module-machine-learning-ODS.md

Machine Learning in a Public Cloud on Red Hat OpenShift Data Science (RHODS)

This document describes how to prepare & execute the machine learning demo on RHODS

• Demo Preparation/ or possible Demo Usecase
  • Deploy OpenDataHub with a JupyterHub on the Red Hat OpenShift Data Science (RHODS)
• Demo Execution
  • Demo ML modeling on RHODS

Demo Preparation/ or possible Demo Usecase

The demo is based on the internal Red Hat OpenShift Data Science (https://source.redhat.com/groups/public/rhodsinternal), but can be doployed on a customer specific OpenShift Data Science, too.

Deploy OpenDataHub with a JupyterHub on the Red Hat OpenShift Data Science (RHODS)

• Go to https://source.redhat.com/groups/public/rhodsinternal and find the place where you can get support on the internal offer of Red Hat OpenShift Data Science.

• Look for the Link https://red.ht/rhods-internal and use it to reach the login page for RHODS.

• Click "Log in with OpenShift". Then choose RedHat-Google-Auth.

You should now see the Dashboard of the RHODS. Under the menu point "Applications -> Enabled" you see the all the applications that you can lunch. In our case you have to look for "JupyterHub". Then press "Launch application"

Option 1: Load necessary data through a Terminal

In your newly created JupyterHub you know need to create a new tab. Click on blue button with the "+" in it and choose under the headline "Other" a "Terminal"

With that Jupyter Terminal you can download with these commands direclty the neccessary documents from git:

curl -O https://raw.githubusercontent.com/sa-mw-dach/manuela-dev/master/ml-models/anomaly-detection/Anomaly-Detection-simple-ML-Training.ipynb

curl -O https://raw.githubusercontent.com/sa-mw-dach/manuela-dev/master/ml-models/anomaly-detection/raw-data.csv

Option 2: Load a Git repository

• Load the repository: Git -> Add Remote Repository
• Enter this link: https://github.com/sa-mw-dach/manuela-dev.git
• Open in the left column the folder manueladev -> ml-models -> anomaly-detection

Demo Execution

Demo ML modeling on RHODS

Demo the notebook

Open the notebook Anomaly-Detection-simple-ML-Training.ipynb with a click on the file on the left column.

Option 1: Lightweigt demo

• All output cells are populated. Don't run any cells.
• Walk through the content and explain the high level flow.

Option 2: Full demo

• Clear current outputs: Edit -> Clear All Outputs
• Run each cell by clicking in the bash cell then press [Shift][Enter] and explain each step.