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Components v external add ons (#2630)
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* move istio, serving, and feature-store to external-add-ons

* add description for external add-ons

* add fairing to external add-ons

* add kale to external add-ons

* update kale description and link to tutorial

* update redirects for components -> external-add-ons

* update fairing internal links

* update istio internal links

* update feature-store internal links

* update serving internal links
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shannonbradshaw committed Apr 21, 2021
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6 changes: 3 additions & 3 deletions content/en/_index.html
Expand Up @@ -89,10 +89,10 @@ <h5 class="card-title text-white section-head">
<div class="card-body bg-primary-dark">
<h5 class="card-title text-white section-head">Model serving</h5>
<p class="card-text text-white">
Kubeflow supports a <a target="_blank" rel="noopener" href="/docs/components/serving/tfserving_new/">TensorFlow Serving</a> container to export trained
Kubeflow supports a <a target="_blank" rel="noopener" href="/docs/external-add-ons/serving/tfserving_new/">TensorFlow Serving</a> container to export trained
TensorFlow models to Kubernetes. Kubeflow is also integrated with
<a target="_blank" rel="noopener" href="/docs/components/serving/seldon/">Seldon Core</a>, an open source platform for deploying machine learning
models on Kubernetes, and <a target="_blank" rel="noopener" href="/docs/components/serving/tritoninferenceserver/">NVIDIA Triton Inference Server</a> for
<a target="_blank" rel="noopener" href="/docs/external-add-ons/serving/seldon/">Seldon Core</a>, an open source platform for deploying machine learning
models on Kubernetes, and <a target="_blank" rel="noopener" href="/docs/external-add-ons/serving/tritoninferenceserver/">NVIDIA Triton Inference Server</a> for
maximized GPU utilization when deploying ML/DL models at scale.
</p>
</div>
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24 changes: 17 additions & 7 deletions content/en/_redirects
Expand Up @@ -75,15 +75,15 @@ docs/started/requirements/ /docs/started/getting-started/
/docs/components/pipelines /docs/components/pipelines/pipelines
/docs/components/pytorch /docs/components/training/pytorch
/docs/components/nuclio /docs/components/misc/nuclio
/docs/components/seldon /docs/components/serving/seldon
/docs/components/trtinferenceserver /docs/components/serving/tritoninferenceserver
/docs/components/tfbatchpredict /docs/components/serving/tfbatchpredict
/docs/components/seldon /docs/external-add-ons/serving/seldon
/docs/components/trtinferenceserver /docs/external-add-ons/serving/tritoninferenceserver
/docs/components/tfbatchpredict /docs/external-add-ons/serving/tfbatchpredict
/docs/components/tftraining /docs/components/training/tftraining
/docs/components/tfserving_new /docs/components/serving/tfserving_new
/docs/components/tfserving_new /docs/external-add-ons/serving/tfserving_new

# Deleted the PyTorch serving page
/docs/components/pytorchserving/ /docs/components/serving/overview/
/docs/components/serving/pytorchserving/ /docs/components/serving/overview/
/docs/components/pytorchserving/ /docs/external-add-ons/serving/overview/
/docs/components/serving/pytorchserving/ /docs/external-add-ons/serving/overview/

# Restructured the getting-started and other-guides sections.
/docs/started/getting-started-k8s/ /docs/started/k8s/
Expand All @@ -110,7 +110,7 @@ docs/started/requirements/ /docs/started/getting-started/
/docs/other-guides/multi-user-overview/ /docs/components/multi-tenancy/

# Rename TensorRT Inference Server to Triton Inference Server
/docs/components/serving/trtinferenceserver /docs/components/serving/tritoninferenceserver
/docs/components/serving/trtinferenceserver /docs/external-add-ons/serving/tritoninferenceserver

# Kubeflow Operator move to under distributions
/docs/operator /docs/distributions/operator
Expand Down Expand Up @@ -144,6 +144,12 @@ docs/started/requirements/ /docs/started/getting-started/
/docs/started/workstation/minikf-aws /docs/distributions/minikf-aws
/docs/started/workstation/minikf-gcp /docs/distributions/minikf-gcp

