zh/guides/deepstream-nvidia-jetson/

[giscus[bot]]

zh/guides/deepstream-nvidia-jetson/

了解如何使用TensorRT 和 DeepStream SDK 在NVIDIA Jetson 设备上部署Ultralytics YOLO11。探索性能基准，最大限度地提高人工智能能力。

https://docs.ultralytics.com/zh/guides/deepstream-nvidia-jetson/

[dao027]

do you have an implement of the bytetrack with deepstream?

[glenn-jocher]

@dao027 currently, there is no implementation of ByteTrack with DeepStream in our repository. However, you can explore DeepStream-Yolo for YOLO integrations with DeepStream, which might be adapted for tracking solutions.

[UltralyticsAssistant]

👋 Hello, thank you for your interest in Ultralytics 🚀! We recommend checking out the Chinese Documentation for guidance on using TensorRT and the DeepStream SDK for deploying Ultralytics YOLO11 on NVIDIA Jetson devices. This guide will help you explore performance benchmarks and enhance AI capabilities.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

An Ultralytics engineer will assist you soon. If you have specific queries or need further details, sharing more information about your setup and any challenges you're facing will be helpful.

For further community support, join us on Discord 🎧 for real-time conversations or participate in in-depth discussions on Discourse. You can also engage with others on our Subreddit.

Upgrade

Ensure you're using the latest ultralytics package with all requirements in a Python>=3.8 environment with PyTorch>=1.8 by upgrading:

pip install -U ultralytics

Environments

YOLO runs smoothly in these verified configurations (CUDA, CUDNN, Python, PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are passing. This ensures the correct operation of all YOLO Modes and Tasks across multiple platforms.

We're here to help! 😊