Deploy Kubernetes on Jetstream 2 with Kubespray 2.18.0


March 30, 2022

Obsolete: please use the updated release of this tutorial.

This is the first tutorial targeted at Jetstream 2. The system is in early access and will be soon made available, see

My latest tutorial on Jetstream 1 executed Kubespray 2.15.0, here we are also switching to Kubespray 2.18.0, which installs Kubernetes v1.22.5, released in December 2021.

For an overview of my work on deploying Kubernetes and JupyterHub on Jetstream, see my Gateways 2020 paper.

Create Jetstream Virtual machines with Terraform

Terraform allows to execute recipes that describe a set of OpenStack resources and their relationship. In the context of this tutorial, we do not need to learn much about Terraform, we will configure and execute the recipe provided by kubespray.


I have been testing with python-openstackclient version 5.8.0, but any recent openstack client should work. install terraform by copying the correct binary to /usr/local/bin/, see The requirement is a terraform version > 0.12, I tested with 0.14.4. Newer versions, for example I tried 1.1.7, do not work with Kubespray.

Request API access

The procedure to configure API access has changed since Jetstream, make sure you follow the instructions in the Jetstream 2 documentation to create application credentials

Also make sure you are not hitting any of the issues in the Troubleshooting page, in particular, it is a good idea to set your password within single quotes to avoid special characters being interpreted by the shell:

export OS_APPLICATION_CREDENTIAL_SECRET='xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'

Test with:

openstack flavor list

This should return the list of available “sizes” of the Virtual Machines.

You also need to add to the app* also this line:


otherwise Ansible will fail with you must either set external_openstack_username or external_openstack_application_credential_name.

Clone kubespray

I needed to make a few modifications to kubespray to adapt it to Jetstream:

git clone
git checkout -b branch_v2.18.0 origin/branch_v2.18.0

See an overview of my changes compared to the standard kubespray release 2.18.0, compared to Jetstream 1, there are no actual changes to the kubespray software itself, it is just a matter of configuring it. Anyway, it is convenient to just clone the repository.

Notice also that the most important change compared to Jetstream 1 is that we are using the new External Openstack provider, instead of the internal Openstack provider which is being discontinued. This also pushed me to use the Cinder CSI plugin, which also has a more modern architecture.

Get a subdomain

One option is to use the Designate Openstack service deployed by Jetstream to get an automatically created domain for the instances. In this case the DNS name will be of the form:

where tg-xxxxxxxxx is the ID of your Jestream 2 allocation, you need to specify it at the bottom of cluster.tfvars before running Terraform. The first part of the URL is the instance name, we shortened it removing k8s-master because domains too long do not work with Letsencrypt.

After having executed Terraform, you can pip install on your local machine the package python-designateclient to check what records were created (mind the final period):

openstack recordset list

As usual with stuff related to DNS, there are delays, so your record could take up to 1 hour to work, or if you delete the instance and create it again with another IP it could take hours to update.

Instead, if you have a way of getting a domain outside of Jetstream, better reserve a floating IP, see below.

Reserve a floating IP

We prefer not to have a floating IP handled by Terraform, otherwise it would be released every time we need to redeploy the cluster, better create it beforehand:

openstack floating ip create public

This will return a public floating IP address, it can also be accessed with:

openstack floating ip list

It is useful to save the IP into the app*, so that every time you load the credentials you also get the address of the master node.


Run Terraform

Inside jetstream_kubespray, choose a name for the cluster and copy from my template:

export CLUSTER=yourclustername
cp -r inventory/kubejetstream inventory/$CLUSTER
cd inventory/$CLUSTER

also export CLUSTER=yourclustername is useful to add to the app*

Open and modify cluster.tfvars, choose your image (by default Ubuntu 20) and number of nodes and the flavor of the nodes, by default they are medium instances ("4").

Paste the floating ip created previously into k8s_master_fips, unless you are using a subdomain. Make also sure you add your auto allocated router ID, see instructions inside cluster.tfvars.

Initialize Terraform:


Create the resources:


Terraform is very fast in building all the resources, sometimes resources are not ready yet, so the Apply command fails, just run it again, it happens regularly, nothing to worry about.

You can SSH into the master node with:

ssh ubuntu@$IP

Inspect with Openstack the resources created:

openstack server list
openstack network list

You can cleanup the virtual machines and all other Openstack resources (all data is lost) with bash The floating IP won’t be released so we can create a cluster again from scratch with the same IP address.

