sudo apt install ffmpeg

# 方案1：直接pip安装
# 指定特定commit，安装适合python3.7的版本
pip3.7 install 'git+https://github.com/facebookresearch/detectron2.git@d779ea63faa54fe42b9b4c280365eaafccb280d6'

# 方案2：手动从源码构建安装
# 如果网络不佳，直接访问网页下载压缩包：https://github.com/facebookresearch/detectron2/tree/d779ea63faa54fe42b9b4c280365eaafccb280d6
# zip包链接：https://github.com/facebookresearch/detectron2/archive/d779ea63faa54fe42b9b4c280365eaafccb280d6.zip
git clone -b d779ea63faa54fe42b9b4c280365eaafccb280d6 https://github.com/facebookresearch/detectron2.git
python3.7 -m pip install -e detectron2

# 如果VideoPose3D目录下没有checkpoint文件夹的话，先mkdir checkpoint
cd checkpoint
wget https://dl.fbaipublicfiles.com/video-pose-3d/pretrained_h36m_detectron_coco.bin

cd inference
python3.7 infer_video_d2.py \
    --cfg COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml \
    --output-dir output_directory \
    --image-ext mp4 \
    input_directory

cd data
python3.7 prepare_data_2d_custom.py -i /home/nvidia/VideoPose3D/inference/output_directory/ -o myvideos
cd ..

python3.7 run.py -d custom -k myvideos -arc 3,3,3,3,3 -c checkpoint --evaluate pretrained_h36m_detectron_coco.bin --render --viz-subject test_video.mp4 --viz-action custom --viz-camera 0 --viz-video /home/nvidia/VideoPose3D/inference/input_directory/test_video.mp4 --viz-output output.mp4 --viz-size 6
# 下面输入视频路径是绝对路径
# python3.7 run.py -d custom -k myvideos -arc 3,3,3,3,3 -c checkpoint --evaluate pretrained_h36m_detectron_coco.bin --render --viz-subject test_video.mp4 --viz-action custom --viz-camera 0 --viz-video /home/nvidia/VideoPose3D/inference/input_directory/test_video.mp4 --viz-output output.mp4 --viz-size 6

data/
  └── h36m/
    ├── Poses_D3_Positions_S1.tgz
    ├── Poses_D3_Positions_S5.tgz
    ├── Poses_D3_Positions_S6.tgz
    ├── Poses_D3_Positions_S7.tgz
    ├── Poses_D3_Positions_S8.tgz
    ├── Poses_D3_Positions_S9.tgz
    └── Poses_D3_Positions_S11.tgz

# 解压数据集：
cd /home/nvidia/data/h36m/
for file in *.tgz; do tar -xvzf $file; done

cd /home/nvidia/data
pip3.7 install cdflib
python3.7 prepare_data_h36m.py --from-source-cdf /home/nvidia/data/h36m/
# 运行成功后，data目录下生成两个文件：data_3d_h36m.npz和data_2d_h36m_gt.npz。

mkdir checkpoint
cd checkpoint
wget https://dl.fbaipublicfiles.com/video-pose-3d/pretrained_h36m_cpn.bin
wget https://dl.fbaipublicfiles.com/video-pose-3d/pretrained_humaneva15_detectron.bin
cd ..

python run.py -k cpn_ft_h36m_dbb -arc 3,3,3,3,3 -c checkpoint --evaluate pretrained_h36m_cpn.bin

pip3.7 install TensorboardX Tensorboard_logger

cd HRFAE
nano test.py
# 找到下面该行代码，将load函数，改成safe_load或者full_load
# config = yaml.load(open('./configs/' + opts.config + '.yaml', 'r'))

