sudo passwd root

sudo visudo

[user_name] ALL=(ALL) NOPASSWD: ALL

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

sudo vim /etc/apt/sources.list

 deb http://mirrors.aliyun.com/ubuntu/ focal main restricted universe multiverse
 deb-src http://mirrors.aliyun.com/ubuntu/ focal main restricted universe multiverse
 deb http://mirrors.aliyun.com/ubuntu/ focal-security main restricted universe multiverse
 deb-src http://mirrors.aliyun.com/ubuntu/ focal-security main restricted universe multiverse
 deb http://mirrors.aliyun.com/ubuntu/ focal-updates main restricted universe multiverse
 deb-src http://mirrors.aliyun.com/ubuntu/ focal-updates main restricted universe multiverse
 deb http://mirrors.aliyun.com/ubuntu/ focal-proposed main restricted universe multiverse
 deb-src http://mirrors.aliyun.com/ubuntu/ focal-proposed main restricted universe multiverse
 deb http://mirrors.aliyun.com/ubuntu/ focal-backports main restricted universe multiverse
 deb-src http://mirrors.aliyun.com/ubuntu/ focal-backports main restricted universe multiverse

sudo apt update
sudo apt upgrade

sudo apt install python3
sudo apt install python3-pip

cd ~
mkdir .pip

sudo vim ~/.pip/pip.conf

[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple/ 
[install]
trusted-host = pypi.tuna.tsinghua.edu.cn

sudo rm /usr/bin/python

sudo ln -s /usr/bin/python3 /usr/bin/python
sudo ln -s /usr/bin/pip3 /usr/bin/pip

sudo apt install ssh

sudo apt install xrdp 

sudo systemctl status xrdp

sudo adduser xrdp ssl-cert  
sudo systemctl restart xrdp

tar -zxvf frp_0.34.3_linux_amd64.tar.gz

 [common]
 bind_port = 7000 # frp服务的端口号，可以自己定
 dashboard_port = 7500 # frp的web界面的端口号
 dashboard_user = user # web界面的登陆账户，自己修改
 dashboard_pwd = pass # web界面的登陆密码，自己修改
 authentication_method = token
 token = xxxxx # frp客户端连接时的密码，自己修改

 ./frps -c ./frps.ini

 [common]
 server_addr = xx.xx.xx.xx # 你的云服务器的公网ip
 authentication_method = token
 token = xxxxx # 刚刚配置的frp连接密码 
 server_port = 7000 # 刚刚配置的frp服务端口
 
 [Fusion-ssh]
 type = tcp
 local_ip = 127.0.0.1
 local_port = 22
 remote_port = 20022
 
 [Fusion-rdp]
 type = tcp
 local_ip = 127.0.0.1
 local_port = 3389
 remote_port = 23389

 [Fusion]
 Description=Frp Server Daemon
 After=syslog.target network.target
 Wants=network.target
 
 [Service]
 Type=simple
 ExecStart=/usr/local/bin/frp/frpc -c /usr/local/bin/frp/frpc.ini # 修改为你的frp实际安装目录
 ExecStop=/usr/bin/killall frpc
 #启动失败1分钟后再次启动
 RestartSec=1min
 KillMode=control-group
 #重启控制：总是重启
 Restart=always
 
 [Install]
 WantedBy=multi-user.target

sudo systemctl enable frpc.service
sudo systemctl start frpc.service

sudo systemctl status frpc.service

sudo apt-get install samba samba-common-bin

sudo nano /etc/samba/smb.conf

# 共享文件夹显示的名称
[home]
# 说明信息
comment = Fusion WorkStation Storage
# 可以访问的用户
valid users = [user_name],root
# 共享文件的路径
path = /home/[user_name]/
# 可被其他人看到资源名称（非内容）
browseable = yes
# 可写
writable = yes
# 新建文件的权限为 664
create mask = 0664
# 新建目录的权限为 775
directory mask = 0775

sudo smbpasswd -a pi

sudo /etc/init.d/samba-ad-dc restart

sudo apt update
sudo apt upgrade

 sudo apt-get remove --purge nvidia*

chmod +x cuda_11.0.2_450.51.05_linux.run
sudo sh ./cuda_11.0.2_450.51.05_linux.run

