This repository provides examples for running AI models and applications on NVIDIA Jetson devices. For generative AI, it supports a variety of examples including text generation, image generation, vision transformers, vector databases, and audio models. To run the examples, you need to install the jetson-examples package and use the Seeed Studio reComputer, the edge AI device powered by Jetson Orin. The repo aims to make it easy to deploy state-of-the-art AI models, with just one line of command, on Jetson devices for tasks like language understanding, computer vision, and multimodal processing.
This repo builds upon the work of the Jetson Containers, which provides a modular container build system for various AI/ML packages on NVIDIA Jetson devices. It also leverages resources and tutorials from the Jetson Generative AI Lab, which showcases bringing generative AI to the edge, powered by Jetson hardware.
pip install jetson-examplesTo run and chat with LLaVA:
reComputer run llavareComputer supports a list of examples from jetson-ai-lab
Here are some examples that can be run:
| Example | Type | Model/Data Size | Image Size | Command |
|---|---|---|---|---|
| text-generation-webui | Text (LLM) | 3.9GB | 14.8GB | reComputer run text-generation-webui |
| LLaVA | Text + Vision (VLM) | 13GB | 14.4GB | reComputer run llava |
| stable-diffusion-webui | Image Generation | 3.97G | 7.3GB | reComputer run stable-diffusion-webui |
| nanoowl | Vision Transformers(ViT) | 613MB | 15.1GB | reComputer run nanoowl |
| nanodb | Vector Database | 76GB | 7.0GB | reComputer run nanodb |
| whisper | Audio | 1.5GB | 6.0GB | reComputer run whisper |
Note: You should have enough space to run example, like
LLaVA, at least27.4GBtotally
More Examples can be found examples.md
- check disk space enough or not before run
- allow to setting some configs, such as
BASE_PATH - detect host environment and install what we need
- support jetson-containers update
- all type jetson support checking list
- better table to show example's difference
- try jetpack 6.0