# 科学网络条件好的，可以直接参考官方命令下载checkpoint
cd ./logs/001
./download.sh
# 如果网络条件不行的话，直接使用链接下载后在传入logs目录，注意上传后记得修改属性权限755或者777
# https://partage.imt.fr/index.php/s/7DzqFHQggfJDw79/download

cd HRFAE
# 年龄区间：20-70
python3.7 test.py --config 001 --target_age 40

# 查看虚拟内存使用分配机制，vm.swappiness = 0：最大限度使用物理内存，vm.swappiness = 100：积极的使用交换空间
sysctl -a| grep vm.swappiness
# vm.swappiness = 60
# 临时设置swappiness
echo 10 > /proc/sys/vm/swappiness
# 查看内存使用率高的进程id
top
# 对某个进程禁用系统OOM killer，$PID替换为进程ID数字即可
echo -17 > /proc/$PID/oom_adj

# 查看系统内核日志中最后7行
sudo dmesg | tail -7

# 通过以下三种命令查看系统是否主动杀死程序进程
# 将dmesg命令的输出通过管道传递给egrep命令进行过滤。egrep命令使用正则表达式'killed process'来匹配包含"killed process"的行，并且使用-i选项忽略大小写。-B100选项会在匹配到的行之前显示100行的上下文。
dmesg | egrep -i -B100 'killed process'
# 在/var/log/messages文件中搜索包含"killed process"的行，并且使用-i选项忽略大小写
egrep -i 'killed process' /var/log/messages
# 在/var/log目录及其子目录下递归地搜索包含"killed process"的行，并且使用-i选项忽略大小写
egrep -i -r 'killed process' /var/log
# 将journalctl -xb命令的输出通过管道传递给egrep命令进行过滤。journalctl -xb命令用于查看系统启动时的日志，并且使用-i选项忽略大小写
journalctl -xb | egrep -i 'killed process'

# 如果未发现/var/log/messages文件，编辑50-default.conf文件，取消注释一行内容
nano /etc/rsyslog.d/50-default.conf
#       mail,news.none          -/var/log/messages

conda create -n hrfae python=3.7
conda activate hrfae

# 安装必要库
sudo apt-get install qemu-user-static
sudo apt-get install python
# j为使用bzip2算法，x为解压，v为显示操作的详细信息，f为指定操作的文件
sudo tar -jxvf Jetson_Linux_R32.7.4_aarch64.tbz2
cd Linux_for_Tegra/rootfs
# p为于保留文件的权限和属性，即保持原有文件的权限模式和时间戳信息
sudo tar -jxpf ../../Tegra_Linux_Sample-Root-Filesystem_R32.7.4_aarch64.tbz2
cd ..
# 拷贝NVIDIA库文件到文件系统
sudo ./apply_binaries.sh
cd Linux_for_Tegra/tools
# u为用户名，p为密码
sudo ./l4t_create_default_user.sh -u nvidia -p nvidia
# NVIDIA Jetson是通过Micro USB接口烧录系统。Recovery模式下可以进行文件系统更新包含：内核Kernel，启动Bootloader，文件系统Rootfs等。
# 进入Recovery模式的步骤：
# 1）使用Micro USB 的数据线连接Jetson和Ubuntu Host主机(一端插在开发板的OTG口,一端插在Ubuntu Host主机的usb插口)；
# 2）连接开发板的电源;
# 3) 按住RECOVERY按键，同时按下RESET复位按键1-2秒后释放，等待3秒释放RECOVERY按键，此时开发板进入Recovery刷机模式 （可通过在Ubuntu Host主机上运行命令：lsusb查看是否有Nvidia Corporation 设备（不同Jetson模块USB VID/PID 不同）来确认是否进入正常）
# 执行刷机命令
cd Linux_for_Tegra
sudo ./flash.sh jetson-xavier-nx-devkit-tx2-nx mmcblk0p1

sudo apt-get install nvidia-l4t-x11
sudo reboot

# 切换到root模式，并保留当前终端环境
sudo -i
apt-get update
dpkg --configure -a
apt-get dist-upgrade
apt-get -f install
reboot

cd rootOnNVMe
# 这个过程耗时较久，请耐心等待执行完毕再执行下一步
# 建议是刚烧录好系统之后就执行这一步转换，否则EMMC数据一旦很多的话，这步操作的转换过程十分漫长
./copy-rootfs-ssd.sh
./setup-service.sh
sudo reboot

# 备份/etc/lib路径下的source.list文件，
sudo cp /etc/apt/sources.list /etc/apt/sources.list.bak
# i进入编辑模式。按esc键退出模式，先按gg（到达顶部），然后dG全部删除。:wq保存编辑并退出vi。
sudo vim /etc/apt/sources.list
# 编辑软件源后，更新拉取源表
sudo apt update

deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial-updates main restricted universe multiverse
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial-updates main restricted universe multiverse
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial-security main restricted universe multiverse
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial-security main restricted universe multiverse
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial-backports main restricted universe multiverse
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial-backports main restricted universe multiverse
deb http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial main universe restricted
deb-src http://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ xenial main universe restricted

deb http://ports.ubuntu.com/ubuntu-ports/ bionic main restricted
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic main restricted
deb http://ports.ubuntu.com/ubuntu-ports/ bionic-updates main restricted
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-updates main restricted
deb http://ports.ubuntu.com/ubuntu-ports/ bionic universe
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic universe
deb http://ports.ubuntu.com/ubuntu-ports/ bionic-updates universe
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-updates universe
deb http://ports.ubuntu.com/ubuntu-ports/ bionic multiverse
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic multiverse
deb http://ports.ubuntu.com/ubuntu-ports/ bionic-updates multiverse
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-updates multiverse
deb http://ports.ubuntu.com/ubuntu-ports/ bionic-backports main restricted universe multiverse
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-backports main restricted universe multiverse
# deb http://archive.canonical.com/ubuntu bionic partner
# deb-src http://archive.canonical.com/ubuntu bionic partner
deb http://ports.ubuntu.com/ubuntu-ports/ bionic-security main restricted
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-security main restricted
deb http://ports.ubuntu.com/ubuntu-ports/ bionic-security universe
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-security universe
deb http://ports.ubuntu.com/ubuntu-ports/ bionic-security multiverse
deb-src http://ports.ubuntu.com/ubuntu-ports/ bionic-security multiverse

# 通过ssh连接到开发板系统，通过winscp上传python源码压缩包到开发板的家目录
tar -xf Python-3.7.16.tar.xz
# 安装构建pyhon所需的包，使用清华源的话，安装libncurses5库可能会出现库依赖错误，这个需要用ubuntu官方源（bionic源）才能解决，其他库可以用清华源
sudo apt install build-essential zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libreadline-dev libffi-dev libbz2-dev liblzma-dev
# 或者build-dep参数让ubuntu自动搜索相关包。使用这个命令，记得apt list自行核对下其他额外包有无安装好，参照「系统缺乏库的一些报错」小节。
# 如果build-dep报错：Error :: You must put some 'source' URIs in your sources.list，请启用/etc/apt/sources.list文件中的deb-src源，即取消注释。修改完成后重新运行sudo apt update。
sudo apt build-dep python3
# prefix配置安装路径，方便卸载时直接删除prefix的路径即可
./configure --prefix=/usr/local/python3.7

# 假设使用conda创建虚拟环境的话，可以用以下方式配置安装路径
# 安装到miniconda环境（base）
./configure --prefix=/root/miniconda3/lib/python3.7

# npro查看处理器核心数，lscpu查看处理器详情，-j无参为使用所有核心，后面跟数字就是并行编译数量
make -j
# 安装python
sudo make install
cd /usr/bin/
# 创建python3.7和pip3.7软链接
sudo ln -s /usr/local/python3.7/bin/python3.7 python3.7
sudo ln -s /usr/local/python3.7/bin/pip3.7 pip3.7
# 配置pip源
pip3.7 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
# export bin目录到path
export PATH=$PATH:/usr/local/python3.7/bin

mkdir ~/.pip
cd ~/.pip
touch pip.conf
nano pip.conf

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

export OPENBLAS_CORETYPE=ARMV8

sudo apt install nvidia-jetpack
sudo jetson_release

The jetson_stats.service is not active. Please run:
sudo systemctl restart jetson_stats.service

