Building and Deploying Custom JupyterHub Images using Docker and Kubernetes to run Workshops in the Cloud
Jupyter provides an excellent interactive environment for running workshops. While there are many free services that let you explore Jupyter, you will need to run your own JupyterHub server if you want to use a custom image that includes your own software, if you want more cores than are provided by the free service, or you want to run a workshop with large numbers of attendees. Building and deploying your own JupyterHub using Docker, Kubernetes and the Cloud is very easy, and this workshop will show you how. You will build your own Docker image, create your own Cloud Kubernetes cluster, and will then deploy JupyterHub to this cluster using Helm. We will also provide tips and tricks we’ve learned from running Jupyter workshops ourselves. So, in short, this is a workshop in which you will learn how to build and run your own workshop
You should be comfortable using the Linux command line, should have a very basic understanding of what Docker (or containers) are, and have some knowledge of what Jupyter is (we will provide background reading on Docker and Jupyter, and will teach you about Kubernetes, JupyterHub, and deploying these to the cloud).
Installing the material onto the VM
The VM needs to have the following installed:
- Git (so you can download and update this material)
- A Python that includes jupyter (we will use the installed python3)
- Docker, with a running docker service
- The Microsoft “az” command line interface
- The “kubectl” interface installed via “az”
If you are attending a workshop then all of the necessary software should have already be installed in your workshop VM. For the RSE18 workshop you can validate this is the case by typing;
workshops@RSE2018-workshops:~$ cd woods workshops@RSE2018-workshops:~/woods$ git pull workshops@RSE2018-workshops:~/woods$ source conda.sh workshops@RSE2018-workshops:~/woods$ conda activate idp (idp) workshops@RSE2018-workshops:~/woods$ python test_workshop.py Tesing git... PASS Testing jupyter... PASS Testing docker... PASS Testing az... PASS Testing kubectl... PASS Testing helm... PASS Success. Looks like everything has installed correctly :-)
If you see the “Success. Looks like everything has installed correctly :-)” message, then you can move onto the next section.
If you are running this at home on your own computer, then you will need to install the software yourself. The below instructions provide all of the necessary instructions. If you are taking part in a workshop, then all of the software is installed, so you can move onto the next section.
Instructions to install and test all of these are below.
# sudo apt install git
# git clone https://github.com/chryswoods/k8s_jupyter_workshop ./woods # cd woods # ls Dockerfile README.html course fix-permissions test_workshop.py LICENSE conda.sh example_workshop jupyterhub
(this should result in the contents of this GitHub repository being downloaded into the local directory called ./woods)
We need a Python that includes Jupyter. The easiest way to do this is to use the existing python3 and install jupyter into a virtualenv
First, we need to make sure that virtualenv is installed
# sudo apt install virtualenv
Now install jupyter into the virtualenv in the workshop directory
# virtualenv --python=python3 woods_project # source woods_project/bin/activate # pip install jupyter
Once jupyter has installed, you can test using
This should print out a lot to the screen showing that Jupyter is starting, and then it will launch a web browser with a Jupyter file dialog.
You can close the web browser and use “CTRL+C” to shut down the Jupyter server.
When you have finished in the virtualenv, you can exit by typing
docker-ce using the instructions here
# sudo apt-get update # sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ software-properties-common # curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - # sudo apt-key fingerprint 0EBFCD88 pub 4096R/0EBFCD88 2017-02-22 Key fingerprint = 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88 uid Docker Release (CE deb) <firstname.lastname@example.org> sub 4096R/F273FCD8 2017-02-22 # sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ edge" # sudo apt-get update # sudo apt-get install docker-ce
(note we had to use the “edge” repository as a “stable” version for docker-ce does not exist yet for Ubuntu 18.04).
Next, you need to add the current user to the “docker” group. Do this using
# sudo usermod -a -G docker $USER
You will now need to log out and in again (actually have to reboot!)
Once you have logged in again, test, by running
$ docker run hello-world Unable to find image 'hello-world:latest' locally latest: Pulling from library/hello-world Digest: sha256:f5233545e43561214ca4891fd1157e1c3c563316ed8e237750d59bde73361e77 Status: Downloaded newer image for hello-world:latest Hello from Docker! This message shows that your installation appears to be working correctly. To generate this message, Docker took the following steps: 1. The Docker client contacted the Docker daemon. 2. The Docker daemon pulled the "hello-world" image from the Docker Hub. (amd64) 3. The Docker daemon created a new container from that image which runs the executable that produces the output you are currently reading. 4. The Docker daemon streamed that output to the Docker client, which sent it to your terminal. To try something more ambitious, you can run an Ubuntu container with: $ docker run -it ubuntu bash Share images, automate workflows, and more with a free Docker ID: https://hub.docker.com/ For more examples and ideas, visit: https://docs.docker.com/engine/userguide/
and you should see the output as printed below the command.
