Intelligent Applications on OpenShift with radanalytics.io
This workshop will teach you how to use radanalytics.io to develop and deploy intelligent applications with Apache Spark and OpenShift.
Prerequisites
Ideally, you'll want access to an OpenShift cluster (either Origin or OpenShift Container Platform) -- a local cluster that you've created with oc cluster up is fine. If you have Docker or a compatible container runtime installed, you can run the notebook images. It is also possible to run the notebooks outside of a container runtime, but we recommend running them in containers to simplify deployment.
Running the workshop in OpenShift
OpenShift is the easiest way to run the workshop, whether you're running against a hosted OpenShift environment or whether you've created a local development environment with oc cluster up. You'll need this resources.yaml file to set things up. First, create a project:
oc new-project workshop
Then use the template to create the necessary resources:
oc create -f https://raw.githubusercontent.com/radanalyticsio/workshop/develop/openshift/resources.yaml
Then you'll be able to add the var-notebook application to your project and run it. There will be a route created for you after the notebook server starts up. Visit that route and log in with password developer.
Running the notebooks locally with Docker
If you can't run OpenShift for some reason, you can run the notebook image in Docker. You'll want to pull the radanalyticsio/workshop-notebook image:
docker pull radanalyticsio/workshop-notebook:latest
You'll also want to clone the git repository for the application skeleton we'll be fleshing out in the workshop:
git clone https://github.com/radanalyticsio/var-sandbox.
To run the notebook, use
docker run --name workshop-notebook -d -p 8888:8888 radanalyticsio/workshop-notebook:latest
This will print out a container hash. You'll then use either the name we specified (workshop-notebook) or the hash (if, for some reason, you wanted to use a different name) to find the URL to log in to your notebook, by doing this:
docker exec workshop-notebook jupyter notebook list
Open the URL that command prints out and you're ready to go.
When you're done, download any modified notebook files that you want to save (or copy them out directly with docker cp workshop-notebook:/notebooks/$FILE .) and then delete the container with docker rm workshop-notebook.
If you can't run containers
If you can't install OpenShift, can't install Docker, or can't run the images for whatever reason, don't worry! This repository contains IPython notebook files for the hands-on portion of the workshop and GitHub will (mostly) render them, so you can follow along with those. (We recommend installing them locally so you can experiment with a live notebook yourself when you get a chance, of course.)