This project contains the code, data, and artifacts for the Getting Started in Domino Tutorial, found here for Python.

This tutorial will guide you through a common model lifecycle in Domino. You will start by working with data from the Balancing Mechanism Reporting Service in the UK. We will be exploring the Electricty Generation by Fuel Type and predicting the electricty generation in the future. You’ll see examples of Jupyter, Dash, pandas, and Prophet used in Domino.

Table of Contents:

• Forecast_Power_Generation.ipynb
• Scheduled_Forecast_Power_Generation.ipynb
• Forecast_Power_Generation_for_Launcher.ipynb
• forecast.ipynb
• forecast_predictor.py
• data.csv
• app.sh

Before using this project in a training, follow the steps below: (Note you will need Admin access for the following)

• Check that the com.cerebro.domino.builder.job.environment.buildMemory central config is set to 4294967294 (a requirement for PyStan).

• Then create environment with this base image: quay.io/domino/compute-environment-images:ubuntu20-py3.9-r4.2-domino5.3-standard and the following dockerfile instructions:

RUN pip install --disable-pip-version-check prophet

• Set the ShortLived.iFrameSecurityEnabled to false (this is so that the Dash app plot can render).

• Publish the app, create a model API, launcher, and scheduled job to test functionality.

• The app in this file is compatible with dash < 2.0.0. Ensure you install the following version of dash: RUN pip install dash==1.20.0 dash-core-components==1.16.0 dash-html-components==1.1.3 dash-renderer==1.9.1 dash-table==4.11.3