# Distinguish components from external add-ons
/docs/components/fairing /docs/external-add-ons/fairing
/docs/components/istio /docs/external-add-ons/istio
/docs/components/feature-store /docs/external-add-ons/feature-store
/docs/components/serving /docs/external-add-ons/serving

# ===============
# IMPORTANT NOTE:
# Catch-all redirects should be added at the end of this file as redirects happen from top to bottom
Expand All @@ -156,3 +162,7 @@ docs/started/requirements/ /docs/started/getting-started/
/docs/gke/* /docs/distributions/gke/:splat
/docs/ibm/* /docs/distributions/ibm/:splat
/docs/openshift/* /docs/distributions/openshift/:splat
/docs/components/fairing/* /docs/external-add-ons/fairing/:splat
/docs/components/istio/* /docs/external-add-ons/istio/:splat
/docs/components/feature-store/* /docs/external-add-ons/feature-store/:splat
/docs/components/serving/* /docs/external-add-ons/serving/:splat
2 changes: 1 addition & 1 deletion content/en/docs/components/notebooks/setup.md
Expand Up @@ -300,7 +300,7 @@ exposed to the internet and is an unsecured endpoint by default.
* See a [simple example](https://github.com/kubeflow/examples/tree/master/pipelines/simple-notebook-pipeline) of creating Kubeflow pipelines in a Jupyter notebook on GCP.
* Build machine-learning pipelines with the [Kubeflow Pipelines
SDK](/docs/components/pipelines/sdk/sdk-overview/).
* Explore [Kubeflow Fairing](/docs/components/fairing/) for a complete solution to
* Explore [Kubeflow Fairing](/docs/external-add-ons/fairing/) for a complete solution to
building, training, and deploying an ML model from a notebook.
* See how to configure [multi-user isolation](/docs/components/multi-tenancy/) in Kubeflow, to separate the notebooks for each user in a shared Kubeflow deployment.
* Learn the advanced features available from a Kubeflow notebook, such as
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2 changes: 1 addition & 1 deletion content/en/docs/distributions/gke/deploy/project-setup.md
Expand Up @@ -31,7 +31,7 @@ Follow these steps to set up your GCP project:
* [Service Management API](https://console.cloud.google.com/apis/api/servicemanagement.googleapis.com)
* [Cloud Resource Manager API](https://console.developers.google.com/apis/library/cloudresourcemanager.googleapis.com)
* [AI Platform Training & Prediction API](https://console.developers.google.com/apis/library/ml.googleapis.com)
* [Cloud Build API](https://console.cloud.google.com/apis/library/cloudbuild.googleapis.com) (It's required if you plan to use [Fairing](https://www.kubeflow.org/docs/components/fairing/) in your Kubeflow cluster)
* [Cloud Build API](https://console.cloud.google.com/apis/library/cloudbuild.googleapis.com) (It's required if you plan to use [Fairing](https://www.kubeflow.org/docs/external-add-ons/fairing/) in your Kubeflow cluster)

