jetson-examples 0.2.8

Running Gen AI models and applications on NVIDIA Jetson devices with one-line command

jetson-examples

This repository provides examples for running AI models and applications on NVIDIA Jetson devices with a single command.

This repo builds upon the work of the jetson-containers, ultralytics and other excellent projects.

Features

• 🚀 Easy Deployment: Deploy state-of-the-art AI models on Jetson devices in one line.
• 🔄 Versatile Examples: Supports text generation, image generation, computer vision and so on.
• ⚡ Optimized for Jetson: Leverages Nvidia Jetson hardware for efficient performance.

Install

To install the package, run:

pip3 install jetson-examples

Notes:

• Check here for more installation methods
• To upgrade to the latest version, use: pip3 install jetson-examples --upgrade.

Quickstart

To run and chat with LLaVA, execute:

reComputer run llava

Example list

Here are some examples that can be run:

Example	Type	Model/Data Size	Docker Image Size	Command	Supported JetPack
🆕 Ultralytics-yolo	Computer Vision		15.4GB	reComputer run ultralytics-yolo	4.6, 5.1.1, 5.1.2, 5.1.3, 6.0, 6.1, 6.2
🆕 YOLO11	Computer Vision	*	*	reComputer run yolo11	5.1.1, 5.1.2, 5.1.3, 6.0, 6.1, 6.2, 6.2.1
🆕 YOLO26	Computer Vision	*	*	reComputer run yolo26	5.1.1, 5.1.2, 5.1.3, 6.0, 6.1, 6.2, 6.2.1, 7.0, 7.1
🆕 Deep-Live-Cam	Face-swapping	0.5GB	20GB	reComputer run deep-live-cam	6.0
🆕 Live-VLM-WebUI	Computer Vision (VLM)	*	*	reComputer run live-vlm-webui	6.0, 6.1, 6.2, 6.2.1, 7.0, 7.1
🆕 llama-factory	Finetune LLM		13.5GB	reComputer run llama-factory	5.1.1, 5.1.2, 5.1.3
🆕 ComfyUI	Computer Vision		20GB	reComputer run comfyui	5.1.1, 5.1.2, 5.1.3
Depth-Anything-V2	Computer Vision		15GB	reComputer run depth-anything-v2	5.1.1, 5.1.2, 5.1.3
Depth-Anything-V3	Computer Vision		7.6GB	reComputer run depth-anything-v3	6.1, 6.2, 6.2.1
🆕 Gemma4	Text (LLM)	2.5GB	0.49GB	reComputer run gemma4	6.1, 6.2, 6.2.1
🆕 Qwen3.5-4B	Text (LLM)	2.5GB	0.2GB	reComputer run qwen3.5-4b	6.1, 6.2, 6.2.1
🆕 Qwen3.6-35B	Text (LLM)	28GB	0.59GB	reComputer run qwen3.6-35b	6.1, 6.2, 6.2.1
🆕 Nemotron-3-Nano-30B	Text (LLM)	24.5GB	0.59GB	reComputer run nemotron-3-nano	6.1, 6.2, 6.2.1
Depth-Anything	Computer Vision		12.9GB	reComputer run depth-anything	5.1.1, 5.1.2, 5.1.3
Yolov10	Computer Vision	7.2M	5.74 GB	reComputer run yolov10	5.1.1, 5.1.2, 5.1.3, 6.0
Llama3	Text (LLM)	4.9GB	10.5GB	reComputer run llama3	5.1.1, 5.1.2, 5.1.3, 6.0
gpt-oss	Text (LLM)	39GB	31.28GB	reComputer run gpt-oss	6.1, 6.2, 6.2.1
🆕 GraspNet-Gemini	Robotics / Grasping	*	2.0GB	reComputer run graspnet-gemini	6.0, 6.1, 6.2, 6.2.1
ros1-jp6	Robotics / ROS 1	*	1.27GB	reComputer run ros1-jp6	6.1, 6.2, 6.2.1
nvblox	Robotics / Mapping	*	20.5GB+	reComputer run nvblox	6.x

Note: You should have enough space to run example, like LLaVA, at least 27.4GB totally

More Examples can be found examples.md

Calling Contributors Join Us!

How to work with us?

Want to add your own example? Check out the development guide.

We welcome contributions to improve jetson-examples! If you have an example you'd like to share, please submit a pull request. Thank you to all of our contributors! 🙏

This open call is listed in our Contributor Project. If this is your first time joining us, click here to learn how the project works. We follow the steps with:

• Assignments: We offer a variety of assignments to enhance wiki content, each with a detailed description.
• Submission: Contributors can submit their content via a Pull Request after completing the assignments.
• Review: Maintainers will merge the submission and record the contributions.

Contributors receive a $250 cash bonus as a token of appreciation.

For any questions or further information, feel free to reach out via the GitHub issues page or contact [e-mail]

TODO List

• detect host environment and install what we need
• all type jetson support checking list
• try jetpack 6.0
• check disk space enough or not before run
• allow to setting some configs, such as BASE_PATH
• support jetson-containers update
• better table to show example's difference

👥 Contributors

License

This project is licensed under the MIT License.

Resources

• https://github.com/dusty-nv/jetson-containers
• https://www.jetson-ai-lab.com/
• https://www.ultralytics.com/