How to Train XGBoost Models in the Cloud with Amazon Web Services

Last Updated on August 27, 2020

The XGBoost library provides an implementation of gradient boosting designed for speed and performance.

It is implemented to make best use of your computing resources, including all CPU cores and memory.

In this post you will discover how you can setup a server on Amazon’s cloud service to quickly and cheaply create very large models.

After reading this post you will know:

• How to setup and configure an Amazon EC2 server instance for use with XGBoost.
• How to confirm the parallel capabilities of XGBoost are working on your server.
• How to transfer data and code to your server and train a very large model.

Kick-start your project with my new book XGBoost With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

• Update May/2020: Updated instructions.

How to Train XGBoost Models in the Cloud with Amazon Web Services
Photo by Dennis Jarvis, some rights reserved.