# 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 VersionNVIDIA Framework [Container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch)NVIDIA Framework [Wheel](https://developer.download.nvidia.com/compute/redist/jp/)JetPack Version
[2.8.0a0+5228986c39](https://github.com/pytorch/pytorch/commit/5228986c395dc79f90d2a2b991deea1eef188260)25.06-6.2
[2.8.0a0+5228986c39](https://github.com/pytorch/pytorch/commit/5228986c395dc79f90d2a2b991deea1eef188260)25.05-6.2
[2.7.0a0+79aa17489c](https://github.com/pytorch/pytorch/commit/79aa17489c3fc5ed6d5e972e9ffddf73e6dd0a5c)25.04-6.2
[2.7.0a0+7c8ec84dab](https://github.com/pytorch/pytorch/commit/7c8ec84dab7dc10d4ef90afc93a49b97bbd04503)25.03-6.2
[2.7.0a0+6c54963f75](https://github.com/pytorch/pytorch/commit/6c54963f75e9dfdae34c44f71081b5d3972b6b8d)25.02-6.2
[2.6.0a0+ecf3bae40a](https://github.com/pytorch/pytorch/commit/ecf3bae40a6f2f0f3b237bde1fc4b2492765ab13)25.01-6.1
[2.6.0a0+df5bbc09d1](https://github.com/pytorch/pytorch/commit/df5bbc09d191fff3bdb592c184176e84669a7157)24.12-6.1
[2.6.0a0+df5bbc0](https://github.com/pytorch/pytorch/commit/df5bbc09d191fff3bdb592c184176e84669a7157)24.11-6.1
[2.5.0a0+e000cf0ad9](https://github.com/pytorch/pytorch/commit/e000cf0ad980e5d140dc895a646174e9b945cf26)24.10-6.1
[2.5.0a0+b465a5843b](https://github.com/pytorch/pytorch/commit/b465a5843b92f33fe3e89ff7ee91c6833df6aec0)24.0924.096.1
[2.5.0a0+872d972e41](https://github.com/pytorch/pytorch/commit/872d972e41596a9ac94dfd343f40bfc12b340a74)24.08-6.0
[2.4.0a0+3bcc3cddb5](https://github.com/pytorch/pytorch/commit/3bcc3cddb580bf0f0f1958cfe27001f236eac2c1)24.0724.076.0
[2.4.0a0+f70bd71a48](https://github.com/pytorch/pytorch/commit/f70bd71a48)24.0624.066.0
[2.4.0a0+07cecf4168](https://github.com/pytorch/pytorch/commit/07cecf4168503a5b3defef9b2ecaeb3e075f4761)24.0524.056.0
[2.3.0a0+6ddf5cf85e](https://github.com/pytorch/pytorch/commit/6ddf5cf85e3c27c596175aba7bf5affb5426255f)24.0424.046.0 Developer Preview
[2.3.0a0+40ec155e58](https://github.com/pytorch/pytorch/commit/40ec155e58ee1a1921377ff921b55e61502e4fb3)24.03[24.03](https://developer.download.nvidia.com/compute/redist/jp/v60dp/pytorch/torch-2.3.0a0+40ec155e58.nv24.03.13384722-cp310-cp310-linux_aarch64.whl)
[2.3.0a0+ebedce2](https://github.com/pytorch/pytorch/commit/ebedce24ab578036dd9257e4928eea9ee38d1192)24.0224.02
[2.2.0a0+81ea7a4](https://github.com/pytorch/pytorch/commit/81ea7a48)23.12, 24.0123.12, 24.01
[2.2.0a0+6a974bec](https://github.com/pytorch/pytorch/commit/6a974bec)23.1123.11
[2.1.0a](https://github.com/pytorch/pytorch/commit/41361538a978eb03fa1e88bf5b8e4410db7a6927) 23.065.1.x
[2.0.0](https://github.com/pytorch/pytorch/tree/v2.0.0) 23.05
[2.0.0a0+fe05266f](https://github.com/pytorch/pytorch/commit/fe05266fda4f908130dea7cbac37e9264c0429a2) 23.04
[2.0.0a0+8aa34602](https://github.com/pytorch/pytorch/commit/8aa34602f703896c16ae57f622ff4cb1c86c04dd) 23.03
[1.14.0a0+44dac51c](https://github.com/pytorch/pytorch/commit/44dac51c36d01f63e64585e5e7a864cb8e37948a) 23.02, 23.01
[1.13.0a0+936e930](https://github.com/pytorch/pytorch/commit/936e930) 22.115.0.2
[1.13.0a0+d0d6b1f](https://github.com/pytorch/pytorch/commit/d0d6b1f) 22.09, 22,10
[1.13.0a0+08820cb](https://github.com/pytorch/pytorch/commit/08820cb)22.0722.07
[1.13.0a0+340c412](https://github.com/pytorch/pytorch/commit/340c412)22.0622.065.0.1
[1.12.0a0+8a1a93a9](https://github.com/pytorch/pytorch/commit/8a1a93a9)22.0522.055.0
[1.12.0a0+bd13bc66](https://github.com/pytorch/pytorch/commit/bd13bc66) 22.04
[1.12.0a0+2c916ef](https://github.com/pytorch/pytorch/commit/2c916ef) 22.03
[1.11.0a0+bfe5ad28](https://github.com/pytorch/pytorch/commit/bfe5ad28) 22.014.6.1
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 ```bash 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 ```bash 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 ```bash 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 ```bash 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。 ```python 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() ``` [![image.png](https://www.linkzeelabs.com/wiki/uploads/images/gallery/2025-07/scaled-1680-/bEUimage.png)](https://www.linkzeelabs.com/wiki/uploads/images/gallery/2025-07/bEUimage.png) 更多信息可参考[Ultralytics YOLO11 -Ultralytics YOLO 文档](https://docs.ultralytics.com/zh/models/yolo11/)