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Jetson Xavier/NX/nano快速安装PyTorch与torchVision的教程

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jetsonXavier
torchVison
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本篇文档是一份针对NVIDIA Jetson Xavier、NX和nano平台的详细安装指南，主要介绍如何在这些设备上安装PyTorch和torchvision库。Jetson系列，包括Jetson Xavier和Jetson NX，是专为边缘计算和人工智能应用设计的嵌入式开发板，而Jetson Nano则是更小型且入门级别的版本。 首先，值得注意的是，文档提供的资源来自于NVIDIA官方论坛，其中提到的是预构建的PyTorch pip wheel installers，这些安装包针对JetPack 4.2及更高版本，支持ARM aarch64架构，这意味着它们是为Jetson系列的处理器设计的，而非主机PC。提供的PyTorch版本涵盖了从v1.0.0到v1.7.0，用户可以根据自己的JetPack版本选择合适的安装包下载。 安装步骤分为两部分： 1. 下载PyTorch wheel安装文件：用户可以从提供的链接下载适合自己JetPack版本的PyTorch wheel文件，例如对于PyTorch v1.7.0，对应的下载链接是`https://nvidia.box.com/shared/static/wa34qwrwtk9njtyarwt5nvo6imenfy26.whl`，下载后保存为`torch-1.7.0-cp36-cp36m-linux_aarch64.whl`（或者其他版本对应的文件名）。 2. 安装依赖和执行安装命令：在Jetson设备上运行安装，需要先确保系统已经安装了基本的Python3依赖，如`python3-pip`, `libopenblas`, 和 `libopenmpi-dev`。然后，使用`pip3`命令来安装下载的wheel文件，安装命令示例如下： ``` Python3.6 wget <下载链接> sudo apt-get install python3-pip libopenblas-dev libopenmpi-dev pip3 install --no-index --find-links <保存wheel文件的路径> torch-1.7.0-cp36-cp36m-linux_aarch64.whl ``` 其中，`<下载链接>`和`<保存wheel文件的路径>`需要替换为实际的下载链接和文件路径。 完成以上步骤后，PyTorch及其相关的torchvision库就成功安装在了Jetson Xavier、NX或nano上，可以用于进行深度学习模型的开发、训练和推理任务。为了确保最佳性能和兼容性，建议在安装前检查设备的硬件配置和JetPack版本是否匹配，以及遵循官方文档中的任何特定注意事项。

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print(torch.__version__)` - `{CUDA_VERSION}`：CPU版本用`cpu`，GPU版本用`cu121`(CUDA12.1)等 示例（PyTorch 2.2.1 + CPU）： ```bash pip install torch-scatter -f ...
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print(torch_geometric.__version__) ``` 如果版本低于2.0，可能就会有问题。因为引用[3]指出PyG >=2.0才支持某些新API。所以，解决办法应该是升级PyG到最新版本。 另外，安装PyG可能需要依赖库，比如torch-scatter...
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Traceback (most recent call last): File "D:\LHH_BSYJ\LP-5\ControlSCD\models\Step_1.py", line 10, in <module> from torch_geometric.nn import GCNConv File "D:\Anaconda3\envs\LINHAIHAN\lib\site-packages\torch_geometric\__init__.py", line 2, in <module> import torch_geometric.nn File "D:\Anaconda3\envs\LINHAIHAN\lib\site-packages\torch_geometric\nn\__init__.py", line 2, in <module> from .data_parallel import DataParallel File "D:\Anaconda3\envs\LINHAIHAN\lib\site-packages\torch_geometric\nn\data_parallel.py", line 5, in <module> from torch_geometric.data import Batch File "D:\Anaconda3\envs\LINHAIHAN\lib\site-packages\torch_geometric\data\__init__.py", line 1, in <module> from .data import Data File "D:\Anaconda3\envs\LINHAIHAN\lib\site-packages\torch_geometric\data\data.py", line 8, in <module> from torch_sparse import coalesce, SparseTensor File "D:\Anaconda3\envs\LINHAIHAN\lib\site-packages\torch_sparse\__init__.py", line 13, in <module> library, [osp.dirname(__file__)]).origin) File "D:\Anaconda3\envs\LINHAIHAN\lib\site-packages\torch\_ops.py", line 573, in load_library ctypes.CDLL(path) File "D:\Anaconda3\envs\LINHAIHAN\lib\ctypes\__init__.py", line 356, in __init__ self._handle = _dlopen(self._name, mode) OSError: [WinError 127] 找不到指定的程序。

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from torch_geometric.nn import GCNConv中的torch_geometric.nn是什么意思

torch_geometric.nn是PyTorch的一个库，它提供了一些用于图神经网络的常见层和实用程序，例如GCNConv。GCNConv是一个基于图卷积神经网络的卷积层，用于对图数据进行学习和预测。
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