629164261d
|
||
|---|---|---|
| .github/ISSUE_TEMPLATE | ||
| backend | ||
| components | ||
| contrib | ||
| docs | ||
| frontend | ||
| manifests | ||
| proxy | ||
| samples | ||
| sdk | ||
| test | ||
| third_party | ||
| .cloudbuild.yaml | ||
| .dockerignore | ||
| .gitattributes | ||
| .gitignore | ||
| .release.cloudbuild.yaml | ||
| .travis.yml | ||
| BUILD.bazel | ||
| CHANGELOG.md | ||
| CONTRIBUTING.md | ||
| LICENSE | ||
| Makefile | ||
| OWNERS | ||
| README.md | ||
| ROADMAP.md | ||
| WORKSPACE | ||
| developer_guide.md | ||
| go.mod | ||
| go.sum | ||
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.
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.