Prototype your SAP Data Intelligence ML Scenario with Docker Desktop

[architectSAP]

Previously, I described how to Prototype your SAP Data Intelligence ML Scenario on Raspberry Pi.

Inspired by ravi.mittal’s excellent blog Manage your First Container using Kubernetes in SAP Cloud Platform, Kyma Runtime I got intrigued to use Docker Desktop instead.

Who would have thought that it comes with Kubernetes?

Adding Kubernetes Dashboard (see Appendix)

And JupyterHub (see Appendix) is easy enough. With this I create my Jupyter Notebook as before to import into SAP Data Intelligence later

On top I got complete control of my Containers / Apps

How cool is that?

Appendix

Kubernetes Dashboard

kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.3.1/aio/deploy/recommended.yaml

kubectl patch deployment kubernetes-dashboard -n kubernetes-dashboard --type 'json' -p '[{"op": "add", "path": "/spec/template/spec/containers/0/args/-", "value": "--enable-skip-login"}]'

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.5.0/components.yaml

kubectl patch deployment metrics-server -n kube-system --type 'json' -p '[{"op": "add", "path": "/spec/template/spec/containers/0/args/-", "value": "--kubelet-insecure-tls"}]'

kubectl proxy

JupyterHub

helm repo add jupyterhub https://jupyterhub.github.io/helm-chart/

helm repo update

helm upgrade --install jupyterhub jupyterhub/jupyterhub

Notebook

pip install sklearn

pip install matplotlib

from sklearn import datasets

from matplotlib import pyplot

df = datasets.load_iris()

x = df.data

y_true = df.target

fig = pyplot.figure()

ax = fig.add_subplot(projection='3d')

ax.scatter(x[:,2],x[:,1],x[:,0],c=y_true)

ax.set_xlabel(df.feature_names[2])

ax.set_ylabel(df.feature_names[1])

ax.set_zlabel(df.feature_names[0])

fig