Move Kubeflow Pipelines under /components (#2505)

* Remove existing pipelines redirect

* mv /pipelines to under /components

* Fix all links poining to /pipelines -» /components/pipelines

* Fix redirects src + add base redirect

* Updating redirects + adding pipelines catch all

* Moved Catch-all redirects to bottom of file

* Update "weight" so that Pipelines is right after notebooks

content/en/_index.html

@@ -111,7 +111,7 @@ <h5 class="card-title text-white section-head">Model serving</h5>
         <div class="card-body bg-primary-dark">
           <h5 class="card-title text-white section-head">Pipelines</h5>
           <p class="card-text text-white">
-			<a target="_blank" rel="noopener" href="/docs/pipelines/overview/pipelines-overview/" >Kubeflow Pipelines</a> is a comprehensive solution for deploying
+			<a target="_blank" rel="noopener" href="/docs/components/pipelines/overview/pipelines-overview/" >Kubeflow Pipelines</a> is a comprehensive solution for deploying
             and managing end-to-end ML workflows. Use Kubeflow Pipelines for rapid and reliable experimentation.
             You can schedule and compare runs, and examine detailed reports on each run.
           </p>

content/en/_redirects

@@ -13,36 +13,33 @@
 /blog/announcing_kubeflow_0.2/         https://medium.com/kubeflow/kubeflow-0-2-offers-new-components-and-simplified-setup-735e4c56988d
 
 /docs/guides/components/components     /docs/components/
-/docs/guides/pipelines/deploy-pipelines-service/  /docs/pipelines/pipelines-quickstart/
-
-# Removed the "guides" section from the doc URLs.
-/docs/guides/*  /docs/:splat
+/docs/guides/pipelines/deploy-pipelines-service/  /docs/components/pipelines/pipelines-quickstart/
 
 # Merged duplicate page pipelines-samples.md into build-pipeline.md
-/docs/pipelines/pipelines-samples/	   /docs/pipelines/build-pipeline/
+/docs/pipelines/pipelines-samples/	   /docs/components/pipelines/build-pipeline/
 
 # Removed redundant UI guide. Quickstart is a better destination.
-/docs/pipelines/pipelines-ui/	         /docs/pipelines/pipelines-quickstart/
+/docs/pipelines/pipelines-ui/	         /docs/components/pipelines/pipelines-quickstart/
 
 # Restructured the pipelines docs.
-/docs/pipelines/output-viewer/                      /docs/pipelines/sdk/output-viewer/
-/docs/pipelines/pipelines-metrics/                  /docs/pipelines/sdk/pipelines-metrics/
-/docs/pipelines/build-component/                    /docs/pipelines/sdk/build-component/
-/docs/pipelines/install-sdk/                        /docs/pipelines/sdk/install-sdk/
-/docs/pipelines/lightweight-python-components/      /docs/pipelines/sdk/python-function-components/
-/docs/pipelines/sdk/lightweight-python-components/  /docs/pipelines/sdk/python-function-components/
-/docs/pipelines/build-pipeline/                     /docs/pipelines/tutorials/build-pipeline/
-/docs/pipelines/pipelines-tutorial/                 /docs/pipelines/tutorials/cloud-tutorials/
-/docs/pipelines/tutorials/pipelines-tutorial/       /docs/pipelines/tutorials/cloud-tutorials/
-/docs/gke/pipelines-tutorial/                       /docs/pipelines/tutorials/cloud-tutorials/
-/docs/gke/pipelines/pipelines-tutorial/             /docs/pipelines/tutorials/cloud-tutorials/
+/docs/pipelines/                                    /docs/components/pipelines
+/docs/pipelines/output-viewer/                      /docs/components/pipelines/sdk/output-viewer/
+/docs/pipelines/pipelines-metrics/                  /docs/components/pipelines/sdk/pipelines-metrics/
+/docs/pipelines/build-component/                    /docs/components/pipelines/sdk/build-component/
+/docs/pipelines/install-sdk/                        /docs/components/pipelines/sdk/install-sdk/
+/docs/pipelines/lightweight-python-components/      /docs/components/pipelines/sdk/python-function-components/
+/docs/pipelines/sdk/lightweight-python-components/  /docs/components/pipelines/sdk/python-function-components/
+/docs/pipelines/build-pipeline/                     /docs/components/pipelines/tutorials/build-pipeline/
+/docs/pipelines/pipelines-tutorial/                 /docs/components/pipelines/tutorials/cloud-tutorials/
+/docs/pipelines/tutorials/pipelines-tutorial/       /docs/components/pipelines/tutorials/cloud-tutorials/
+/docs/gke/pipelines-tutorial/                       /docs/components/pipelines/tutorials/cloud-tutorials/
+/docs/gke/pipelines/pipelines-tutorial/             /docs/components/pipelines/tutorials/cloud-tutorials/
 /docs/gke/authentication-pipelines/                 /docs/gke/pipelines/authentication-pipelines/
 
-/docs/pipelines/metrics/                       /docs/pipelines/sdk/pipelines-metrics/
-/docs/pipelines/metrics/pipelines-metrics/     /docs/pipelines/sdk/pipelines-metrics/
-/docs/pipelines/metrics/output-viewer/         /docs/pipelines/sdk/output-viewer/
-/docs/pipelines/concepts/*                     /docs/pipelines/overview/concepts/:splat
-/docs/pipelines/pipelines-overview/            /docs/pipelines/overview/pipelines-overview/
+/docs/pipelines/metrics/                       /docs/components/pipelines/sdk/pipelines-metrics/
+/docs/pipelines/metrics/pipelines-metrics/     /docs/components/pipelines/sdk/pipelines-metrics/
+/docs/pipelines/metrics/output-viewer/         /docs/components/pipelines/sdk/output-viewer/
+/docs/pipelines/pipelines-overview/            /docs/components/pipelines/overview/pipelines-overview/
 /docs/pipelines/enable-gpu-and-tpu/            /docs/gke/pipelines/enable-gpu-and-tpu/
 /docs/pipelines/sdk/enable-gpu-and-tpu/        /docs/gke/pipelines/enable-gpu-and-tpu/
 /docs/pipelines/sdk/gcp/enable-gpu-and-tpu/    /docs/gke/pipelines/enable-gpu-and-tpu/
@@ -55,13 +52,13 @@
 /docs/other-guides/monitoring/                 /docs/gke/monitoring/
 
 # Created a new section for pipeline concepts.
-/docs/pipelines/pipelines-concepts/            /docs/pipelines/concepts/
+/docs/pipelines/pipelines-concepts/            /docs/components/pipelines/concepts/
 
 # Replaces Pipelines DSL overview with SDK overview
-/docs/pipelines/sdk/dsl-overview/              /docs/pipelines/sdk/sdk-overview/
+/docs/pipelines/sdk/dsl-overview/              /docs/components/pipelines/sdk/sdk-overview/
 
 # Created a new section for pipelines installation.
-/docs/pipelines/standalone-deployment-gcp/    /docs/pipelines/installation/standalone-deployment/
+/docs/pipelines/standalone-deployment-gcp/    /docs/components/pipelines/installation/standalone-deployment/
 
 # Removed the downloads page from Reference to Getting Started with Kubeflow
 /docs/reference/downloads/                     /docs/started/getting-started/
@@ -118,3 +115,11 @@ docs/started/requirements/                    /docs/started/getting-started/
 
 # Rename TensorRT Inference Server to Triton Inference Server
 /docs/components/serving/trtinferenceserver   /docs/components/serving/tritoninferenceserver
+
+# ===============
+# IMPORTANT NOTE: 
+# Catch-all redirects should be added at the end of this file as redirects happen from top to bottom
+# ===============
+/docs/guides/*  /docs/:splat
+/docs/pipelines/*                                   /docs/components/pipelines/:splat
+/docs/pipelines/concepts/*                     /docs/components/pipelines/overview/concepts/:splat

content/en/docs/about/use-cases.md

@@ -91,6 +91,6 @@ See these docs for more information on the topics covered above:
 
 - [Hyperparameter tuning with Katib](/docs/components/katib/)
 - [Training models with operators](/docs/components/training/)
-- [Get started with Pipelines](https://www.kubeflow.org/docs/pipelines/)
+- [Get started with Pipelines](https://www.kubeflow.org/docs/components/pipelines/)
 - [Jupyter notebooks](/docs/components/notebooks/)
 - [Kubeflow roadmap](http://bit.ly/kf_roadmap)

content/en/docs/aws/pipeline.md

@@ -183,7 +183,7 @@ Here's an example.
 
 ## Support TensorBoard in Kubeflow Pipelines
 
- [TensorBoard](/docs/pipelines/sdk/output-viewer/#tensorboard) needs some extra settings on AWS like below:
+ [TensorBoard](/docs/components/pipelines/sdk/output-viewer/#tensorboard) needs some extra settings on AWS like below:
 
 1. Create a Kubernetes secret `aws-secret` in the `kubeflow` namespace. Follow instructions [here](#s3-access-from-kubeflow-pipelines).
 

content/en/docs/azure/authentication-oidc.md

@@ -214,7 +214,7 @@ This section shows the how to set up Kubeflow with authentication and authorizat
 
    Navigate to `https://<YOUR_LOADBALANCER_IP_ADDRESS_OR_DNS_NAME>/` and start using Kubeflow.
 
-## Authenticate Kubeflow pipelines using [Kubeflow Pipelines SDK](https://www.kubeflow.org/docs/pipelines/sdk/sdk-overview/)
+## Authenticate Kubeflow pipelines using [Kubeflow Pipelines SDK](https://www.kubeflow.org/docs/components/pipelines/sdk/sdk-overview/)
 
 Perform interactive login from browser by visitng `https://<YOUR_LOADBALANCER_IP_ADDRESS_OR_DNS_NAME>/` and copy the value of cookie `authservice_session` to authenticate using SDK with below code:
 

content/en/docs/azure/azureEndtoEnd.md

@@ -219,4 +219,4 @@ When you are done, make sure you delete your resource group to avoid extra charg
 You can optionally choose to delete individual resources on your clusters using the [Azure cluster docs](https://docs.microsoft.com/en-us/azure/service-fabric/service-fabric-tutorial-delete-cluster).
 
 ## Next steps
-Build your own pipeline using the [Kubeflow Pipelines SDK](/docs/pipelines/sdk/sdk-overview/).
+Build your own pipeline using the [Kubeflow Pipelines SDK](/docs/components/pipelines/sdk/sdk-overview/).

content/en/docs/components/feature-store/getting-started.md

@@ -23,9 +23,9 @@ Once Feast is installed within the same Kubernetes cluster as Kubeflow, users ca
 Feast APIs can roughly be grouped into the following sections:
 * __Feature definition and management__: Feast provides both a [Python SDK](https://docs.feast.dev/getting-started/connect-to-feast) and [CLI](https://docs.feast.dev/getting-started/connect-to-feast) for interacting with Feast Core. Feast Core allows users to define and register features and entities and their associated metadata and schemas. The Python SDK is typically used from within a Jupyter notebook by end users to administer Feast, but ML teams may opt to version control feature specifications in order to follow a GitOps based approach.
 
-* __Model training__: The Feast Python SDK can be used to trigger the [creation of training datasets](https://docs.feast.dev/user-guide/getting-training-features). The most natural place to use this SDK is to create a training dataset as part of a [Kubeflow Pipeline](/docs/pipelines/pipelines-overview) prior to model training.
+* __Model training__: The Feast Python SDK can be used to trigger the [creation of training datasets](https://docs.feast.dev/user-guide/getting-training-features). The most natural place to use this SDK is to create a training dataset as part of a [Kubeflow Pipeline](/docs/components/pipelines/pipelines-overview) prior to model training.
 
-* __Model serving__: Feast provides three different SDKs for [online feature serving](https://docs.feast.dev/user-guide/getting-online-features), a [Python SDK](https://api.docs.feast.dev/python/), [Java SDK](https://javadoc.io/doc/dev.feast/feast-sdk), and [Go SDK](https://godoc.org/github.com/feast-dev/feast/sdk/go). These clients are used prior to inference with [Model Serving](/docs/pipelines/pipelines-overview) systems like KFServing, TFX, or Seldon. 
+* __Model serving__: Feast provides three different SDKs for [online feature serving](https://docs.feast.dev/user-guide/getting-online-features), a [Python SDK](https://api.docs.feast.dev/python/), [Java SDK](https://javadoc.io/doc/dev.feast/feast-sdk), and [Go SDK](https://godoc.org/github.com/feast-dev/feast/sdk/go). These clients are used prior to inference with [Model Serving](/docs/components/pipelines/pipelines-overview) systems like KFServing, TFX, or Seldon. 
 
 All of the above clients interact with Feast through gRPC endpoints ([Core](https://api.docs.feast.dev/grpc/feast.core.pb.html), [Serving](https://api.docs.feast.dev/grpc/feast.serving.pb.html)). These APIs allow users to directly interface with Feast services if they do not wish to use an SDK.
 

content/en/docs/components/metadata.md

@@ -1,7 +1,7 @@
 +++
 title = "Metadata"
 description = "Tracking and managing metadata of machine learning workflows in Kubeflow"
-weight = 15
+weight = 20
 +++
 
 {{% beta-status feedbacklink="https://github.com/kubeflow/metadata/issues" %}}

content/en/docs/components/multi-tenancy/overview.md

@@ -43,7 +43,7 @@ deployments) also inherit the same access.
 
 Kubeflow Pipelines is partially integrated with multi-user isolation starting
 from Kubeflow v1.1. You can find more information on [Multi-user Isolation for
-Pipelines](https://www.kubeflow.org/docs/pipelines/multi-user/).
+Pipelines](https://www.kubeflow.org/docs/components/pipelines/multi-user/).
 
 Metadata or any other applications currently don't have full
 fledged integration with isolation, though they do have access to the user

content/en/docs/components/notebooks/setup.md

@@ -304,7 +304,7 @@ 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/pipelines/sdk/sdk-overview/).
+  SDK](/docs/components/pipelines/sdk/sdk-overview/).
 * Explore [Kubeflow Fairing](/docs/components/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.

content/en/docs/components/pipelines/_index.md

@@ -1,5 +1,5 @@
 +++
-title = "Pipelines"
-description = "ML Pipelines in Kubeflow"
-weight = 70
+title = "Kubeflow Pipelines"
+description = "Documentation for Kubeflow Pipelines."
+weight = 15
 +++

content/en/docs/pipelines/caching.md → content/en/docs/components/pipelines/caching.md

@@ -15,7 +15,7 @@ Starting from Kubeflow Pipelines 0.4, Kubeflow Pipelines supports step caching c
 
 This guide tells you the basic concepts of Kubeflow Pipelines step caching and how to use it. 
 This guide assumes that you already have Kubeflow Pipelines installed or want to use standalone or GCP hosted deployment options in the [Kubeflow Pipelines deployment 
-guide](/docs/pipelines/installation/) to deploy Kubeflow Pipelines.
+guide](/docs/components/pipelines/installation/) to deploy Kubeflow Pipelines.
 
 ## What is step caching?
 

content/en/docs/pipelines/installation/overview.md → content/en/docs/components/pipelines/installation/overview.md

@@ -14,7 +14,7 @@ portable installation that only includes Kubeflow Pipelines.
 * Kubeflow Pipelines as [part of a full Kubeflow deployment](#full-kubeflow-deployment) provides
 all Kubeflow components and more integration with each platform.
 * **Beta**: [Google Cloud AI Platform Pipelines](#google-cloud-ai-platform-pipelines) makes it easier to install and use Kubeflow Pipelines on Google Cloud by providing a management UI on [Google Cloud Console](https://console.cloud.google.com/ai-platform/pipelines/clusters).
-* A [local](/docs/pipelines/installation/localcluster-deployment) Kubeflow Pipelines deployment for testing purposes.
+* A [local](/docs/components/pipelines/installation/localcluster-deployment) Kubeflow Pipelines deployment for testing purposes.
 
 ## Choosing an installation option
 
@@ -23,7 +23,7 @@ all Kubeflow components and more integration with each platform.
     If yes, choose the [full Kubeflow deployment](#full-kubeflow-deployment).
 1. Can you use a cloud/on-prem Kubernetes cluster?
 
-    If you can't, you should try using Kubeflow Pipelines on a local Kubernetes cluster for learning and testing purposes by following the steps in [Deploying Kubeflow Pipelines on a local cluster](/docs/pipelines/installation/localcluster-deployment).
+    If you can't, you should try using Kubeflow Pipelines on a local Kubernetes cluster for learning and testing purposes by following the steps in [Deploying Kubeflow Pipelines on a local cluster](/docs/components/pipelines/installation/localcluster-deployment).
 1. Do you want to use Kubeflow Pipelines with [multi-user support](https://github.com/kubeflow/pipelines/issues/1223)?
 
     If yes, choose the [full Kubeflow deployment](#full-kubeflow-deployment) with version >= v1.1.
@@ -56,7 +56,7 @@ Kubeflow Pipelines into an existing Kubernetes cluster.
 
 Installation guide
 : [Kubeflow Pipelines Standalone deployment
-  guide](/docs/pipelines/installation/standalone-deployment/)
+  guide](/docs/components/pipelines/installation/standalone-deployment/)
 
 Interfaces
 :
@@ -74,7 +74,7 @@ Release Schedule
 You will have access to the latest features.
 
 Upgrade Support (**Beta**)
-: [Upgrading Kubeflow Pipelines Standalone](/docs/pipelines/installation/standalone-deployment/#upgrading-kubeflow-pipelines) introduces how to upgrade
+: [Upgrading Kubeflow Pipelines Standalone](/docs/components/pipelines/installation/standalone-deployment/#upgrading-kubeflow-pipelines) introduces how to upgrade
 in place.
 
 Google Cloud Integrations

content/en/docs/pipelines/multi-user.md → content/en/docs/components/pipelines/multi-user.md

@@ -14,7 +14,7 @@ for the common Kubeflow multi-user operations including the following:
 * For Google Cloud: [In-cluster authentication to Google Cloud from Kubeflow](/docs/gke/authentication/#in-cluster-authentication)
 
 Note, Kubeflow Pipelines multi-user isolation is only supported in
-[the full Kubeflow deployment](/docs/pipelines/installation/overview/#full-kubeflow-deployment)
+[the full Kubeflow deployment](/docs/components/pipelines/installation/overview/#full-kubeflow-deployment)
 starting from Kubeflow v1.1 and **currently** on all platforms except OpenShift. For the latest status about platform support, refer to [kubeflow/manifests#1364](https://github.com/kubeflow/manifests/issues/1364#issuecomment-668415871).
 
 Also be aware that the isolation support in Kubeflow doesn’t provide any hard
@@ -108,7 +108,7 @@ Detailed documentation for the Kubeflow Pipelines SDK can be found in the
 
 ### When using REST API or generated python API client
 
-Similarly, when calling [REST API endpoints](/docs/pipelines/reference/api/kubeflow-pipeline-api-spec/)
+Similarly, when calling [REST API endpoints](/docs/components/pipelines/reference/api/kubeflow-pipeline-api-spec/)
 or using [the generated python API client](https://kubeflow-pipelines.readthedocs.io/en/stable/source/kfp.server_api.html),
 namespace argument is required for experiment APIs. Note that namespace is
 referred to using a resource reference. The resource reference **type** is

content/en/docs/pipelines/overview/concepts/component.md → content/en/docs/components/pipelines/overview/concepts/component.md

@@ -41,7 +41,7 @@ Kubeflow Pipelines system. A component definition has the following parts:
   component has finished running.
 
 For the complete definition of a component, see the
-[component specification](/docs/pipelines/reference/component-spec/).
+[component specification](/docs/components/pipelines/reference/component-spec/).
 
 ## Containerizing components
 
@@ -57,11 +57,11 @@ deserialize the data for use in the downstream component.
 
 ## Next steps
 
-* Read an [overview of Kubeflow Pipelines](/docs/pipelines/pipelines-overview/).
-* Follow the [pipelines quickstart guide](/docs/pipelines/pipelines-quickstart/) 
+* Read an [overview of Kubeflow Pipelines](/docs/components/pipelines/pipelines-overview/).
+* Follow the [pipelines quickstart guide](/docs/components/pipelines/pipelines-quickstart/) 
   to deploy Kubeflow and run a sample pipeline directly from the Kubeflow 
   Pipelines UI.
 * Build your own 
-  [component and pipeline](/docs/pipelines/sdk/build-component/).
-* Build a [reusable component](/docs/pipelines/sdk/component-development/) for
+  [component and pipeline](/docs/components/pipelines/sdk/build-component/).
+* Build a [reusable component](/docs/components/pipelines/sdk/component-development/) for
   sharing in multiple pipelines.

content/en/docs/pipelines/overview/concepts/experiment.md → content/en/docs/components/pipelines/overview/concepts/experiment.md

@@ -8,11 +8,11 @@ weight = 40
 An *experiment* is a workspace where you can try different configurations of
 your pipelines. You can use experiments to organize your runs into logical
 groups. Experiments can contain arbitrary runs, including 
-[recurring runs](/docs/pipelines/concepts/run#recurring-run).
+[recurring runs](/docs/components/pipelines/concepts/run#recurring-run).
 
 ## Next steps
 
-* Read an [overview of Kubeflow Pipelines](/docs/pipelines/pipelines-overview/).
-* Follow the [pipelines quickstart guide](/docs/pipelines/pipelines-quickstart/) 
+* Read an [overview of Kubeflow Pipelines](/docs/components/pipelines/pipelines-overview/).
+* Follow the [pipelines quickstart guide](/docs/components/pipelines/pipelines-quickstart/) 
   to deploy Kubeflow and run a sample pipeline directly from the Kubeflow 
   Pipelines UI.