自用记录，还望读者海涵，谢谢！
conda clean -a

conda remove --name clam --all

conda env create -n clam -f E:\\CLAM\\CLAM_Code\\docs\\clam.yaml



wsl --list --verbose
wsl --status
wsl --shutdown
wsl --unregister Ubuntu-22.04


sudo cp  /etc/apt/sources.list /etc/apt/sources.list.bak
sudo vim /etc/apt/sources.list

# 默认注释了源码镜像以提高 apt update 速度，如有需要可自行取消注释
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy main restricted universe multiverse
# deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy-updates main restricted universe multiverse
# deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy-updates main restricted universe multiverse
deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy-backports main restricted universe multiverse
# deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy-backports main restricted universe multiverse

# deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy-security main restricted universe multiverse
# # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy-security main restricted universe multiverse

deb http://security.ubuntu.com/ubuntu/ jammy-security main restricted universe multiverse
# deb-src http://security.ubuntu.com/ubuntu/ jammy-security main restricted universe multiverse

# 预发布软件源，不建议启用
# deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy-proposed main restricted universe multiverse
# # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ jammy-proposed main restricted universe multiverse


sudo apt-get update
sudo apt-get upgrade


sudo apt install build-essential

https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/Anaconda3-2023.07-2-Linux-x86_64.sh
bash Anaconda3-2023.07-2-Linux-x86_64.sh



conda config --show-sources
sudo vim .condarc


channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch-lts: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  deepmodeling: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/

conda clean -i


conda install "conda-build!=3.26.0"

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia


在已经安装好jupyter notebook的前提下，

Alt+Ctrl+T打开终端，

先生成配置文件 :jupyter-notebook --generate-config

打开配置文件：code ~/.jupyter/jupyter_notebook_config.py（自己找到自己电脑的这个配置文件）

设置默认路径：（推荐写绝对路径),在c.NotebookApp.notebook_dir后面写上自己的工作环境路径（记得前面的#号去掉）

再次打开jupyter notebook默认的工作环境就变为自己设置的默认环境了。



import torch
print('CUDA版本:',torch.version.cuda)
print('Pytorch版本:',torch.__version__)
print('显卡是否可用:','可用' if(torch.cuda.is_available()) else '不可用')
print('显卡数量:',torch.cuda.device_count())
print('是否支持BF16数字格式:','支持' if (torch.cuda.is_bf16_supported()) else '不支持')
print('当前显卡型号:',torch.cuda.get_device_name())
print('当前显卡的CUDA算力:',torch.cuda.get_device_capability())
print('当前显卡的总显存:',torch.cuda.get_device_properties(0).total_memory/1024/1024/1024,'GB')
print('是否支持TensorCore:','支持' if (torch.cuda.get_device_properties(0).major >= 7) else '不支持')
print('当前显卡的显存使用率:',torch.cuda.memory_allocated(0)/torch.cuda.get_device_properties(0).total_memory*100,'%')


conda安装：
conda install matplotlib
conda install -c conda-forge opencv