Install Kubeflow Pipelines

Kubeflow is an open-source platform for machine learning (ML) that enables teams to easily create, deploy, and manage scalable ML workflows on Kubernetes. Kubeflow Pipelines is a key component of the Kubeflow platform that allows users to build and run reusable ML workflows. In this article, we will walk you through the process of installing Kubeflow Pipelines on your Kubernetes cluster.

Before we begin, it's important to note that you will need a Kubernetes cluster to install Kubeflow Pipelines. If you don't have a Kubernetes cluster yet, you can use a cloud provider like Google Cloud Platform (GCP) or Amazon Web Services (AWS) to create one.

1. Install Kubeflow Pipelines CLI

The first step to installing Kubeflow Pipelines is to install the Kubeflow Pipelines CLI. This command-line interface (CLI) tool allows you to interact with your Kubeflow Pipelines installation.

To install the Kubeflow Pipelines CLI, run the following command:

pip install kfp --upgrade --user

2. Install the Kubeflow Pipelines SDK

Next, you will need to install the Kubeflow Pipelines SDK. This SDK provides a set of Python packages that allow you to define and run Kubeflow Pipelines workflows.

To install the Kubeflow Pipelines SDK, run the following command:

pip install kfp-sdk --upgrade --user

3. Create a Kubeflow Pipelines namespace

Before you can install Kubeflow Pipelines, you will need to create a namespace for it in your Kubernetes cluster. To create a namespace called kubeflow, run the following command:

kubectl create namespace kubeflow

4. Install Kubeflow Pipelines

Now that you have the Kubeflow Pipelines CLI and SDK installed, and a namespace created, you can install Kubeflow Pipelines on your Kubernetes cluster.

To install Kubeflow Pipelines, run the following command:

export NAMESPACE=kubeflow
export VERSION=1.7.0
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/cluster-scoped-resources?ref=$VERSION"
kubectl wait --for condition=established --timeout=60s crd/applications.app.k8s.io
kubectl apply -k "github.com/kubeflow/pipelines/manifests/kustomize/env/platform-agnostic?ref=$VERSION"
kubectl wait --for condition=established --timeout=60s deployments/argo-ui -n $NAMESPACE

This command will install Kubeflow Pipelines version 1.7.0 in the kubeflow namespace.

5. Verify the installation

To verify that Kubeflow Pipelines has been installed successfully, run the following command:

kubectl get pods -n kubeflow

This command will display a list of all the running pods in the kubeflow namespace. You should see several pods with names starting with ml-pipeline-.

Congratulations! You have successfully installed Kubeflow Pipelines on your Kubernetes cluster.

Kubeflow Pipelines is a powerful tool for creating, deploying, and managing ML workflows on Kubernetes. In this article, we have walked you through the process of installing Kubeflow Pipelines on your Kubernetes cluster. By following the steps outlined in this article, you can easily get started with building and running scalable ML workflows using Kubeflow Pipelines.

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That's it for this post. Keep practicing and have fun. Leave your comments if any.