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* Update input arguments * Remove fileOutputs * Update outputs to new paths * Modify integ test artifact path * Add unit test for new output format * Add unit test for write_output * Migrate tests into test_utils * Add clarifying comment * Remove output path file extension * Update license to 0.5.2 * Update component to 0.5.2 * Add 0.5.2 to changelog * Remove JSON |
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README.md
SDK:
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 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.
Kubeflow Pipelines Community Meeting
The meeting is happening every other Wed 10-11AM (PST) Calendar Invite or Join Meeting Directly
Kubeflow Pipelines Slack Channel
Blog posts
- Getting started with Kubeflow Pipelines (By Amy Unruh)
- How to create and deploy a Kubeflow Machine Learning Pipeline (By Lak Lakshmanan)
Acknowledgments
Kubeflow pipelines uses Argo under the hood to orchestrate Kubernetes resources. The Argo community has been very supportive and we are very grateful.