You can also enable these APIs by running the following command in Cloud Shell:
```bash
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5 changes: 5 additions & 0 deletions content/en/docs/external-add-ons/_index.md
@@ -0,0 +1,5 @@
+++
title = "External Add-Ons"
description = "Additional tools that may be integrated with a Kubeflow deployment or distribution."
weight = 30
+++
Expand Up @@ -81,7 +81,7 @@ Kubeflow cluster.
[kubeflow-install]: /docs/started/getting-started/
[kubectl-access]: https://kubernetes.io/docs/reference/access-authn-authz/authentication/
[kubectl-install]: https://kubernetes.io/docs/tasks/tools/install-kubectl/
[conf-gcp]: /docs/components/fairing/gcp/configure-gcp/
[conf-gcp]: /docs/external-add-ons/fairing/gcp/configure-gcp/
[docker-login]: https://docs.docker.com/engine/reference/commandline/login/
[fairing-install]: /docs/components/fairing/install-fairing/
[tutorials]: /docs/components/fairing/tutorials/other-tutorials/
[fairing-install]: /docs/external-add-ons/fairing/install-fairing/
[tutorials]: /docs/external-add-ons/fairing/tutorials/other-tutorials/
Expand Up @@ -59,6 +59,6 @@ The following are the goals of the [Kubeflow Fairing project][fairing-repo]:
[fairing-repo]: https://github.com/kubeflow/fairing
[kubeflow]: /docs/about/kubeflow/

[conf]: /docs/components/fairing/configure-fairing/
[install]: /docs/components/fairing/install-fairing/
[tutorials]: /docs/components/fairing/tutorials/other-tutorials/
[conf]: /docs/external-add-ons/fairing/configure-fairing/
[install]: /docs/external-add-ons/fairing/install-fairing/
[tutorials]: /docs/external-add-ons/fairing/tutorials/other-tutorials/
Expand Up @@ -130,5 +130,5 @@ GKE][kubeflow-gcp-install] to set one up.
[gcloud-install]: https://cloud.google.com/sdk/docs/
[kubeflow-gcp-install]: /docs/gke/deploy/
[container-registry]: https://cloud.google.com/container-registry/
[fairing-install]: /docs/components/fairing/install-fairing/
[tutorials]: /docs/components/fairing/gcp/tutorials/
[fairing-install]: /docs/external-add-ons/fairing/install-fairing/
[tutorials]: /docs/external-add-ons/fairing/gcp/tutorials/
Expand Up @@ -225,5 +225,5 @@ development environments for training and prediction from Kubeflow Fairing.
[kubeflow-install-gke]: https://www.kubeflow.org/docs/gke/deploy/
[kubeflow-install]: https://www.kubeflow.org/docs/gke/deploy/deploy-cli/
[kubeflow-deploy]: https://deploy.kubeflow.cloud
[gcp]: /docs/components/fairing/configure-gcp.md
[gcp]: /docs/external-add-ons/fairing/configure-gcp.md
[container-registry]: https://cloud.google.com/container-registry/
Expand Up @@ -233,8 +233,8 @@ use for training and deployment, follow the instructions in the guide to
[docker-attack]: https://docs.docker.com/engine/security/security/#docker-daemon-attack-surface
[docker-start]: https://docs.docker.com/config/daemon/#start-the-daemon-manually
[kubectl-install]: https://kubernetes.io/docs/tasks/tools/install-kubectl/
[conf]: /docs/components/fairing/configure-fairing/
[conf-gcp]: /docs/components/fairing/gcp/configure-gcp/
[tutorials]: /docs/components/fairing/tutorials/other-tutorials/
[conf]: /docs/external-add-ons/fairing/configure-fairing/
[conf-gcp]: /docs/external-add-ons/fairing/gcp/configure-gcp/
[tutorials]: /docs/external-add-ons/fairing/tutorials/other-tutorials/
[local]: #set-up-kubeflow-fairing-for-local-development
[hosted]: #set-up-kubeflow-fairing-in-a-hosted-jupyter-notebook
Expand Up @@ -19,6 +19,6 @@ and deploy on various environments such as on the Google Cloud Platform (GCP).
* Learn how to [train and deploy a model on Azure from a notebook hosted on
Kubeflow][azure-fairing].

[gcp-local]: /docs/components/fairing/gcp/tutorials/gcp-local-notebook/
[gcp-kubeflow]: /docs/components/fairing/gcp/tutorials/gcp-kubeflow-notebook/