Microsoft “az” command line interface
We will install following the instructions here
# AZ_REPO=$(lsb_release -cs) # echo "deb [arch=amd64] https://packages.microsoft.com/repos/azure-cli/ $AZ_REPO main" | sudo tee /etc/apt/sources.list.d/azure-cli.list # curl -L https://packages.microsoft.com/keys/microsoft.asc | sudo apt-key add # sudo apt-get install apt-transport-https # sudo apt-get update && sudo apt-get install azure-cli
To test, run the command
az, which should result in something similar to;
# az /\ / \ _____ _ _ __ ___ / /\ \ |_ / | | | \'__/ _ \ / ____ \ / /| |_| | | | __/ /_/ \_\/___|\__,_|_| \___| Welcome to the cool new Azure CLI! Here are the base commands: account : Manage Azure subscription information. acr : Manage Azure Container Registries. acs : Manage Azure Container Services. ad : Synchronize on-premises directories and manage Azure Active Directory resources. advisor : (PREVIEW) Manage Azure Advisor. aks : Manage Kubernetes clusters. appservice : Manage App Service plans. backup : Commands to manage Azure Backups. batch : Manage Azure Batch. batchai : Batch AI. billing : Manage Azure Billing. cdn : Manage Azure Content Delivery Networks (CDNs). cloud : Manage registered Azure clouds. cognitiveservices: Manage Azure Cognitive Services accounts. configure : Display and manage the Azure CLI 2.0 configuration. This command is interactive. consumption : Manage consumption of Azure resources. container : (PREVIEW) Manage Azure Container Instances. cosmosdb : Manage Azure Cosmos DB database accounts. disk : Manage Azure Managed Disks. dla : (PREVIEW) Manage Data Lake Analytics accounts, jobs, and catalogs. dls : (PREVIEW) Manage Data Lake Store accounts and filesystems. eventgrid : Manage Azure Event Grid topics and subscriptions. extension : Manage and update CLI extensions. feature : Manage resource provider features. feedback : Loving or hating the CLI? Let us know! find : Find Azure CLI commands. functionapp : Manage function apps. group : Manage resource groups and template deployments. identity : Managed Service Identities. image : Manage custom virtual machine images. interactive : Start interactive mode. iot : (PREVIEW) Manage Internet of Things (IoT) assets. keyvault : Safeguard and maintain control of keys, secrets, and certificates. lab : Manage Azure DevTest Labs. lock : Manage Azure locks. login : Log in to Azure. logout : Log out to remove access to Azure subscriptions. managedapp : Manage template solutions provided and maintained by Independent Software Vendors (ISVs). monitor : Manage the Azure Monitor Service. mysql : Manage Azure Database for MySQL servers. network : Manage Azure Network resources. policy : Manage resource policies. postgres : Manage Azure Database for PostgreSQL servers. provider : Manage resource providers. redis : Access to a secure, dedicated Redis cache for your Azure applications. reservations : Manage Azure Reservations. resource : Manage Azure resources. role : Manage user roles for access control with Azure Active Directory and service principals. sf : Manage and administer Azure Service Fabric clusters. snapshot : Manage point-in-time copies of managed disks, native blobs, or other snapshots. sql : Manage Azure SQL Databases and Data Warehouses. storage : Manage Azure Cloud Storage resources. tag : Manage resource tags. vm : Provision Linux or Windows virtual machines. vmss : Manage groupings of virtual machines in an Azure Virtual Machine Scale Set (VMSS). webapp : Manage web apps.
