This is an update to previous tutorials, focused on deploying Kubernetes 1.19.7 (released in Jan 2021, based on 1.19.0 released in August 2020), compared to 1.17.6 of the previous version of the tutorial.
For an overview of my work on deploying Kubernetes and JupyterHub on Jetstream, see my Gateways 2020 paper.
We will use Kubespray 2.15.0, which first runs
terraform to create the Openstack resources,
ansible to configure the servers to run all the Kubernetes services.
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
On a Ubuntu 18.04 install
python3-openstackclient with APT, I tested with
Any other platform works as well, also install
terraform by copying the correct binary to
/usr/local/bin/, see https://www.terraform.io/intro/getting-started/install.html.
The requirement is a terraform version
> 0.12, I tested with
Request API access
In order to make sure your XSEDE account can access the Jetstream API, you need to contact the Helpdesk, see the instructions on the Jetstream Wiki. You will also receive your TACC password, which could be different than your XSEDE one (username is generally the same).
Login to the TACC Horizon panel at https://tacc.jetstream-cloud.org/dashboard, this is basically the low level web interface to OpenStack, a lot more complex and powerful than Atmosphere available at https://use.jetstream-cloud.org/application. Use
tacc as domain, your TACC username (generally the same as your XSEDE username) and your TACC password.
First choose the right project you would like to charge to in the top dropdown menu (see the XSEDE website if you don’t recognize the grant code).
Click on Compute / API Access and download the OpenRC V3 authentication file to your machine. Source it typing:
it should ask for your TACC password. This configures all the environment variables needed by the
openstack command line tool to interface with the Openstack API.
openstack flavor list
This should return the list of available “sizes” of the Virtual Machines.
I needed to make a few modifications to
kubespray to adapt it to Jetstream:
git clone https://github.com/zonca/jetstream_kubespray git checkout -b branch_v2.15.0 origin/branch_v2.15.0
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
jetstream_kubespray, copy from my template:
export CLUSTER=kubejetstream cd inventory/$CLUSTER
Open and modify
cluster.tfvars, choose your image and number of nodes.
You can find suitable images (they need to be JS-API-Featured, you cannot use the same instances used in Atmosphere):
openstack image list | grep "JS-API"
The default is
Paste the floating ip created previously into
I already preconfigured the network UUID both for IU and TACC, but you can crosscheck
looking for the
public network in:
openstack network list
Create the resources:
The last output log of Terraform should contain the IP of the master node
k8s_master_fips, wait for it to boot then SSH in with:
export IP=XXX.XXX.XXX.XXX ssh ubuntu@$IP
centos@$IP for CentOS images.
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 terraform_destroy.sh. 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
First make sure you have a recent version of
ansible installed, you also need additional modules,
so first run:
pip install -r requirements.txt
pip script installs a predefined version of ansible, currently
2.9.16, 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
If a server is not answering to ping, first try to reboot it:
openstack server reboot $CLUSTER-k8s-node-nf-1
Or delete it and run
terraform_apply.sh to create it again.
Install Kubernetes with
inventory/$CLUSTER/group_vars/all.yml, in particular
bootstrap_os, I setup
ubuntu, change it to
centos if you used the Centos 7 base image.