# 刷新bash配置，普通账户nvidia的终端下执行该命令
source ~/.bashrc
# 切换root权限
sudo -i
# 切换root权限后，工作目录会自动转到root的家目录下
nano .bashrc
# 将以下两条命令添加到.bashrc的底部
export PATH=/usr/local/cuda-10.2/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
source .bashrc

# sdk-manager安装路径
# cuDNN 默认安装路径在 /usr/lib/aarch64-linux-gnu
# CUDA 默认安装路径在 /usr/local/cuda 下
# cuDNN的头文件在：/usr/include，库文件位于：/usr/lib/aarch64-linux-gnu

# 复制文件到cuda目录下
cd /usr/include && sudo cp cudnn* /usr/local/cuda-10.2/include
cd /usr/lib/aarch64-linux-gnu && sudo cp libcudnn* /usr/local/cuda-10.2/lib64

# 修改文件权限，修改复制完的头文件与库文件的权限，所有用户都可读，可写，可执行：
sudo chmod 777 /usr/local/cuda-10.2/include/cudnn.h
sudo chmod 777 /usr/local/cuda-10.2/lib64/libcudnn*

# 软链接移动之后就失效了，需重新软链接，这里的8.2.1和8对应安装的cudnn版本号和首数字
cd /usr/local/cuda-10.2/lib64

sudo ln -sf libcudnn.so /etc/alternatives/libcudnn_so
sudo ln -sf libcudnn.so.8.2.1 libcudnn.so.8

sudo ln -sf libcudnn_ops_train.so /etc/alternatives/libcudnn_ops_train_so
sudo ln -sf libcudnn_ops_train.so.8.2.1 libcudnn_ops_train.so.8
sudo ln -sf libcudnn_ops_infer.so /etc/alternatives/libcudnn_ops_infer_so
sudo ln -sf libcudnn_ops_infer.so.8.2.1 libcudnn_ops_infer.so.8

sudo ln -sf libcudnn_adv_train.so /etc/alternatives/libcudnn_adv_train_so
sudo ln -sf libcudnn_adv_train.so.8.2.1 libcudnn_adv_train.so.8
sudo ln -sf libcudnn_adv_infer.so /etc/alternatives/libcudnn_adv_infer_so
sudo ln -sf libcudnn_adv_infer.so.8.2.1 libcudnn_adv_infer.so.8

sudo ln -sf libcudnn_cnn_train.so /etc/alternatives/libcudnn_cnn_train_so
sudo ln -sf libcudnn_cnn_train.so.8.2.1 libcudnn_cnn_train.so.8
sudo ln -sf libcudnn_cnn_infer.so /etc/alternatives/libcudnn_cnn_infer_so
sudo ln -sf libcudnn_cnn_infer.so.8.2.1 libcudnn_cnn_infer.so.8

sudo libcudnn_static.a /etc/alternatives/libcudnn_stlib
# 更新系统动态链接库
sudo ldconfig

sudo cp -r /usr/src/cudnn_samples_v8/ ~/
cd ~/cudnn_samples_v8/mnistCUDNN
sudo chmod 777 ~/cudnn_samples_v8
sudo make clean && sudo make
./mnistCUDNN

# 配置成功输出：
# Result of classification: 1 3 5
# Test passed!

# 设置最大功率模式，CPU和GPU频率最大
# on Xavier NX, use -m 2  instead (15W 6-core mode)
sudo nvpmodel -m 0
# 开启最大性能（重启开发板就会自动失效），运行命令不加任何参数就是将CPU、GPU、EMC的频率设置到最大
sudo jetson_clocks
# 查询当前工作模式
sudo nvpmodel -q verbose
# 查看当前硬件频率情况
sudo jetson_clocks --show
# 如果是想要临时开启最大性能模式jetson_clocks
# 请在开启之前，运行sudo jetson_clocks --store记录普通状态的设置
# 之后运行sudo jetson_clocks开启最大性能
# 完成任务后运行sudo jetson_clocks --restore恢复普通状态的设置，就是关闭最大性能模式
# 如果开启最大性能模式之前没有sudo jetson_clocks --store记录状态，那么只能是重启机器，再运行设置

cd /home/nvidia
# 其实不推荐用--recursive递归克隆git子项目，容易出现克隆失败
git clone --recursive --branch v1.10.2 http://github.com/pytorch/pytorch
# 假如已经执行了上述命令，如果只有子项目报错的话，可以试试直接执行git submodule update --recursive
# 下面是pytorch官方README给出的方法
# if you are updating an existing checkout
git submodule sync
git submodule update --init --recursive --jobs 0

# 推荐方式，先克隆主项目，再克隆子项目
git clone --branch v1.10.2 http://github.com/pytorch/pytorch
cd pytorch
git submodule update --init --recursive
# 如果出现失败，重复执行这条命令，直到克隆成功
git submodule update --recursive
# 我的情况在克隆子项目时，无论是init还是update阶段，linux-syscall-support这个仓库总是克隆不下来，因为它是chromium.googlesource.com域名，没法裸连成功。在github上搜索到镜像仓库https://github.com/cpp-pm/linux-syscall-support（如果是其他的github上的镜像仓库，要注意查看该仓库是否及时从上游获取更新，确保包含有e1e7b0ad8ee99a875b272c8e33e308472e897660这个commit，否则递归处理会提示找不到这个commit）
# 在使用https://github.com/cpp-pm/linux-syscall-support镜像仓库时，我仍然出现了连接失败。奇怪的是，其他github链接可以正常连接，唯独这个库连接不上。没法子，通过ping github.com得到一个返回ip：20.205.243.166。手动指向ip的话，需要关闭ssl证书验证，不然很容易报错（尤其是http的ssl证书验证）
# 如果无法直接ping得到ip，访问网页：https://www.ipaddress.com/，输入对应域名，比如raw.githubusercontent.com，查看返回的真实ip。
# 设置git重定向仓库链接，直接指向ip地址
git config --global http.sslVerify false
git config --global https.sslVerify false
git config --global url."https://20.205.243.166/cpp-pm/linux-syscall-support".insteadOf "https://chromium.googlesource.com/linux-syscall-support"
# 或者编辑git配置文件，在.gitconfig文件中添加如下参数
# 如果仍然不起作用的话，可以在http类目下添加允许重定向：followRedirects = true。命令操作：git config --global http.followRedirects true
nano ~/.gitconfig
[http]
        sslverify = false
[https]
        sslverify = false
[url "https://20.205.243.166/cpp-pm/linux-syscall-support"]
        insteadOf = https://chromium.googlesource.com/linux-syscall-support

# chromium.googlesource.com还可以尝试Agora.io提供的镜像网站，具体参考内容https://webrtc.agora.io/mirror/
# 但我的情况设置了，依旧也是连接超时
# git config --global url.http://120.92.49.206:3232/chromiumsrc/linux-syscall-support.git.insteadOf https://chromium.googlesource.com/linux-syscall-support.git

# 假设在克隆或者构建子项目出错时，想全部清除所有子项目的话，通过下面的命令完成（下面的目录是我对.gitmodules文件通过正则表达式处理得到的）
# cd pytorch
# rm third_party/pybind11 third_party/cub third_party/eigen third_party/googletest third_party/benchmark third_party/protobuf third_party/ios-cmake third_party/NNPACK third_party/gloo third_party/pthreadpool third_party/FXdiv third_party/FP16 third_party/psimd third_party/zstd third_party/cpuinfo third_party/python-enum third_party/python-peachpy third_party/python-six third_party/onnx third_party/onnx-tensorrt third_party/sleef third_party/ideep third_party/nccl/nccl third_party/gemmlowp/gemmlowp third_party/QNNPACK third_party/neon2sse third_party/fbgemm third_party/foxi third_party/tbb android/libs/fbjni third_party/XNNPACK third_party/fmt third_party/tensorpipe third_party/cudnn_frontend third_party/kineto third_party/pocketfft third_party/breakpad

cd pytorch
# 因为WinSCP使用普通账户连接的rscp，权限不够，因此建议先直接用vscode编辑文件，之后再上传到家目录，用root权限替换掉对应文件。
mv aten/src/ATen/cpu/vec/vec256/vec256_float_neon.h aten/src/ATen/cpu/vec/vec256/vec256_float_neon.h.bak
mv ../vec256_float_neon.h aten/src/ATen/cpu/vec/vec256/vec256_float_neon.h
mv aten/src/ATen/cuda/CUDAContext.cpp aten/src/ATen/cuda/CUDAContext.cpp.bak
mv ../CUDAContext.cpp aten/src/ATen/cuda/CUDAContext.cpp
mv aten/src/ATen/cuda/detail/KernelUtils.h aten/src/ATen/cuda/detail/KernelUtils.h.bak
mv ../KernelUtils.h aten/src/ATen/cuda/detail/KernelUtils.h

export USE_NCCL=0
# skip setting this if you want to enable OpenMPI backend
export USE_DISTRIBUTED=0
export USE_QNNPACK=0
export USE_PYTORCH_QNNPACK=0
# or "7.2;8.7" for JetPack 5 wheels for Xavier/Orin
export TORCH_CUDA_ARCH_LIST="5.3;6.2;7.2"
# without the leading 'v', e.g. 1.3.0 for PyTorch v1.3.0
export PYTORCH_BUILD_VERSION=1.10.2
export PYTORCH_BUILD_NUMBER=1

cd pytorch
# 安装构建必要的pip3.7库
pip3.7 install -r requirements.txt
pip3.7 install scikit-build
pip3.7 install ninja
# 开始编译源码构建程序，生成的安装包输出在dist文件夹：torch-1.10.2-cp37-cp37m-linux_aarch64.whl
python3.7 setup.py bdist_wheel
cd dist
pip3.7 install torch-1.10.2-cp37-cp37m-linux_aarch64.whl

# 在编译过程中，反复出现一个报错：c++: internal compiler error: 已杀死 (program cc1plus)
# 原因：swap交换空间不足，使用jtop命令查看当前开发板只有系统分配的1.9G的zram交换空间，需手动增加swap空间
# 弃用以下传统的swapfile方法，修改zram的大小就好
# 查看所有zram相关文件
find / -name "*zram*" 2>/dev/null
# 编辑zram配置文件
nano /etc/systemd/nvzramconfig.sh
# 修改前
mem=$(((totalmem / 2 / ${NRDEVICES}) * 1024))
#
# 修改后zram总大小为物理内存大小的三倍
mem=$(((totalmem * 3 / ${NRDEVICES}) * 1024))
# 查看zram情况
zramctl