kubectl interface via az
Next we need to install kubectl using az. Type
# sudo az aks install-cli
If this works, you should see kubectl being installed. You can type by typing
kubectl and seeing if the following is printed
# kubectl kubectl controls the Kubernetes cluster manager. Find more information at https://github.com/kubernetes/kubernetes. Basic Commands (Beginner): create Create a resource from a file or from stdin. expose Take a replication controller, service, deployment or pod and expose it as a new Kubernetes Service run Run a particular image on the cluster set Set specific features on objects run-container Run a particular image on the cluster. This command is deprecated, use "run" instead Basic Commands (Intermediate): get Display one or many resources explain Documentation of resources edit Edit a resource on the server delete Delete resources by filenames, stdin, resources and names, or by resources and label selector Deploy Commands: rollout Manage the rollout of a resource rolling-update Perform a rolling update of the given ReplicationController scale Set a new size for a Deployment, ReplicaSet, Replication Controller, or Job autoscale Auto-scale a Deployment, ReplicaSet, or ReplicationController Cluster Management Commands: certificate Modify certificate resources. cluster-info Display cluster info top Display Resource (CPU/Memory/Storage) usage. cordon Mark node as unschedulable uncordon Mark node as schedulable drain Drain node in preparation for maintenance taint Update the taints on one or more nodes Troubleshooting and Debugging Commands: describe Show details of a specific resource or group of resources logs Print the logs for a container in a pod attach Attach to a running container exec Execute a command in a container port-forward Forward one or more local ports to a pod proxy Run a proxy to the Kubernetes API server cp Copy files and directories to and from containers. auth Inspect authorization Advanced Commands: apply Apply a configuration to a resource by filename or stdin patch Update field(s) of a resource using strategic merge patch replace Replace a resource by filename or stdin convert Convert config files between different API versions Settings Commands: label Update the labels on a resource annotate Update the annotations on a resource completion Output shell completion code for the specified shell (bash or zsh) Other Commands: api-versions Print the supported API versions on the server, in the form of "group/version" config Modify kubeconfig files help Help about any command plugin Runs a command-line plugin version Print the client and server version information Use "kubectl <command> --help" for more information about a given command. Use "kubectl options" for a list of global command-line options (applies to all commands).
The final step is to install helm, following the instructions from here
# curl https://raw.githubusercontent.com/kubernetes/helm/master/scripts/get > get_helm.sh # chmod 700 get_helm.sh # ./get_helm.sh
Test by trying to run the command
helm, which should produce the output that is similar to the below
# helm The Kubernetes package manager To begin working with Helm, run the 'helm init' command: $ helm init This will install Tiller to your running Kubernetes cluster. It will also set up any necessary local configuration. Common actions from this point include: - helm search: search for charts - helm fetch: download a chart to your local directory to view - helm install: upload the chart to Kubernetes - helm list: list releases of charts Environment: $HELM_HOME set an alternative location for Helm files. By default, these are stored in ~/.helm $HELM_HOST set an alternative Tiller host. The format is host:port $HELM_NO_PLUGINS disable plugins. Set HELM_NO_PLUGINS=1 to disable plugins. $TILLER_NAMESPACE set an alternative Tiller namespace (default "kube-system") $KUBECONFIG set an alternative Kubernetes configuration file (default "~/.kube/config") Usage: helm [command] Available Commands: completion Generate autocompletions script for the specified shell (bash or zsh) create create a new chart with the given name delete given a release name, delete the release from Kubernetes dependency manage a chart's dependencies fetch download a chart from a repository and (optionally) unpack it in local directory get download a named release history fetch release history home displays the location of HELM_HOME init initialize Helm on both client and server inspect inspect a chart install install a chart archive lint examines a chart for possible issues list list releases package package a chart directory into a chart archive plugin add, list, or remove Helm plugins repo add, list, remove, update, and index chart repositories reset uninstalls Tiller from a cluster rollback roll back a release to a previous revision search search for a keyword in charts serve start a local http web server status displays the status of the named release template locally render templates test test a release upgrade upgrade a release verify verify that a chart at the given path has been signed and is valid version print the client/server version information Flags: --debug enable verbose output -h, --help help for helm --home string location of your Helm config. Overrides $HELM_HOME (default "/home/chris/.helm") --host string address of Tiller. Overrides $HELM_HOST --kube-context string name of the kubeconfig context to use --tiller-namespace string namespace of Tiller (default "kube-system") Use "helm [command] --help" for more information about a command.
Once you are happy, you can remove the
get_helm.sh script, e.g.
# rm get_helm.sh
Everything installed - let’s run a final test
Congratulations - everything should now be installed. Please confirm this by running the following command, which is included with this repository (and so should have been downloaded by the git clone command above)
# source woods_project/bin/activate # python test_workshop.py Tesing git... PASS Testing jupyter... PASS Testing docker... PASS Testing az... PASS Testing kubectl... PASS Testing helm... PASS Success. Looks like everything has installed correctly :-)
Hopefully you will see the “Success” message. If not, check the instructions above or send me an email