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

In this video, we will guide you through the comprehensive process of setting up NVIDIA Jetson devices, including an in-depth look at installing the Ultralytics package on these devices. We will cover the step-by-step installation procedures and delve into the specifics of configuring the environment for optimal performance. Additionally, we will provide an overview of Ultralytics YOLOv8 models benchmark across different formats, including PyTorch, TorchScript, and TensorRT, providing detailed comparisons and insights to help you understand the performance implications of each format on Nvidia Jetson.
Learn more ➡️ docs.ultralytics.com/guides/n...
Key highlights ❤️
0:00 - Introduction
0:45 - Nvidia Jetson Nano Documentation
1:05 - What is NVIDIA Jetson?
1:34 - Nvidia Jetson Series Comparison
2:58 - What is NVIDIA JetPack?
3:31 - How to Flash JetPack to NVIDIA Jetson?
3:50 - Setup Ultralytics for NVIDIA Jetson
3:52 - Setup Ultralytics in the Docker Environment
4:02 - Setup Ultralytics without Docker
4:22 - Importance of OnnxRuntime on Jetson Devices
5:21 - Importance of TensorRT on NVIDIA Jetson
6:03 - Ultralytics YOLOv8 Benchmarks on NVIDIA Jetson Orin
6:15 - Comparison Chart with PyTorch, TorchScript, and TensorRT Models
7:17 - Detailed Comparison Table
8:26 - Reproduce the Benchmarks
8:49 - Best Practices When Using NVIDIA Jetson
9:37 - Summary and Conclusion
Ultralytics ⚡ resources
- About Us - ultralytics.com/about
- Join Our Team - ultralytics.com/work
- Contact Us - ultralytics.com/contact
- Discord - / discord
- Ultralytics License - ultralytics.com/license
YOLOv8 🚀 resources
- GitHub - github.com/ultralytics/ultral...
- Docs - docs.ultralytics.com/

[@o7s-EmilyW]

This walkthrough is quite illuminating! Could you delve a bit deeper into the potential trade-offs when choosing between PyTorch and TensorRT models on lesser-powered Jetson devices, especially in real-time applications such as smart surveillance or autonomous drones? I sense there's a lot more beneath the surface regarding performance versus accuracy in diverse operational scenarios.

[@m033372]

Great walkthrough! How does the performance and accuracy of YOLOv8 on NVIDIA Jetson compare to using it on more traditional hardware like GPUs or CPUs? Are there any specific challenges or advantages that come with the Jetson setup?

[@Ultralytics]

Thank you for your kind words! 😊 YOLOv8 on NVIDIA Jetson offers impressive performance, especially with TensorRT optimizations, but it may not match the raw power of high-end GPUs. Jetson devices shine in edge applications due to their low power consumption and compact size. For detailed benchmarks and comparisons, check out our NVIDIA Jetson Guide docs.ultralytics.com/guides/nvidia-jetson/. If you have specific performance metrics or scenarios in mind, feel free to share more details!

[@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! 😊

[@m033372]

Can you explain the different Jetson nano versions, because I think now there are Jetpack4 and Jetpack5 versions right? What's the difference and what are the price points of each?

[@Ultralytics]

Hi! Great question! Yes, there are different versions of the Jetson Nano, primarily distinguished by the JetPack versions they support. JetPack 4.x is for older models like the Jetson Nano 4GB, while JetPack 5.x supports newer models like the Jetson Orin Nano. JetPack 5.x brings improved AI performance and support for the latest software features. For detailed specs and pricing, you can check out the official NVIDIA Jetson page. If you need more info on setting up YOLOv8 on these devices, our documentation docs.ultralytics.com/guides/nvidia-jetson/ has you covered! 🚀

[@LunaStargazer-v1s]

So, here we find ourselves batting in the twilight of advanced tech-I've got to ask, is there a significant trade-off in real-time performance when using TorchScript versus TensorRT for intensive applications on Jetson devices? And on a more whimsical note, will my Jetson be able to keep up with my daydreams of building a personal AI assistant that whispers poetic musings into my ear?

[@Ultralytics]

Great questions! 🌟
For real-time performance on Jetson devices, TensorRT generally offers superior speed and efficiency compared to TorchScript, especially for intensive applications. TensorRT optimizes the model for the specific hardware, leading to faster inference times. You can check out more details here: NVIDIA Jetson Guide docs.ultralytics.com/guides/nvidia-jetson/.
As for your poetic AI assistant, your Jetson can certainly handle it! With the right optimizations and models, you can create an AI that not only whispers poetic musings but also does so efficiently. Happy building! 🚀

[@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! 🚀

[@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! 🚀