Preparing to manage models for using the model CLI
Before you can start using the model CLI to automate model deployment or to perform
any other tasks, you must install the scikit-learn machine learning library
for Python through the Cloudera AI web UI.
You must perform this task through the Cloudera AI web
UI.
Create a new project with Python through the web UI.
Python provides sample files that you can use to create models using
CLI.
To start a new session, go to the Sessions page from the
left navigation panel and click new session.
The Start the new session page is displayed.
On Start the new session page, select, for example,
Python 3 from the Kernel
drop-down menu.
Enable or disable Spark ML Runtimes Addons. If you enable that option, select
the ID for the Spark ML Runtime Addon from the drop-down list.
Figure 1. Enabling Spark ML Runtimes Addon
Select Start Session to create the session.
From the input prompt, install the scikit-learn machine
learning library for Python by running the following command:
!pip3 install sklearn
Open the fit.py file available within your project from the
left navigation panel.
You can use the fit.py file to create a fitted model which
creates a model.pkl file that you can use to deploy the actual
model.
Run the fit.py file by clicking Run > Run all.
The model.pkl directory is created that you can see within
your project on the left navigation pane.