[azure-fairing]: /docs/components/fairing/azure/
[gcp-local]: /docs/external-add-ons/fairing/gcp/tutorials/gcp-local-notebook/
[gcp-kubeflow]: /docs/external-add-ons/fairing/gcp/tutorials/gcp-kubeflow-notebook/
[azure-fairing]: /docs/external-add-ons/fairing/azure/
Expand Up @@ -7,7 +7,7 @@ weight = 20

This guide provides the necessary resources to install [Feast](http://feast.dev/) alongside Kubeflow, describes the usage of Feast with Kubeflow components, and provides examples that users can follow to test their setup.

For an overview of Feast, please read [Introduction to Feast](/docs/components/feature-store/overview/).
For an overview of Feast, please read [Introduction to Feast](/docs/external-add-ons/feature-store/overview/).

## Installing Feast with Kubeflow

Expand Down
Expand Up @@ -48,7 +48,7 @@ Feast provides the following functionality:

## Next steps

Please follow the [Getting Started with Feast](/docs/components/feature-store/getting-started/) guide to set up Feast and run walk through our tutorials.
Please follow the [Getting Started with Feast](/docs/external-add-ons/feature-store/getting-started/) guide to set up Feast and run walk through our tutorials.

## Resources

Expand Down
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9 changes: 9 additions & 0 deletions content/en/docs/external-add-ons/kale/_index.md
@@ -0,0 +1,9 @@
+++
title = "Kale"
description = "Kale enables data scientists to orchestrate end-to-end machine learning (ML) workflows."
weight = 30
+++

Kale simplifies the use of Kubeflow, giving data scientists the tool they need to orchestrate end-to-end ML workflows. Kale provides a UI in the form of a JupyterLab extension. You can annotate cells in Jupyter Notebooks to define: pipeline steps, hyperparameter tuning, GPU usage, and metrics tracking. Click a button to create pipeline components and KFP DSL, resolve dependencies, inject data objects into each step, deploy the data science pipeline, and serve the best model.

See <a href="https://codelabs.arrikto.com/codelabs/minikf-kale-katib-kfserving/#0" target="_blank">From Notebook to Kubeflow Pipelines to KFServing</a> for a tutorial overview of Kale.
File renamed without changes.
Expand Up @@ -162,19 +162,19 @@ Notes:
Further information:

* KFServing:
* [Kubeflow documentation](/docs/components/serving/kfserving/)
* [Kubeflow documentation](/docs/components/kfserving/)
* [GitHub repository](https://github.com/kubeflow/kfserving)
* [Community](/docs/about/community/)
* Seldon Core
* [Kubeflow documentation](/docs/components/serving/seldon/)
* [Kubeflow documentation](/docs/external-add-ons/serving/seldon/)
* [Seldon Core documentation](https://docs.seldon.io/projects/seldon-core/en/latest/)
* [GitHub repository](https://github.com/SeldonIO/seldon-core)
* [Community](https://docs.seldon.io/projects/seldon-core/en/latest/developer/community.html)

## TensorFlow Serving

For TensorFlow models you can use TensorFlow Serving for
[real-time prediction](/docs/components/serving/tfserving_new).
[real-time prediction](/docs/external-add-ons/serving/tfserving_new).
However, if you plan to use multiple frameworks, you should consider KFServing
or Seldon Core as described above.

Expand All @@ -186,7 +186,7 @@ optimized to deploy machine learning algorithms on both GPUs and
CPUs at scale. Triton Inference Server was previously known as TensorRT Inference Server.

You can use NVIDIA Triton Inference Server as a
[standalone system](/docs/components/serving/tritoninferenceserver),
[standalone system](/docs/external-add-ons/serving/tritoninferenceserver),
but you should consider KFServing as described above. KFServing includes support
for NVIDIA Triton Inference Server.

Expand All @@ -202,7 +202,7 @@ support, which achieves the advantage of batch processing in online serving. It
