Training and saving models with CML on a dedicated AWS EC2 runner

The files in this repository provide an example on how to use a dedicated runner on AWS to train and export a machine learning model. It accompanies this blog post, which contains a full guide on how to achieve this.

Contents

This repository contains the following files:

• requirements.txt: the packages necessary for training our model.
• get_data.py: script that generates sample data to train a model on.
• train.py: script that trains a random forest on the generated data and exports that model to a binary file, along with a confusion matrix and some metrics.
• .github/workflows/cml.yaml: example workflow that provisions an AWS EC2 instance to run train.py and export the resulting model.

How to install and run

Clone this repository and follow the instructions in this blog post. Specifically, make sure satisfy the prerequisites with regards to the AWS and GitHub Workflows set-up.

Need help? Or want to contribute?

If you need any help in following this guide you can drop us a message in Discord. In case you run into any issues that need fixing, don't hesitate to open an issue in this repository and/or submit a fix in a pull request.