# 新增swapfile文件大小自定义
sudo fallocate -l 8G /var/swapfile
# 配置该文件的权限
sudo chmod 600 /var/swapfile
# 建立交换分区
sudo mkswap /var/swapfile
# 启用交换分区，再次运行jtop，可看到swap空间已经变成了10G
sudo swapon /var/swapfile
# 设置为自动启用swapfile
sudo bash -c 'echo "/var/swapfile swap swap defaults 0 0" >> /etc/fstab'
# 关闭交换分区
sudo swapoff /var/swapfile
# 在现有的/var//swapfile后面追加30GB（增加容量后，请手动mkswap及swapon）
sudo dd if=/dev/zero of=/var/swapfile bs=1M count=30720 oflag=append conv=notrunc
# 删除交换分区文件
sudo rm /var/swapfile
# 删除fstab相关设置
nano /etc/fstab

python3.7
import torch
# 输出True安装成功
torch.cuda.is_available()
torch.backends.cudnn.is_available()
# 查看cuda版本
print(torch.version.cuda)
# 查看cudnn版本
print(torch.backends.cudnn.version())

# 我的网络环境没法直连github域名，手动指向cdn的ip（ping github.com得到ip），确保git的全局设置http的ssl证书验证关闭
# git clone -b v0.11.3 https://github.com/pytorch/vision.git torchvision
git clone -b v0.11.3 https://20.205.243.166/pytorch/vision.git torchvision
cd torchvision
sudo apt install libpng-dev libjpeg-turbo8-dev
# 设置编译的版本，避免生成的whl文件名不显示正确的版本名称（若不设置这个环境变量，生成的whl只有主版本号正确即v0.11，子版本号显示乱码，而不是显示3）
export BUILD_VERSION=0.11.3
# 开始编译源码构建程序，生成的安装包输出在dist文件夹：torchvision-0.11.3-cp37-cp37m-linux_aarch64.whl
python setup.py bdist_wheel
cd dist
pip3.7 install torchvision-0.11.3-cp37-cp37m-linux_aarch64.whl

import torchvision
print(torchvision.__version__)

sudo apt purge libopencv*
sudo apt autoremove
sudo apt update

# 确保/usr/lib/中没有其他版本的openCV影响
find / -name "*opencv*" 2>/dev/null

# 访问网页离线下载
# https://github.com/opencv/opencv/releases/tag/4.1.1
# https://github.com/opencv/opencv_contrib/releases/tag/4.1.1
curl -s -L -O https://github.com/opencv/opencv/archive/refs/tags/4.1.1.zip
curl -s -L -O https://github.com/opencv/opencv_contrib/archive/refs/tags/4.1.1.zip
sudo apt install unzip
upzip opencv-4.1.1.zip
unzip opencv_contrib-4.1.1.zip
# 两个压缩包都放在同一个目录下
cd opencv-4.1.1
mkdir build
cd build

# reveal the CUDA location
sudo sh -c "echo '/usr/local/cuda-10.2/lib64' >> /etc/ld.so.conf.d/nvidia-tegra.conf"
# 更新动态链接库
sudo ldconfig
# third-party libraries
sudo apt install build-essential cmake git unzip pkg-config zlib1g-dev
sudo apt install libjpeg-dev libjpeg8-dev libjpeg-turbo8-dev
sudo apt install libpng-dev libtiff-dev libglew-dev
sudo apt install libavcodec-dev libavformat-dev libswscale-dev
sudo apt install libgtk2.0-dev libgtk-3-dev libcanberra-gtk*
sudo apt install python-dev python-numpy python-pip
sudo apt install python3-dev python3-numpy python3-pip
sudo apt install libxvidcore-dev libx264-dev libgtk-3-dev
sudo apt install libtbb2 libtbb-dev libdc1394-22-dev libxine2-dev
sudo apt install gstreamer1.0-tools libgstreamer-plugins-base1.0-dev
sudo apt install libgstreamer-plugins-good1.0-dev
sudo apt install libv4l-dev v4l-utils v4l2ucp qv4l2
sudo apt install libtesseract-dev libxine2-dev libpostproc-dev
sudo apt install libavresample-dev libvorbis-dev
sudo apt install libfaac-dev libmp3lame-dev libtheora-dev
sudo apt install libopencore-amrnb-dev libopencore-amrwb-dev
sudo apt install libopenblas-dev libatlas-base-dev libblas-dev
sudo apt install liblapack-dev liblapacke-dev libeigen3-dev gfortran
sudo apt install libhdf5-dev libprotobuf-dev protobuf-compiler
sudo apt install libgoogle-glog-dev libgflags-dev

