安装使用pytorch和torchvision

​​ PyTorch 是 Python 中最流行、最易用的深度学习框架之一。它让开发者能够像操作普通 Python 代码一样，直观、灵活地设计和训练复杂的神经网络模型。其简洁的 API 设计和强大的 GPU 加速支持，使得从研究想法到实际部署的开发过程都极其高效便捷，广受开发者青睐。​ 

 NVIDIA 为 Jetson 系列设备专门适配了对应的软件包，其版本依赖关系如下： 

 

 

 

 PyTorch Version 

 NVIDIA Framework  Container 

 NVIDIA Framework  Wheel 

 JetPack Version 

 

 

 

 

 2.8.0a0+5228986c39 

 25.06 

 - 

 6.2 

 

 

 2.8.0a0+5228986c39 

 25.05 

 - 

 6.2 

 

 

 2.7.0a0+79aa17489c 

 25.04 

 - 

 6.2 

 

 

 2.7.0a0+7c8ec84dab 

 25.03 

 - 

 6.2 

 

 

 2.7.0a0+6c54963f75 

 25.02 

 - 

 6.2 

 

 

 2.6.0a0+ecf3bae40a 

 25.01 

 - 

 6.1 

 

 

 2.6.0a0+df5bbc09d1 

 24.12 

 - 

 6.1 

 

 

 2.6.0a0+df5bbc0 

 24.11 

 - 

 6.1 

 

 

 2.5.0a0+e000cf0ad9 

 24.10 

 - 

 6.1 

 

 

 2.5.0a0+b465a5843b 

 24.09 

 24.09 

 6.1 

 

 

 2.5.0a0+872d972e41 

 24.08 

 - 

 6.0 

 

 

 2.4.0a0+3bcc3cddb5 

 24.07 

 24.07 

 6.0 

 

 

 2.4.0a0+f70bd71a48 

 24.06 

 24.06 

 6.0 

 

 

 2.4.0a0+07cecf4168 

 24.05 

 24.05 

 6.0 

 

 

 2.3.0a0+6ddf5cf85e 

 24.04 

 24.04 

 6.0 Developer Preview 

 

 

 2.3.0a0+40ec155e58 

 24.03 

 24.03 

 

 

 2.3.0a0+ebedce2 

 24.02 

 24.02 

 

 

 2.2.0a0+81ea7a4 

 23.12, 24.01 

 23.12, 24.01 

 

 

 2.2.0a0+6a974bec 

 23.11 

 23.11 

 

 

 2.1.0a 

   

 23.06 

 5.1.x 

 

 

 2.0.0 

   

 23.05 

 

 

 2.0.0a0+fe05266f 

   

 23.04 

 

 

 2.0.0a0+8aa34602 

   

 23.03 

 

 

 1.14.0a0+44dac51c 

   

 23.02, 23.01 

 

 

 1.13.0a0+936e930 

   

 22.11 

 5.0.2 

 

 

 1.13.0a0+d0d6b1f 

   

 22.09, 22,10 

 

 

 1.13.0a0+08820cb 

 22.07 

 22.07 

 

 

 1.13.0a0+340c412 

 22.06 

 22.06 

 5.0.1 

 

 

 1.12.0a0+8a1a93a9 

 22.05 

 22.05 

 5.0 

 

 

 1.12.0a0+bd13bc66 

   

 22.04 

 

 

 1.12.0a0+2c916ef 

   

 22.03 

 

 

 1.11.0a0+bfe5ad28 

   

 22.01 

 4.6.1 

 

 

 

 下面教程以 JetPack6.2.1 cuda12.6 版本为例 

 1.安装torch工具包 

 1.1下载并安装torch , torchvison 

 wget https://pypi.jetson-ai-lab.io/jp6/cu126/+f/62a/1beee9f2f1470/torch-2.8.0-cp310-cp310-linux_aarch64.whl 

wget https://pypi.jetson-ai-lab.io/jp6/cu126/+f/907/c4c1933789645/torchvision-0.23.0-cp310-cp310-linux_aarch64.whl

pip install torch-2.8.0-cp310-cp310-linux_aarch64.whl torchvision-0.23.0-cp310-cp310-linux_aarch64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple 

 1.2 检测是否正确安装 

 使用python执行下面三个语句 

 jetson@jetson-desktop:~$ python

Python 3.10.16 (main, Dec 11 2024, 16:18:56) [GCC 11.2.0] on linux

Type "help", "copyright", "credits" or "license" for more information.

>>> import torch

>>> print(torch.__version__)

2.8.0

>>> print(torch.cuda.is_available())

True 

 2. 运行YOLO11 

 YOLO 是一种实时目标检测算法，它将目标检测视为单阶段回归问题，通过将图像划分为网格并直接预测边界框与类别概率，实现高速且高精度的检测。YOLO系列因开源易用、部署灵活，广泛应用于自动驾驶、安防监控、工业质检等领域。 

 2.1 安装miniconda 

 curl -L https://repo.anaconda.com/miniconda/Miniconda3-py310_25.3.1-1-Linux-aarch64.sh | bash

source ~/miniconda3/bin/activate

conda --version   

 2.2 conda换源 

 conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/main/

conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/

conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/

conda config --add channels https://mirrors.ustc.edu.cn/anaconda/cloud/msys2/

conda config --set show_channel_urls yes 

 2.3创建conda环境 

 conda create -n jetson-ai python=3.10 

 2.4 进入conda环境 

 conda activate jetson-ai 

 2.5 安装torch和torchvison 

 wget https://pypi.jetson-ai-lab.io/jp6/cu126/+f/62a/1beee9f2f1470/torch-2.8.0-cp310-cp310-linux_aarch64.whl 

wget https://pypi.jetson-ai-lab.io/jp6/cu126/+f/907/c4c1933789645/torchvision-0.23.0-cp310-cp310-linux_aarch64.whl

pip install torch-2.8.0-cp310-cp310-linux_aarch64.whl torchvision-0.23.0-cp310-cp310-linux_aarch64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple 

 2.6 安装ultralytics 

 pip install ultralytics -i https://pypi.tuna.tsinghua.edu.cn/simple 

 2.7 运行摄像头视频推理例程 

 接入摄像头，并在上面创建的环境中运行如下程序。 

 import cv2

import time

from ultralytics import YOLO

from ultralytics import YOLOWorld

# Load the YOLO model

model = YOLO("yolo11s.pt")

# Open the video file

video_path = 0

cap = cv2.VideoCapture(video_path)

# Loop through the video frames

while cap.isOpened():

 

 # Read a frame from the video

 success, frame = cap.read()

 start = time.time()

 if success:

 # Run YOLO inference on the frame

 results = model(frame)

 inf_time = time.time() - start

 # Visualize the results on the frame

 annotated_frame = results[0].plot() 

 fps = 1.0 / inf_time if inf_time > 0 else 0

 # show FPS

 cv2.putText(annotated_frame, f"FPS: {fps:.2f}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,0), 2)

 cv2.imshow("YOLO Inference", annotated_frame)

 # Break the loop if 'q' is pressed

 if cv2.waitKey(1) & 0xFF == ord("q"):

 break

 else:

 # Break the loop if the end of the video is reached

 break

# Release the video capture object and close the display window

cap.release()

cv2.destroyAllWindows() 

 

 更多信息可参考 Ultralytics YOLO11 -Ultralytics YOLO 文档 

 3. 手动编译安装Pytorch/torchvison 

 某些项目可能需要指定的pytorch版本，若官方没有提供编译完成的whl文件，也可以按照下面步骤进行手动编译。