This repository contains the supporting scripts and example applications for Optimize and deploy AI inference with ExecuTorch on Arm. Use the course for prerequisites, setup, and exercise instructions. The links below take you to the code for the module you are following.
| Module | Code |
|---|---|
| 1 | Transformer inference on Raspberry Pi |
| 2 | Image classification and INT8 quantization on Raspberry Pi |
| 3 | Semantic segmentation on Android |
| 4 | Ethos-U inference with Corstone-320 |
- Follow your lesson's environment, working-directory, and command instructions. Requirements differ between modules, so use the versions and installation steps specified for that exercise.
- Clone and keep the complete repository together. Module scripts import shared helpers from course_support. These helpers support tasks such as calibration and benchmarking across the course modules. Copying a single module directory can leave those imports unavailable.
- Open the script or application named in the lesson to connect each command with its implementation. Continue using the course to interpret outputs and decide the next step.
Optional AI-agent skills support working with the code; Module 0 of the course explains how to use them.
For proposed fixes or improvements, see Contributing. Code is provided under the Clear BSD License.