1.2-creating-artifacts-fine-tunning-pipeline.md

Experimenting and creating our Serving and Training script

In this first module we are going to open JupyterHub and logging-in to create our user session, let's start by exposing JupyterHub using kubectl port-forward.

Note: We are not exposing via LoadBalancer to avoid security risks, since we are using Jupyter dummy auth.

Accessing JupyterHub

Exposing JupyterHub endpoint:

kubectl port-forward svc/proxy-public 8080:80 -n jupyterhub

You can choose any port that you'd like (local-port):(service-port). In the above example we are using 8080, so open the http://localhost:8080 in your local browser.

Since we are using JupyterHub dummy auth mechanism, we can define any user and password in-order to get access to Jupyter Console:

Select [X] the GPU Server profile in JupyterHub and click in Start.

Now wait for the Server to start up.

Selecting the pipeline Notebook

In the left panel there is a "notebooks" folder. You have to choose the Notebook named "<model>-training-pipeline.ipynb" and copy it into the root folder because the "notebooks" folder in exposed as a read-only filesystem to keep the original files.

Now you can follow the steps on Jupyter Notebook.