlOWt1c2lkY25jdmlfMjAxOTExMTNUMTgwMDAwWiBqZXNzaWV6aHVAZ29vZ2xlLmNvbQ&tmsrc=jessiezhu%40google.com&scp=ALL) or [Join Meeting Directly](https://meet.google.com/phd-ixfj-kcr/)

[Meeting notes](http://bit.ly/kfp-meeting-notes)

## Kubeflow Pipelines Slack Channel
[#kubeflow-pipelines](https://kubeflow.slack.com)

## 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](https://github.com/argoproj/argo) under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful.