Kubeflow – Installing Pipelines

Docs: Installation Options for Kubeflow Pipelines

Mon, 01 Jan 0001 00:00:00 +0000

Kubeflow Pipelines offers a few installation options. This page describes the options and the features available with each option:

Kubeflow Pipelines Standalone is the minimal portable installation that only includes Kubeflow Pipelines.
Kubeflow Pipelines as part of a full Kubeflow deployment provides all Kubeflow components and more integration with each platform.
Beta: 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.
A local Kubeflow Pipelines deployment for testing purposes.

Choosing an installation option

Do you want to use other Kubeflow components in addition to Pipelines?

If yes, choose the full Kubeflow deployment.
Do you want to use Kubeflow Pipelines with multi-user support?

If yes, choose the full Kubeflow deployment with version >= v1.1.
Do you deploy on Google Cloud?

If yes, deploy Kubeflow Pipelines Standalone. You can also use Google Cloud AI Platform Pipelines to deploy Kubeflow Pipelines using a user interface, but there are limitations in customizability and upgradability. For details, please read corresponding sections.
You deploy on other platforms.

Please compare your platform specific full Kubeflow with the Kubeflow Pipelines Standalone before making your decision.

Warning: Choose your installation option with caution, there’s no current supported path to migrate data between different installation options. Please create a GitHub issue if that’s important for you.

Kubeflow Pipelines Standalone

Use this option to deploy Kubeflow Pipelines to an on-premises, cloud or even local Kubernetes cluster, without the other components of Kubeflow. To deploy Kubeflow Pipelines Standalone, you use kustomize manifests only. This process makes it simpler to customize your deployment and to integrate Kubeflow Pipelines into an existing Kubernetes cluster.

Installation guide: Kubeflow Pipelines Standalone deployment guide

Interfaces :

Kubeflow Pipelines UI
Kubeflow Pipelines SDK
Kubeflow Pipelines API
Kubeflow Pipelines endpoint is only auto-configured for Google Cloud.

If you wish to deploy Kubeflow Pipelines on other platforms, you can either access it through kubectl port-forward or configure your own platform specific auth-enabled endpoint by yourself.

Release Schedule: Kubeflow Pipelines Standalone is available for every Kubeflow Pipelines release. You will have access to the latest features.
Upgrade Support (Beta): Upgrading Kubeflow Pipelines Standalone introduces how to upgrade in place.

Google Cloud Integrations :

A Kubeflow Pipelines public endpoint with auth support is auto-configured for you.
Open the Kubeflow Pipelines UI via the Open Pipelines Dashboard link in the AI Platform Pipelines dashboard of Cloud Console.
(Optional) You can choose to persist your data in Google Cloud managed storage (Cloud SQL and Cloud Storage).
All options to authenticate to Google Cloud are supported.

Notes on specific features :

After deployment, your Kubernetes cluster contains Kubeflow Pipelines only. It does not include the other Kubeflow components. For example, to use a Jupyter Notebook, you must use a local notebook or a hosted notebook in a cloud service such as the AI Platform Notebooks.
Kubeflow Pipelines multi-user support is not available in standalone, because multi-user support depends on other Kubeflow components.

Full Kubeflow deployment

Use this option to deploy Kubeflow Pipelines to your local machine, on-premises, or to a cloud, as part of a full Kubeflow installation.

Installation guide: Kubeflow installation guide

Interfaces :

Kubeflow UI
Kubeflow Pipelines UI within or outside the Kubeflow UI
Kubeflow Pipelines SDK
Kubeflow Pipelines API
Other Kubeflow APIs
Kubeflow Pipelines endpoint is auto-configured with auth support for each platform

Release Schedule: The full Kubeflow is released quarterly. It has significant delay in receiving Kubeflow Pipelines updates.

Kubeflow Version	Kubeflow Pipelines Version
0.7.0	0.1.31
1.0.0	0.2.0
1.0.2	0.2.5
1.1.0	1.0.0* (see note below)

Note: Google Cloud, AWS, and IBM Cloud have supported Kubeflow Pipelines 1.0.0 with multi-user separation. Other platforms might not be up-to-date for now, refer to this GitHub issue for status.

Upgrade Support : Refer to the full Kubeflow section of upgrading Kubeflow Pipelines on Google Cloud guide.

Google Cloud Integrations :

A Kubeflow Pipelines public endpoint with auth support is auto-configured for you using Cloud Identity-Aware Proxy.
There’s no current support for persisting your data in Google Cloud managed storage (Cloud SQL and Cloud Storage). Refer to this GitHub issue for the latest status.
You can authenticate to Google Cloud with Workload Identity.

Notes on specific features :

After deployment, your Kubernetes cluster includes all the Kubeflow components. For example, you can use the Jupyter notebook services deployed with Kubeflow to create one or more notebook servers in your Kubeflow cluster.
Kubeflow Pipelines multi-user support is only available in full Kubeflow. It supports using a single Kubeflow Pipelines control plane to orchestrate user pipeline runs in multiple user namespaces with authorization.
Latest features and bug fixes may not be available soon because release cadence is long.

Google Cloud AI Platform Pipelines

Beta release

Google Cloud AI Platform Pipelines is currently in Beta with limited support. The Kubeflow Pipelines team is interested in any feedback you may have, in particular on the usability of the feature.

You can raise any issues or discussion items in the Kubeflow Pipelines issue tracker.

Use this option to deploy Kubeflow Pipelines to Google Kubernetes Engine (GKE) from Google Cloud Marketplace. You can deploy Kubeflow Pipelines to an existing or new GKE cluster and manage your cluster within Google Cloud.

Installation guide: Google Cloud AI Platform Pipelines documentation

Interfaces :

Google Cloud Console for managing the Kubeflow Pipelines cluster and other Google Cloud services
Kubeflow Pipelines UI via the Open Pipelines Dashboard link in the Google Cloud Console
Kubeflow Pipelines SDK in Cloud Notebooks
Kubeflow Pipelines endpoint of your instance is auto-configured for you

Release Schedule: AI Platform Pipelines is available for a chosen set of stable Kubeflow Pipelines releases. You will receive updates slightly slower than Kubeflow Pipelines Standalone.
Upgrade Support (Alpha): An in-place upgrade is not supported.

To upgrade AI Platform Pipelines by reinstalling it (with existing data), refer to the Upgrading AI Platform Pipelines guide.

Google Cloud Integrations :

You can deploy AI Platform Pipelines on Cloud Console UI.
A Kubeflow Pipelines public endpoint with auth support is auto-configured for you.
(Optional) You can choose to persist your data in Google Cloud managed storage services (Cloud SQL and Cloud Storage).
You can authenticate to Google Cloud with the Compute Engine default service account. However, this method may not be suitable if you need workload permission separation.

Notes on specific features :

After deployment, your Kubernetes cluster contains Kubeflow Pipelines only. It does not include the other Kubeflow components. For example, to use a Jupyter Notebook, you can use AI Platform Notebooks.
Kubeflow Pipelines multi-user support is not available in AI Platform Pipelines, because multi-user support depends on other Kubeflow components.

Docs: Kubeflow Pipelines Standalone Deployment

Mon, 01 Jan 0001 00:00:00 +0000

As an alternative to deploying Kubeflow Pipelines (KFP) as part of the Kubeflow deployment, you also have a choice to deploy only Kubeflow Pipelines. Follow the instructions below to deploy Kubeflow Pipelines standalone using the supplied kustomize manifests.

You should be familiar with Kubernetes, kubectl, and kustomize.

Installation options for Kubeflow Pipelines standalone

This guide currently describes how to install Kubeflow Pipelines standalone on Google Cloud Platform (GCP). You can also install Kubeflow Pipelines standalone on other platforms. This guide needs updating. See Issue 1253.

Before you get started

Working with Kubeflow Pipelines Standalone requires a Kubernetes cluster as well as an installation of kubectl.

Download and install kubectl

Download and install kubectl by following the kubectl installation guide.

You need kubectl version 1.14 or higher for native support of kustomize.

Set up your cluster

If you have an existing Kubernetes cluster, continue with the instructions for configuring kubectl to talk to your cluster.

See the GKE guide to creating a cluster for Google Cloud Platform (GCP).

Use the gcloud container clusters create command to create a cluster that can run all Kubeflow Pipelines samples:

# The following parameters can be customized based on your needs.

CLUSTER_NAME="kubeflow-pipelines-standalone"
ZONE="us-central1-a"
MACHINE_TYPE="n1-standard-2" # A machine with 2 CPUs and 7.50GB memory
SCOPES="cloud-platform" # This scope is needed for running some pipeline samples. Read the warning below for its security implication

gcloud container clusters create $CLUSTER_NAME \
     --zone $ZONE \
     --machine-type $MACHINE_TYPE \
     --scopes $SCOPES

Warning: Using SCOPES="cloud-platform" grants all GCP permissions to the cluster. For a more secure cluster setup, refer to Authenticating Pipelines to GCP.

Note, some legacy pipeline examples may need minor code change to run on clusters with SCOPES="cloud-platform", refer to Authoring Pipelines to use default service account.

References:

Configure kubectl to talk to your cluster

See the Google Kubernetes Engine (GKE) guide to configuring cluster access for kubectl.

Deploying Kubeflow Pipelines

Deploy the Kubeflow Pipelines:

export PIPELINE_VERSION=1.0.4
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"

The Kubeflow Pipelines deployment requires approximately 3 minutes to complete.

Note: The above commands apply to Kubeflow Pipelines version 0.4.0 and higher.

For Kubeflow Pipelines version 0.2.0 ~ 0.3.0, use:

export PIPELINE_VERSION=<kfp-version-between-0.2.0-and-0.3.0>
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/base/crds?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"

For Kubeflow Pipelines version < 0.2.0, use:

export PIPELINE_VERSION=<kfp-version-0.1.x>
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"

Note: kubectl apply -k accepts local paths and paths that are formatted as hashicorp/go-getter URLs. While the paths in the preceding commands look like URLs, the paths are not valid URLs.

Get the public URL for the Kubeflow Pipelines UI and use it to access the Kubeflow Pipelines UI:
```
kubectl describe configmap inverse-proxy-config -n kubeflow | grep googleusercontent.com
```

Upgrading Kubeflow Pipelines

Check the Kubeflow Pipelines GitHub repository for available releases.

To upgrade to Kubeflow Pipelines 0.4.0 and higher, use the following commands:

export PIPELINE_VERSION=<version-you-want-to-upgrade-to>
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"

To upgrade to Kubeflow Pipelines 0.3.0 and lower, use the deployment instructions to upgrade your Kubeflow Pipelines cluster.

Delete obsolete resources manually.

Depending on the version you are upgrading from and the version you are upgrading to, some Kubeflow Pipelines resources may have become obsolete.

If you are upgrading from Kubeflow Pipelines < 0.4.0 to 0.4.0 or above, you can remove the following obsolete resources after the upgrade: metadata-deployment, metadata-service.

Run the following command to check if these resources exist on your cluster:
```
kubectl -n <KFP_NAMESPACE> get deployments | grep metadata-deployment
kubectl -n <KFP_NAMESPACE> get service | grep metadata-service
```
If these resources exist on your cluster, run the following commands to delete them:
```
kubectl -n <KFP_NAMESPACE> delete deployment metadata-deployment
kubectl -n <KFP_NAMESPACE> delete service metadata-service
```
For other versions, you don’t need to do anything.

Customizing Kubeflow Pipelines

Kubeflow Pipelines can be configured through kustomize overlays.

To begin, first clone the Kubeflow Pipelines GitHub repository, and use it as your working directory.

Deploy on GCP with CloudSQL and Google Cloud Storage

Note: This is recommended for production environments. For more details about customizing your environment for GCP, see the Kubeflow Pipelines GCP manifests.

Change deployment namespace

To deploy Kubeflow Pipelines standalone in namespace <my-namespace>:

Set the namespace field to <my-namespace> in dev/kustomization.yaml or gcp/kustomization.yaml.
Apply the changes to update the Kubeflow Pipelines deployment:
```
kubectl apply -k manifests/kustomize/env/dev
```
Note: If using GCP Cloud SQL and Google Cloud Storage, apply with this command:
```
kubectl apply -k manifests/kustomize/env/gcp
```

Disable the public endpoint

By default, the KFP standalone deployment installs an inverting proxy agent that exposes a public URL. If you want to skip the installation of the inverting proxy agent, complete the following:

Comment out the proxy components in the base kustomization.yaml.
Apply the changes to update the Kubeflow Pipelines deployment:
```
kubectl apply -k manifests/kustomize/env/dev
```
Note: If using GCP Cloud SQL and Google Cloud Storage, apply with this command:
```
kubectl apply -k manifests/kustomize/env/gcp
```
Verify that the Kubeflow Pipelines UI is accessible by port-forwarding:
```
kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80
```
Open the Kubeflow Pipelines UI at http://localhost:8080/.

Uninstalling Kubeflow Pipelines

To uninstall Kubeflow Pipelines, run kubectl delete -k <manifest-file>.

For example, to uninstall KFP using manifests from a GitHub repository, run:

export PIPELINE_VERSION=1.0.4
kubectl delete -k "github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=$PIPELINE_VERSION"
kubectl delete -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"

To uninstall KFP using manifests from your local repository or file system, run:

kubectl delete -k manifests/kustomize/env/dev
kubectl delete -k manifests/kustomize/cluster-scoped-resources

Note: If you are using GCP Cloud SQL and Google Cloud Storage, run:

kubectl delete -k manifests/kustomize/env/gcp
kubectl delete -k manifests/kustomize/cluster-scoped-resources

Best practices for maintaining manifests

Similar to source code, configuration files belong in source control. A repository manages the changes to your manifest files and ensures that you can repeatedly deploy, upgrade, and uninstall your components.

Maintain your manifests in source control

After creating or customizing your deployment manifests, save your manifests to a local or remote source control respository. For example, save the following kustomization.yaml:

# kustomization.yaml
apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
# Edit the following to change the deployment to your custom namespace.
namespace: kubeflow
# You can add other customizations here using kustomize.
# Edit ref in the following link to deploy a different version of Kubeflow Pipelines.
bases:
- github.com/kubeflow/pipelines/manifests/kustomize/env/dev?ref=1.0.4

Troubleshooting

If your pipelines are stuck in ContainerCreating state and it has pod events like

MountVolume.SetUp failed for volume "gcp-credentials-user-gcp-sa" : secret "user-gcp-sa" not found

You should remove use_gcp_secret usages as documented in Authenticating Pipelines to GCP.

What’s next

Connecting to Kubeflow Pipelines standalone on Google Cloud using the SDK

Docs: Deploying Kubeflow Pipelines on a local cluster

Mon, 01 Jan 0001 00:00:00 +0000

This guide shows how to deploy Kubeflow Pipelines standalone on a local Kubernetes cluster using:

kind
K3s
K3s on Windows Subsystem for Linux (WSL)
K3ai [alpha]

Such deployment methods can be part of your local environment using the supplied kustomize manifests for test purposes. This guide is an alternative to

Deploying Kubeflow Pipelines (KFP).

Before you get started

You should be familiar with Kubernetes, kubectl, and kustomize.
For native support of kustomize, you will need kubectl v1.14 or higher. You can download and install kubectl by following the kubectl installation guide.

kind

1. Installing kind

kind is a tool for running local Kubernetes clusters using Docker container nodes. kind was primarily designed for testing Kubernetes itself. It can also be used for local development or CI.

You can install and configure kind by following the official quick start.

To get started with kind:

On Linux:

Download and move the kind executable to your directory in your PATH by running the following commands:

curl -Lo ./kind https://kind.sigs.k8s.io/dl/{KIND_VERSION}/kind-linux-amd64 && \
chmod +x ./kind && \
mv ./kind /{YOUR_KIND_DIRECTORY}/kind

Replace the following:

{KIND_VERSION}: the kind version; for example, v0.8.1 as of the date this guide was written
{YOUR_KIND_DIRECTORY}: your directory in PATH

On macOS:

You can use Homebrew to install kind:

brew install kind`

On Windows:

You can use the administrative PowerShell console to run the following commands to download and move the kind executable to a directory in your PATH:
PowerShell: Run these commands to download and move the kind executable to a directory in your PATH:
```
curl.exe -Lo kind-windows-amd64.exe https://kind.sigs.k8s.io/dl/{KIND_VERSION}/kind-windows-amd64
Move-Item .\kind-windows-amd64.exe c:\{YOUR_KIND_DIRECTORY}\kind.exe
```
Replace the following:
- {KIND_VERSION}: the kind version - for example, v0.9 (check the latest stable binary versions on the kind releases pages)
- {YOUR_KIND_DIRECTORY}: your directory for kind in PATH

{KIND_VERSION}: the kind version; for example, v0.8.1 as of the date this guide was written
{YOUR_KIND_DIRECTORY}: your directory in PATH

Alternatively, you can use Chocolatey https://chocolatey.org/packages/kind:
```
choco install kind
```

Note: kind uses containerd as a default container-runtime hence you cannot use the standard kubeflow pipeline manifests.

References:

References:

2. Creating a cluster on kind

Having installed kind, you can create a Kubernetes cluster on kind with this command:

kind create cluster

This will bootstrap a Kubernetes cluster using a pre-built node image. You can find that image on the Docker Hub kindest/node here. If you wish to build the node image yourself, you can use the kind build node-image command—see the official building image section for more details. And, to specify another image, use the --image flag.

By default, the cluster will be given the name kind. Use the --name flag to assign the cluster a different context name.

K3s

1. Setting up a cluster on K3s

K3s is a fully compliant Kubernetes distribution with the following enhancements:

Packaged as a single binary.
Lightweight storage backend based on sqlite3 as the default storage mechanism. etcd3, MySQL, Postgres also still available.
Wrapped in simple launcher that handles a lot of the complexity of TLS and options.
Secure by default with reasonable defaults for lightweight environments.
Simple but powerful “batteries-included” features have been added, such as: a local storage provider, a service load balancer, a Helm controller, and the Traefik ingress controller.
Operation of all Kubernetes control plane components is encapsulated in a single binary and process. This allows K3s to automate and manage complex cluster operations like distributing certificates.
External dependencies have been minimized (just a modern kernel and cgroup mounts needed). K3s packages required dependencies, including:
- containerd
- Flannel
- CoreDNS
- CNI
- Host utilities (iptables, socat, etc)
- Ingress controller (traefik)
- Embedded service loadbalancer
- Embedded network policy controller

You can find the the official K3s installation script to install it as a service on systemd- or openrc-based systems on the official K3s website.

To install K3s using that method, run the following command:

curl -sfL https://get.k3s.io | sh -

References:

2. Creating a cluster on K3s

To create a Kubernetes cluster on K3s, use the following command:
```
sudo k3s server &
```
This will bootstrap a Kubernetes cluster kubeconfig is written to /etc/rancher/k3s/k3s.yaml.
```
sudo k3s kubectl get node
```
(Optional) Check your cluster:
```
sudo k3s kubectl get node
```
K3s embeds the popular kubectl command directly in the binaries, so you may immediately interact with the cluster through it.
(Optional) Run the below command on a different node. NODE_TOKEN comes from /var/lib/rancher/k3s/server/node-token on your server:
```
sudo k3s agent --server https://myserver:6443 --token {YOUR_NODE_TOKEN}
```

K3s on Windows Subsystem for Linux (WSL)

1. Setting up a cluster on K3s on Windows Subsystem for Linux (WSL)

The Windows Subsystem for Linux (WSL) lets developers run a GNU/Linux environment—including most command-line tools, utilities, and applications— directly on Windows, unmodified, without the overhead of a traditional virtual machine or dualboot setup.

The full instructions for installing WSL can be found on the official Windows site.

The following steps summarize what you’ll need to set up WSL and then K3s on WSL.

Install [WSL] by following the official docs.
As per the official instructions, update WSL and download your preferred distibution:

References:

K3s on WSL: Quick Start Guide

2. Creating a cluster on K3s on WSL

Below are the steps to create a cluster on K3s in WSL

To create a Kubernetes cluster on K3s on WSL, run the following command:
```
sudo ./k3s server
```
This will bootstrap a Kubernetes cluster but you will cannot yet access from your Windows machine to the cluster itself.

Note: You can’t install K3s using the curl script because there is no supervisor (systemd or openrc) in WSL.
Download the K3s binary from https://github.com/rancher/k3s/releases/latest. Then, inside the directory where you download the K3s binary to, run this command to add execute permission to the K3s binary:
```
chmod +x k3s
```
Start K3s:
```
sudo ./k3s server
```

3. Setting up access to WSL instance

To set up access to your WSL instance:

Copy /etc/rancher/k3s/k3s.yaml from WSL to $HOME/.kube/config.
Edit the copied file by changing the server URL from https://localhost:6443 to the IP of the your WSL instance (ip addr show dev eth0) (For example, https://192.168.170.170:6443.)
Run kubectl in a Windows terminal. If you don’t kubectl installed, follow the official Kubernetes on Windows instructions.

K3ai [alpha]

K3ai is a lightweight “infrastructure in a box” designed specifically to install and configure AI tools and platforms on portable hardware, such as laptops and edge devices. This enables users to perform quick experimentations with Kubeflow on a local cluster.

K3ai’s main goal is to provide a quick way to install Kubernetes (K3s-based) and Kubeflow Pipelines with NVIDIA GPU support and TensorFlow Serving with just one line. (For Kubeflow and other component support, check K3ai’s website for updates.) To install Kubeflow Pipelines using K3ai, run the following commands:

With CPU-only support:

curl -sfL https://get.k3ai.in | bash -s -- --cpu --plugin_kfpipelines

With GPU support:

curl -sfL https://get.k3ai.in | bash -s -- --gpu --plugin_kfpipelines

For more information about K3ai, refer to the official documentation.

Deploying Kubeflow Pipelines

The installation process for Kubeflow Pipelines is the same for all three environments covered in this guide: kind, K3s, and K3ai.

Note: Process Namespace Sharing (PNS) is not mature in Argo yet - for more information go to Argo Executors and reference “pns executors” in any issue you may come across when using PNS.

To deploy the Kubeflow Pipelines, run the following commands:

# env/platform-agnostic-pns hasn't been publically released, so you will install it from master
export PIPELINE_VERSION=1.0.4
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION"

The Kubeflow Pipelines deployment may take several minutes to complete.

Verify that the Kubeflow Pipelines UI is accessible by port-forwarding:
```
kubectl port-forward -n kubeflow svc/ml-pipeline-ui 8080:80
```
Then, open the Kubeflow Pipelines UI at http://localhost:8080/ or - if you are using kind or K3s within a virtual machine - http://{YOUR_VM_IP_ADDRESS}:8080/

Note that K3ai will automatically print the URL for the web UI at the end of the installation process.

Note: kubectl apply -k accepts local paths and paths that are formatted as hashicorp/go-getter URLs. While the paths in the preceding commands look like URLs, they are not valid URLs.

Uninstalling Kubeflow Pipelines

Below are the steps to remove Kubeflow Pipelines on kind, K3s, or K3ai:

To uninstall Kubeflow Pipelines using your manifest file, run the following command, replacing {YOUR_MANIFEST_FILE} with the name of your manifest file:
```
kubectl delete -k {YOUR_MANIFEST_FILE}`
```

To uninstall Kubeflow Pipelines using manifests from Kubeflow Pipelines’s GitHub repository, run these commands:

export PIPELINE_VERSION=1.0.4
kubectl delete -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic-pns?ref=$PIPELINE_VERSION"
kubectl delete -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$PIPELINE_VERSION"

To uninstall Kubeflow Pipelines using manifests from your local repository or file system, run the following commands:

kubectl delete -k manifests/kustomize/env/platform-agnostic-pns
kubectl delete -k manifests/kustomize/cluster-scoped-resources

Kubeflow – Installing Pipelines

Docs: Installation Options for Kubeflow Pipelines

Choosing an installation option

Kubeflow Pipelines Standalone

Full Kubeflow deployment

Google Cloud AI Platform Pipelines

Beta release

Docs: Kubeflow Pipelines Standalone Deployment

Installation options for Kubeflow Pipelines standalone

Before you get started

Download and install kubectl

Set up your cluster

Configure kubectl to talk to your cluster

Deploying Kubeflow Pipelines

Upgrading Kubeflow Pipelines

Customizing Kubeflow Pipelines

Deploy on GCP with CloudSQL and Google Cloud Storage

Change deployment namespace

Disable the public endpoint

Uninstalling Kubeflow Pipelines

Best practices for maintaining manifests

Maintain your manifests in source control

Further reading

Troubleshooting

What’s next

Docs: Deploying Kubeflow Pipelines on a local cluster

Before you get started

kind

1. Installing kind

2. Creating a cluster on kind

K3s

1. Setting up a cluster on K3s

2. Creating a cluster on K3s

K3s on Windows Subsystem for Linux (WSL)

1. Setting up a cluster on K3s on Windows Subsystem for Linux (WSL)

2. Creating a cluster on K3s on WSL

3. Setting up access to WSL instance

K3ai [alpha]

Deploying Kubeflow Pipelines

Uninstalling Kubeflow Pipelines