Vanilla Installation

Deploy kubeflow into an AKS cluster using default settings.

Background

In this lab, you will use the Azure CLI to deploy an Azure Kubernetes Service (AKS) Automatic cluster. AKS Automatic offers a simplified, managed Kubernetes experience with automated node management, scaling, and security configurations. For more details, see the AKS Automatic documentation. Note that AKS Automatic is currently in preview, while it provides faster setup and less manual configuration, it is not recommended for production use. For production workloads or when advanced features and customization are required, use regular AKS instead. You will then install Kubeflow using the default settings using Kustomize and create a jupyter notebook server you can easily access on your browser.

You can follow these same instructions to deploy Kubeflow on a non-automatic AKS cluster.

Instructions for Basic Deployment without TLS and with Default Password

This deployment option is for testing only. To deploy with TLS, and change default password, please click here: Deploy kubeflow with TLS.

⚠️ Warning: This deployment option would require users to have access to the kubernetes cluster. For a better deployment option that doesn’t have this restriction, uses TLS and shows how to change default password, please head to the [Deploy kubeflow with TLS] option.

Deploy AKS Automatic

Use the Azure CLI to deploy an AKS Automatic cluster.

💡Note: In order to complete this deployment, you will need to have either following permissions on Resource Group:

Microsoft.Authorization/policyAssignments/write
Microsoft.Authorization/policyAssignments/read.

For detailed instructions on installing AKS Automatic, please refer to the AKS Automatic installation documentation.

az login

💡Note: If you have access to multiple subscriptions, you may need to run the following command to work with the appropriate subscription: az account set --subscription <NAME_OR_ID_OF_SUBSCRIPTION>.

Set up your environment variables

RGNAME=kubeflow
CLUSTERNAME=kubeflow-aks-automatic
LOCATION=eastus

Create the resource group

az group create -n $RGNAME -l $LOCATION

Add or Update AKS extension

az extension add --name aks-preview

This article requires the aks-preview Azure CLI extension version 9.0.0b4 or later.

Create an AKS Automatic cluster

az aks create \
    --resource-group $RGNAME \
    --name $CLUSTERNAME \
    --location $LOCATION \
    --sku automatic \
    --generate-ssh-keys

💡Note: AKS Automatic is in Preview and requires feature to be registered in subscription.

az feature register --namespace Microsoft.ContainerService --name AutomaticSKUPreview

Connect to AKS Automatic Cluster

Install kubectl using the Azure CLI, if required.

az aks install-cli

Get the credentials for your AKS cluster

az aks get-credentials --resource-group $RGNAME --name $CLUSTERNAME

Verify connectivity to the cluster. This should return a list of nodes.

kubectl get nodes

💡Note: With AKS Automatic, you don’t need kubelogin as the cluster uses managed identity for authentication.

Deploy KubeFlow

Clone this repo which includes the kubeflow/manifests repo as Git Submodules

git clone --recurse-submodules https://github.com/Azure/kubeflow-aks.git

💡Note: The --recurse-submodules flag helps to get manifests from git submodule linked to this repo

Change directory into the newly cloned directory

cd kubeflow-aks

Install kustomize

Install kustomize using the installation script:

curl -s "https://raw.githubusercontent.com/kubernetes-sigs/kustomize/master/hack/install_kustomize.sh" | bash
sudo mv ./kustomize /usr/local/bin/kustomize

Verify the installation:

kustomize version

Run Kubeflow Kustomize deployment

This deployment option is for testing only. To deploy with TLS, and change default password, please click here: Deploy kubeflow with TLS.

From the root of the repo, cd into kubeflow’s manifests directory and make sure you are in the v1.10-branch.

cd manifests/
git checkout v1.10-branch
cd ..

Install all of the components via a single command

cp -a deployments/vanilla manifests/vanilla
cd manifests/  
while ! kustomize build vanilla | kubectl apply --server-side=true -f -; do echo "Retrying to apply resources"; sleep 10; done

💡Note: The --server-side=true flag helps with large CRDs that may exceed annotation size limits. The retry loop handles dependency ordering issues during installation.

Once the command has completed, check the pods are ready

kubectl get pods -n cert-manager
kubectl get pods -n istio-system
kubectl get pods -n auth
kubectl get pods -n knative-eventing
kubectl get pods -n knative-serving
kubectl get pods -n kubeflow
kubectl get pods -n kubeflow-user-example-com

💡Note: Depending on the VM SKU automatic picked for your region it might scale up to 4 nodes to run all the Kubeflow components

Access the Kubeflow dashboard

Run kubectl port-forward to access the Kubeflow dashboard

kubectl port-forward svc/istio-ingressgateway -n istio-system 8080:80

Finally, open http://localhost:8080 and login with the default user’s credentials. The default email address is user@example.com and the default password is 12341234

Testing the deployment with a Notebook server

You can test that the deployments worked by creating a new Notebook server using the GUI.

Click on “Create a new Notebook”
Click on “+ New Notebook” in the top right corner of the resulting page
Enter a name for the server
Leave the “jupyterlab” option selected
Feel free to pick one of the images available, in this case we choose the default
Set Requested CPU to 0.5 and requested memory in Gi to 1
Under Data Volumes click on “+ Add new volume”
Expand the resulting section
Set the name to datavol-1. The default name provided would not work because it has characters that are not allowed
Set the size in Gi to 1
Uncheck “Use default class”
Choose a class from the provided options. In this case I will choose azurefile-premium
Choose ReadWriteMany as the Access mode. Your data volume config should look like the picture below
Click on “Launch” at the bottom of the page. A successful deployment should have a green checkmark under status, after 1-2 minutes.
Click on “Connect” to access your jupyter lab
Under Notebook, click on Python 3 to access your jupyter notebook and start coding

Next steps

[Secure your kubeflow cluster using TLS and stronger Password] deployment option.