inventory/$CLUSTER/group_vars/k8s-cluster/k8s-cluster.yml, set the public floating IP of the master instance in
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 ingress-nginx ingress-nginx-controller-7tqsr 1/1 Running 0 3h21m kube-system coredns-85967d65-qczgr 1/1 Running 0 117m kube-system coredns-85967d65-wp9vm 1/1 Running 0 117m kube-system dns-autoscaler-5b7b5c9b6f-vh4vv 1/1 Running 0 3h21m kube-system kube-apiserver-kubejetstream-k8s-master-1 1/1 Running 1 3h24m kube-system kube-controller-manager-kubejetstream-k8s-master-1 1/1 Running 0 3h24m kube-system kube-flannel-5qzxn 1/1 Running 0 3h22m kube-system kube-flannel-mrlkz 1/1 Running 0 3h22m kube-system kube-proxy-9qpmz 1/1 Running 0 118m kube-system kube-proxy-rzqv6 1/1 Running 0 118m kube-system kube-scheduler-kubejetstream-k8s-master-1 1/1 Running 0 3h24m kube-system nginx-proxy-kubejetstream-k8s-node-1 1/1 Running 0 3h22m kube-system nodelocaldns-d7r2c 1/1 Running 0 3h21m kube-system nodelocaldns-jx2st 1/1 Running 0 3h21m
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 --2018-09-24 03:01:14-- http://localhost/ Resolving localhost (localhost)... 127.0.0.1 Connecting to localhost (localhost)|127.0.0.1|:80... connected. HTTP request sent, awaiting response... 404 Not Found 2018-09-24 03:01:14 ERROR 404: Not Found.
Error 404 is a good sign, the service is up and serving requests, currently there is nothing to deliver.
Finally test that the routing through the Jetstream instance is working correctly by opening your browser
and test that if you access
js-XX-XXX.jetstream-cloud.org you also get a
default backend - 404 message.
If any of the tests hangs or cannot connect, there is probably a networking issue.
(Optional) Setup kubectl locally
kubectl locally, I am currently using
We also set
kubeconfig_localhost: true, which copies the
I have a script to copy that to
.config/kube and 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:
(Optional) Setup helm locally
Install helm 3 from the release page on Github
I tested with
Now checkout the JupyterHub configuration files repository on the local machine (if you have setup kubectl and helm locally, otherwise on the master node).
git clone https://github.com/zonca/jupyterhub-deploy-kubernetes-jetstream
Inside that, first run
to create the secret strings needed by JupyterHub then edit its output
secrets.yaml to make sure it is consistent, edit the
hosts lines if needed. For example, supply the Jetstream DNS name of the master node
js-XXX-YYY.jetstream-cloud.org (XXX and YYY are the last 2 groups of the floating IP of the instance AAA.BBB.XXX.YYY).
bash configure_helm_jupyterhub.sh kubectl create namespace jhub
It is preferable to run the Hub and the Proxy on the master node, just in case we
want to downsize the cluster to only one node to save resources.
This is already configured in
nodeSelector: node-role.kubernetes.io/master: ""
Delete those lines if instead you’d rather have Hub and Proxy run also on other nodes.
helm to install JupyterHub:
This is installing
0.11.1, you can check on the zero-to-jupyterhub release page if a newer version is available, generally transitioning to new releases is painless, they document any breaking changes very well.
Check pods running with:
kubectl get pods -n jhub
proxy is running, even if
hub is still in preparation, you can check
in browser, you should get “Service Unavailable” which is a good sign that
the proxy is working.
You can finally connect with your browser to
check if the Hub is working fine, after that, the pods running using:
kubectl get pods -n jhub
continuous-image-puller-77bb9 1/1 Running 0 7m23s hub-75d787584d-bhhgc 1/1 Running 0 7m23s jupyter-zonca 1/1 Running 0 4m34s proxy-78b8c47d7b-92fjf 1/1 Running 0 7m23s user-scheduler-6c4d6f7f57-mqwmh 1/1 Running 0 7m23s user-scheduler-6c4d6f7f57-sp5sh 1/1 Running 0 7m23s
After JupyterHub is deployed and integrated with Cinder for persistent volumes, for any other customizations, first authentication, you are in good hands as the Zero-to-Jupyterhub documentation is great.
Setup HTTPS with letsencrypt
Kubespray has the option of deploying also
cert-manager, but I had trouble deploying an issuer,
it was easier to just deploy it afterwards following my previous tutorial
Feedback on this is very welcome, please open an issue on the Github repository or email me at
zonca on the domain of the San Diego Supercomputer Center (sdsc.edu).