Install and test Ansible

Change folder back to the root of the jetstream_kubespray repository,

First make sure you have a recent version of ansible installed, you also need additional modules, so first run:

pip install -r requirements.txt

This pip script installs a predefined version of ansible, currently 2.10.15, so it is useful to create a virtualenv or a conda environment and install packages inside that.

Then following the kubespray documentation, we setup ssh-agent so that ansible can SSH from the machine with public IP to the others:

eval $(ssh-agent -s)
ssh-add ~/.ssh/id_rsa

Test the connection through ansible:

ansible -i inventory/$CLUSTER/hosts -m ping all

Install Kubernetes with kubespray

In inventory/$CLUSTER/group_vars/k8s_cluster/k8s-cluster.yml, set the public floating IP of the master instance in supplementary_addresses_in_ssl_keys.

Finally run the full playbook, it is going to take a good 10 minutes, go make coffee:


If the playbook fails with “cannot lock the administrative directory”, it is due to the fact that the Virtual Machine is automatically updating so it has locked the APT directory. Just wait a minute and launch it again. It is always safe to run ansible multiple times.

If the playbook gives any error, try to retry the above command, sometimes there are temporary failed tasks, Ansible is designed to be executed multiple times with consistent results.

You should have now a Kubernetes cluster running, test it:

$ ssh ubuntu@$IP
$ sudo su
$ kubectl get pods --all-namespaces
NAMESPACE       NAME                                           READY   STATUS    RESTARTS   AGE
ingress-nginx   ingress-nginx-controller-4xd64                 1/1     Running   0          27m
kube-system     coredns-8474476ff8-9gd8w                       1/1     Running   0          27m
kube-system     coredns-8474476ff8-qtshk                       1/1     Running   0          27m
kube-system     csi-cinder-controllerplugin-9fb5946bf-hwfhp    6/6     Running   0          26m
kube-system     csi-cinder-nodeplugin-r69nl                    3/3     Running   0          26m
kube-system     dns-autoscaler-5ffdc7f89d-s4sj4                1/1     Running   0          27m
kube-system     kube-apiserver-kubejs2-k8s-master-1            1/1     Running   1          66m
kube-system     kube-controller-manager-kubejs2-k8s-master-1   1/1     Running   1          66m
kube-system     kube-flannel-2clqv                             1/1     Running   0          29m
kube-system     kube-flannel-2wbtq                             1/1     Running   0          29m
kube-system     kube-proxy-hmz6t                               1/1     Running   0          30m
kube-system     kube-proxy-xkhjx                               1/1     Running   0          30m
kube-system     kube-scheduler-kubejs2-k8s-master-1            1/1     Running   1          66m
kube-system     nginx-proxy-kubejs2-k8s-node-1                 1/1     Running   0          64m
kube-system     nodelocaldns-jwxg8                             1/1     Running   0          27m
kube-system     nodelocaldns-z4sjl                             1/1     Running   0          27m
kube-system     openstack-cloud-controller-manager-6q28z       1/1     Running   0          29m
kube-system     snapshot-controller-786647474f-7x8zx           1/1     Running   0          25m

Compare that you have all those services running also in your cluster. We have also configured NGINX to proxy any service that we will later deploy on Kubernetes, test it with:

$ wget localhost
--2022-03-31 06:51:20--  http://localhost/
Resolving localhost (localhost)...
Connecting to localhost (localhost)||:80... connected.
HTTP request sent, awaiting response... 404 Not Found
2022-03-31 06:51:20 ERROR 404: Not Found.

Error 404 is a good sign, the service is up and serving requests, currently there is nothing to deliver. If any of the tests hangs or cannot connect, there is probably a networking issue.

Set the default storage class

Kubespray sets up the cinder-csi storage class, but it is not set as default, we can fix it with:

kubectl patch storageclass cinder-csi -p '{"metadata": {"annotations":{"":"true"}}}'

so that we don’t need to explicitely configure it in the applications we deploy.

(Optional) Setup kubectl locally

Install kubectl locally, I am currently using 1.20.

We also set kubeconfig_localhost: true, which copies the kubectl configuration admin.conf to:


I have a script to replace the IP with the floating IP of the master node, for this script to work make sure you have exported the variable IP:


Finally edit again the app* and add:

export KUBECONFIG=$(pwd -P)/"jetstream_kubespray/inventory/$CLUSTER/artifacts/admin.conf"

(Optional) Setup helm locally

Install helm 3 from the release page on Github

I tested with v3.8.1.

Install Jupyterhub

Follow up in the next tutorial: Install JupyterHub on Jetstream 2 on top of Kubernetes