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.
- 🚀 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.
To install the package, run:
pip3 install jetson-examplesNotes:
- Check here for more installation methods
- To upgrade to the latest version, use:
pip3 install jetson-examples --upgrade.
To run and chat with LLaVA, execute:
reComputer run llava
Here are some examples that can be run:
| Example | Type | Model/Data Size | Docker Image Size | Command |
|---|---|---|---|---|
| 🆕 Ultralytics-yolo | Computer Vision | 15.4GB | reComputer run ultralytics-yolo |
|
| 🆕 Deep-Live-Cam | Face-swapping | 0.5GB | 20GB | reComputer run deep-live-cam |
| 🆕 llama-factory | Finetune LLM | 13.5GB | reComputer run llama-factory |
|
| 🆕 ComfyUI | Computer Vision | 20GB | reComputer run comfyui |
|
| Depth-Anything-V2 | Computer Vision | 15GB | reComputer run depth-anything-v2 |
|
| Depth-Anything | Computer Vision | 12.9GB | reComputer run depth-anything |
|
| Yolov10 | Computer Vision | 7.2M | 5.74 GB | reComputer run yolov10 |
| Llama3 | Text (LLM) | 4.9GB | 10.5GB | reComputer run llama3 |
| Ollama | Inference Server | * | 10.5GB | reComputer run ollama |
| LLaVA | Text + Vision (VLM) | 13GB | 14.4GB | reComputer run llava |
| Live LLaVA | Text + Vision (VLM) | 13GB | 20.3GB | reComputer run live-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 |
| Yolov8-rail-inspection | Computer Vision | 6M | 13.8GB | reComputer run yolov8-rail-inspection |
| TensorFlow MoveNet Thunder | Computer Vision | 7.7GB | reComputer run MoveNet-Thunder |
|
| Parler-TTS mini: expresso | Audio | 6.9GB | reComputer run parler-tts |
Note: You should have enough space to run example, like
LLaVA, at least27.4GBtotally
More Examples can be found examples.md
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 edgeai@seeed.cc
- 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
This project is licensed under the MIT License.