How to Setup NVIDIA Jetson with Ultralytics YOLOv8 | QuickStart Guide Walkthrough | Episode 63

[@Arun-zn9vd]

Please upload a video to deploy Yolo Vx models on Raspberry Pi

[@Ultralytics]

Thanks for your suggestion! We actually have a detailed guide on deploying YOLOv8 models on Raspberry Pi. You can check it out here: docs.ultralytics.com/guides/raspberry-pi/. It covers everything from setup to running inference. If you have any specific questions or run into issues, feel free to ask! 😊

[@andreswilches1713]

Hi, great video !! I was wondering, I understand that Jetson Nano 4GB with Jetpack 4.x comes with python 3.6 and Ultralytics requires python >= 3.8. So, is not possible to install ultralytics in this device ? or do you know a way to bypass this ? Thank you !

[@Ultralytics]

Hi there! 😊 Thanks for the kind words! You can indeed use Ultralytics on a Jetson Nano 4GB with JetPack 4.x by upgrading Python to version 3.8 or higher. One way to do this is by creating a virtual environment with Python 3.8. You can follow the detailed steps in our guide here: docs.ultralytics.com/guides/nvidia-jetson/. If you encounter any issues, make sure you’re using the latest versions of `torch` and `ultralytics`. Feel free to share any specific error messages if you need further assistance. Happy coding! 🚀

[@edueclara]

Hi, I think it's more complicated than that. Even if you could install Python 3.8 on a Jetson Nano with JetPack 4.x, it would be a nightmare to get CUDA and all the other libraries working

[@Ultralytics]

You're right, it can be quite challenging. JetPack 4.x is tightly integrated with Python 3.6, making it difficult to upgrade without compatibility issues. A more straightforward approach might be to use a Jetson device with a newer JetPack version that supports Python 3.8 natively. Alternatively, consider using Docker to manage dependencies more easily. For more details, check out our guide: docs.ultralytics.com/guides/nvidia-jetson/. If you have further questions, feel free to ask! 😊

[@noedavila9646]

I have a Jetson Nano 4GB and having trouble understanding the docker. Is there another way to install ultralytics without the use of a docker?

[@Ultralytics]

Absolutely! You can set up Ultralytics on your Jetson Nano without Docker. Follow the steps in our NVIDIA Jetson Quickstart Guide docs.ultralytics.com/guides/nvidia-jetson/ for detailed instructions. If you encounter any issues, make sure your packages are up-to-date. 🚀

[@noedavila9646]

@@Ultralytics I apologize and should have been more specific, but it mentions setting up on a Jetson Nano and I have a Jetson Nano Development Kit. Will this affect it in any way? Would it be possible to still use a docker regardless of Jetson Device?

[@Ultralytics]

No worries! The setup process is similar for the Jetson Nano Development Kit. You can still use Docker regardless of the Jetson device. For Docker setup, use the command: ```sh t=ultralytics/ultralytics:latest-jetson-jetpack4 && sudo docker pull $t && sudo docker run -it --ipc=host --runtime=nvidia $t ``` For more details, check out our guide: docs.ultralytics.com/guides/nvidia-jetson/. Happy coding! 😊

[@noedavila9646]

​@@Ultralytics An update to using the docker was made below. However, I still haven't been able to access the cameras (CSI & Webcam) even when using --privileged. Outside the docker works great and inside the docker is recognized, yet no able to access it and output frame. What are some possibilities I can try and take? sudo docker run -it --ipc=host --runtime=nvidia --gpus all --device /dev/video0:/dev/video0 --device /dev/video1:/dev/video1 -v /path/to/code:/usr/src/ultralytics/myProg -v /path/to/Models:/usr/src/ultralytics/Models --privileged ultralytics/ultralytics:latest-jetson-jetpack4

[@Ultralytics]

It sounds like you're on the right track! Here are a few things you can try: 1. Check Permissions: Ensure the Docker container has the necessary permissions to access the camera devices. You can try running the container with `--privileged` and `--device` flags, as you did. 2. Install Dependencies: Make sure all necessary dependencies for camera access are installed inside the Docker container. You might need to install additional packages like `v4l-utils` or `opencv-python`. 3. Verify Camera Access: Inside the Docker container, verify that the cameras are accessible by listing the video devices: ```sh ls /dev/video* ``` 4. Test with Simple Script: Run a simple OpenCV script inside the container to check if the camera feed can be accessed: ```python import cv2 cap = cv2.VideoCapture(0) ret, frame = cap.read() if ret: cv2.imshow('frame', frame) cv2.waitKey(0) cap.release() cv2.destroyAllWindows() ``` 5. Docker Logs: Check Docker logs for any errors related to camera access: ```sh docker logs ``` For more detailed setup, refer to our Docker Quickstart Guide: docs.ultralytics.com/guides/docker-quickstart/. If the issue persists, consider checking NVIDIA forums for Jetson-specific Docker configurations. Good luck! 🚀

[@gptGNT]

Can’t this be done in an Anaconda environment on Jetson??

[@Ultralytics]

Yes, you can set up an Anaconda environment on NVIDIA Jetson for Ultralytics projects. Conda provides isolated environments, which are great for managing dependencies. Check out our Conda Quickstart Guide docs.ultralytics.com/guides/conda-quickstart/ for detailed steps on setting up and using Conda with Ultralytics. 😊

[@manhmit829]

what about jetpack 4.x ??

[@Ultralytics]

JetPack 4.x is compatible with older NVIDIA Jetson devices. For setting up Ultralytics YOLOv8, ensure your JetPack version supports the necessary libraries. You might need to adjust some dependencies. Check out our guide for more details: docs.ultralytics.com/guides/nvidia-jetson/ 🚀

[@WhatTiMe]

Thx

[@Ultralytics]

You're welcome! 😊 Let me know if you have more questions!

[@AsadKhan-js5le]

It runs without GPU. How to run yolo v8 on jetson nano with GPU

[@Ultralytics]

Hi there! To run YOLOv8 on your Jetson Nano with GPU, make sure you have the latest versions of `torch` and `ultralytics` installed. You can follow our detailed guide here: docs.ultralytics.com/guides/nvidia-jetson/. Also, ensure you enable MAX Power Mode with `sudo nvpmodel -m 0` and set the clocks to max with `sudo jetson_clocks`. If you encounter any specific issues, please share more details or error messages. Happy coding! 🚀