A collection of sample code and resources used in the Amazon Distributed Computing Enthusiasts user group's Ray training sessions.
The first training session (conducted 2025-1-24) was an introductory session on what Ray is, who it is for, and how it works.
This code was from a demonstration at the end of the session, designed to provide a simple example of what it would take to transition a simple PyTorch model from pure PyTorch to training with Ray.
Note that this code is designed to be run compeltely locally (to keep the code simple), so you can easily try it out yourself on your local laptop by running the steps below.
This repository includes third-party libraries and dependencies that are subject to their own licenses and terms. While efforts have been made to ensure the quality of the provided code and resources, please note:
- The maintainer(s) of this repository are not responsible for any bugs, vulnerabilities, or issues present in third-party libraries used within this project.
- This repository is provided "as is", without warranty of any kind, express or implied. Use of the code and resources is at your own risk.
By using this repository, you acknowledge and accept these terms.
See CONTRIBUTING for more information.
This library is licensed under the MIT-0 License. See the LICENSE file.