Edge AI with PyTorch & ExecuTorch - Tiny Rock-Paper-Scissors on Arm

About this Learning Path

Who is this for?

This learning path is for machine learning developers interested in deploying TinyML models on Arm-based edge devices. You will learn how to train and deploy a machine learning model for the classic game "Rock-Paper-Scissors" on edge devices. You'll use PyTorch and ExecuTorch, frameworks designed for efficient on-device inference, to build and run a small-scale computer vision model.

What will you learn?

Upon completion of this Learning Path, you will be able to:

• Train a small Convolutional Neural Network (CNN) for image classification using PyTorch.
• Understand how to use synthetic data generation for training a model when real-world data is limited.
• Optimize and convert a PyTorch model into an ExecuTorch program (.pte) for Arm-based devices.
• Run the trained model on a local machine to play an interactive mini-game, demonstrating model inference.

Prerequisites

Before starting, you will need the following:

• A basic understanding of machine learning concepts.
• Familiarity with Python and the PyTorch library.
• Having completed Introduction to TinyML on Arm using PyTorch and ExecuTorch .
• An x86 Linux host machine or VM running Ubuntu 22.04 or higher.