在Jetson nano上搭建深度学习环境（Anoconda和pytorch安装）

1.Miniforge安装

1.安装bash文件

bash Miniforge-pypy3-4.9.0-3-Linux-aarch64.sh

完成后如图：

2.创建自己的环境，命名为nano

conda create -n nano python=3.6

2.pytorch安装

1.激活环境

2.依次执行以下几个命令，pytorch版本视自己情况而定

wget https://nvidia.box.com/shared/static/p57jwntv436lfrd78inwl7iml6p13fzh.whl -O torch-1.8.0-cp36-cp36m-linux_aarch64.whl
sudo apt-get install python3-pip libopenblas-base libopenmpi-dev 
pip3 install Cython
pip3 install numpy torch-1.8.0-cp36-cp36m-linux_aarch64.whl

完成后检查一下

python3

import torch

如下图，无报错则安装正确，若出现core error则关闭terminal，重新打开即可

3.安装torchvision

注意替换为自己的torchvision版本

sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev
git clone --branch <version> https://github.com/pytorch/vision torchvision   # see below for version of torchvision to download
cd torchvision
export BUILD_VERSION=0.x.0  # where 0.x.0 is the torchvision version  
python3 setup.py install --user
cd ../  # attempting to load torchvision from build dir will result in #import error
pip install 'pillow<7' # always needed for Python 2.7, not needed torchvision v0.5.0+ with Python 3.6

import torchvision检查一下：

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完结撒花！！！