nano  ~/.bashrc

export CUDA_HOME=/usr/local/cuda-11.0
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64
export PATH=${CUDA_HOME}/bin:${PATH}

source ~/.bashrc

cd ~/Softwares/cuda/NVIDIA_CUDA-11.0_Samples/1_Utilities/deviceQuery
 make
./deviceQuery

 tar -xzvf cudnn-11.0-linux-x64-v8.0.5.39.tgz

 sudo cp cuda/lib64/* /usr/local/cuda-11.0/lib64/
 sudo cp cuda/include/* /usr/local/cuda-11.0/include/

 cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2

chmod +x Anaconda3-2020.11-Linux-x86_64.sh

./Anaconda3-2020.11-Linux-x86_64.sh

#uninstall old docker version
sudo apt-get remove docker docker-engine docker.io containerd runc
sudo apt-get update
sudo apt-get install \
	ca-certificates \
	curl \
	gnupg
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg

echo \
  "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
  "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
  
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
# verify
sudo docker run hello-world

docker version

##首先要确保已经安装了nvidia driver
# 1. 添加源
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# 2. 安装并重启
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

sudo docker run -it --name test_nvidia_docker --gpus all nvidia/cuda:12.1.0-base-ubuntu20.04

 conda create --name python_38-pytorch_1.7.0 python=3.8

 conda activate python_38-pytorch_1.7.0

 which pip

#cu118/torch-2.0.0%2Bcu118-cp38-cp38-linux_x86_64.whl
#cu118/torchvision-0.15.1%2Bcu118-cp39-cp39-linux_x86_64.whl
#cu118/torchaudio-2.0.1%2Bcu118-cp38-cp38-linux_x86_64.whl

pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1+cu118 -f https://download.pytorch.org/whl/torch_stable.html 

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torchvision import datasets, transforms


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)


    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


def train(model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 10 == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                       100. * batch_idx / len(train_loader), loss.item()))


def main():
    cudnn.benchmark = True
    torch.manual_seed(1)
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    print("Using device: {}".format(device))
    kwargs = {'num_workers': 1, 'pin_memory': True}
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('./data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=64, shuffle=True, **kwargs)

    model = Net().to(device)
    optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

    for epoch in range(1, 11):
        train(model, device, train_loader, optimizer, epoch)

if __name__ == '__main__':
    main()

 sudo docker run  -it  --name train_mnist \
 -v /etc/timezone:/etc/timezone \
 -v /etc/localtime:/etc/localtime \
 -v /home/[user_name]/WorkSpace/_share:/home/workspace/_share  \
 --gpus all nvidia/cuda:12.1.0-base-ubuntu20.04

tar -cvpzf ubuntu_backup@`date +%Y-%m+%d`.tar.gz --exclude=/proc --exclude=/tmp --exclude=/boot  --exclude=/lost+found --exclude=/media --exclude=/mnt --exclude=/run /

sudo su

# 在tryUbuntu根目录下有media文件夹，里面是U盘文件夹和新安装的系统文件夹，在在里分别用（U盘）和（UBUNTU）表示
cd /media/（U盘）
mount -o remount rw ./
 
# 将新系统根目录下/boot/grub/grub.cfg文件备份到U盘中
sudo cp /media/(Ubuntu)/boot/grub/grub.cfg ./    
 
# 将新系统根目录下/etc/fstab文件备份到U盘中，fstab是与系统开机挂载有关的文件，grub.cfg是与开机引导有关的文件，所以这一步至关重要
sudo cp /media/(UBUNTU)/etc/fstab ./
 
# 这一步删除新装ubuntu全部的系统文件，有用的fstab及grub.cfg已经备份
cd /media/(UBUNTU)
sudo rm -rf ./*
 
# 将U盘中backup.tgz复制到该目录下并解压缩
cp /media/(U盘)/backup.tgz ./
sudo tar xvpfz backup.tgz ./
 
# 创建打包系统时排除的文件
sudo mkdir proc lost+found mnt sys media