mirrors/pipelines

Fix sample test failure because of the type information in the pipelineparam (#972)

fix bug: op_to_template resolve the raw arguments by mapping to the argument_inputs but the argument_inputs lost the type information
fix type pattern matching
convert orderedDict to dict from the component module

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

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

Blog posts

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

Acknowledgments

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