provides model management and model deployment functionality, giving ML teams an
end-to-end model serving workflow, with DevOps best practices baked in.

* [BentoML guide for Kubeflow](/docs/components/serving/bentoml)
* [BentoML guide for Kubeflow](/docs/external-add-ons/serving/bentoml)
* [BentoML GitHub repository](https://github.com/bentoml/BentoML)
* [BentoML documentation](https://docs.bentoml.org)
* [Quick start guide](https://docs.bentoml.org/en/latest/quickstart.html)
Expand Down
@@ -1,6 +1,6 @@
+++
title = "TensorFlow Batch Prediction"
description = "See Kubeflow [v0.6 docs](https://v0-6.kubeflow.org/docs/components/serving/tfbatchpredict/) for batch prediction with TensorFlow models"
description = "See Kubeflow [v0.6 docs](https://v0-6.kubeflow.org/docs/external-add-ons/serving/tfbatchpredict/) for batch prediction with TensorFlow models"
weight = 60

+++
Expand All @@ -14,5 +14,5 @@ needs to be updated for Kubeflow 1.1.

[TensorFlow batch prediction](https://github.com/kubeflow/batch-predict) is not
supported in Kubeflow versions greater than v0.6. See the [Kubeflow v0.6
documentation](https://v0-6.kubeflow.org/docs/components/serving/tfbatchpredict/)
documentation](https://v0-6.kubeflow.org/docs/external-add-ons/serving/tfbatchpredict/)
for earlier support for batch prediction with TensorFlow models.
Expand Up @@ -385,7 +385,7 @@ python iap_request.py https://YOUR_HOST/models/MODEL_NAME/ IAP_CLIENT_ID

## Telemetry and Rolling out model using Istio

Please look at the [Istio guide](/docs/components/istio/).
Please look at the [Istio guide](/docs/external-add-ons/istio/).

## Logs and metrics with Stackdriver
See the guide to [logging and monitoring](/docs/gke/monitoring/)
Expand Down
8 changes: 4 additions & 4 deletions content/en/docs/reference/version-policy.md
Expand Up @@ -107,7 +107,7 @@ documentation for that application.
<td></td>
</tr>
<tr>
<td><a href="/docs/components/feature-store/overview">Feature store: Feast</a>
<td><a href="/docs/external-add-ons/feature-store/overview">Feature store: Feast</a>
(<a href="https://github.com/feast-dev/feast">GitHub</a>)
</td>
<td>Alpha</td>
Expand All @@ -122,7 +122,7 @@ documentation for that application.
<td>v1alpha3</td>
</tr>
<tr>
<td><a href="/docs/components/serving/kfserving/">KFServing</a>
<td><a href="/docs/external-add-ons/serving/kfserving/">KFServing</a>
(<a href="https://github.com/kubeflow/kfserving">GitHub</a>)
</td>
<td>Beta</td>
Expand Down Expand Up @@ -189,7 +189,7 @@ documentation for that application.
<td>1.0.0</td>
</tr>
<tr>
<td><a href="/docs/components/serving/seldon">Seldon Core Serving</a>
<td><a href="/docs/external-add-ons/serving/seldon">Seldon Core Serving</a>
(<a href="https://github.com/SeldonIO/seldon-core">GitHub</a>)
</td>
<td>Stable</td>
Expand Down Expand Up @@ -232,7 +232,7 @@ one of the following Kubeflow SDKs and command-line interfaces
</thead>
<tbody>
<tr>
<td><a href="/docs/components/fairing/fairing-overview/">Fairing</a>
<td><a href="/docs/external-add-ons/fairing/fairing-overview/">Fairing</a>
(<a href="https://github.com/kubeflow/fairing">GitHub</a>)
</td>
<td>Beta</td>
Expand Down
2 changes: 1 addition & 1 deletion content/en/docs/started/kubeflow-overview.md
Expand Up @@ -141,7 +141,7 @@ sets of reference documentation:
* [Pipelines reference docs](/docs/components/pipelines/reference/) for the Kubeflow
Pipelines API and SDK, including the Kubeflow Pipelines domain-specific
language (DSL).
* [Fairing reference docs](/docs/components/fairing/reference/) for the Kubeflow Fairing
* [Fairing reference docs](/docs/external-add-ons/fairing/reference/) for the Kubeflow Fairing
SDK.

## Next steps
Expand Down

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