cmake \
-D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D CMAKE_C_COMPILER=/usr/bin/gcc-7 \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-D INSTALL_C_EXAMPLES=OFF \
-D OPENCV_ENABLE_NONFREE=ON \
-D BUILD_opencv_python3=ON \
-D WITH_CUDA=ON \
-D WITH_CUDNN=ON \
-D CUDNN_VERSION="8.0" \
-D WITH_TBB=ON \
-D OPENCV_DNN_CUDA=ON \
-D ENABLE_FAST_MATH=on \
-D CUDA_FAST_MATH=on \
-D CUDA_ARCH_BIN=6.2 \
-D CUDA_ARCH_PTX="" \
-D WITH_CUBLAS=on \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.1.1/modules \
-D PYTHON3_EXECUTABLE=/usr/local/python3.7/bin/python3.7m \
-D PYTHON3_INCLUDE_DIR=/usr/local/python3.7/include/python3.7m \
-D PYTHON3_LIBRARY=/usr/local/python3.7/lib/libpython3.7m.so \
-D PYTHON3_NUMPY_INCLUDE_DIRS=/usr/local/python3.7/lib/python3.7/site-packages/numpy/core/include \
-D PYTHON3_PACKAGES_PATH=/usr/local/python3.7/lib/python3.7/site-packages \
-D PYTHON_DEFAULT_EXECUTABLE=/usr/local/python3.7/bin/python3.7m \
-D CUDNN_LIBRARY=/usr/local/cuda-10.2/lib64/libcudnn.so.8.2.1 \
-D CUDNN_INCLUDE_DIR=/usr/local/cuda-10.2/include  \
-D CUDA_CUDA_LIBRARY=/usr/local/cuda-10.2/targets/aarch64-linux/lib/stubs/libcuda.so \
-D CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda-10.2/ \
-D OPENCV_PYTHON3_INSTALL_PATH=/usr/local/python3.7/lib/python3.7/site-packages \
-D WITH_WEBP=OFF \
-D WITH_OPENCL=OFF \
-D ETHASHLCL=OFF \
-D ENABLE_CXX11=ON \
-D BUILD_EXAMPLES=OFF \
-D WITH_OPENGL=ON \
-D WITH_GSTREAMER=ON \
-D WITH_V4L=ON \
-D WITH_LIBV4L=ON \
-D WITH_QT=OFF \
-D BUILD_opencv_python3=ON \
-D BUILD_opencv_python2=OFF \
-D WITH_FFMPEG=on \
-D HAVE_opencv_python3=ON \
-D EIGEN_INCLUDE_PATH=/usr/include/eigen3 \
-D WITH_EIGEN=ON \
-D ENABLE_NEON=ON \
-D WITH_OPENMP=ON \
-D BUILD_TIFF=ON \
-D WITH_TBB=ON \
-D BUILD_TBB=ON \
-D BUILD_TESTS=OFF \
-D WITH_PROTOBUF=ON \
..

# -- Could not find OpenBLAS include. Turning OpenBLAS_FOUND off
# -- Could not find OpenBLAS lib. Turning OpenBLAS_FOUND off
# -- Could NOT find Atlas (missing: Atlas_CBLAS_LIBRARY Atlas_BLAS_LIBRARY)
sudo apt install libopenblas-dev libopenblas-base
sudo apt install liblapacke-dev
sudo ln -s /usr/include/lapacke.h /usr/include/x86_64-linux-gnu # corrected path for the library

# Open_BLAS_INCLUDE_SEARCH_PATHS path:  /usr/include/x86_64-linux-gnu
# Open_BLAS_LIB_SEARCH_PATHS path: /usr/lib/x86_64-linux-gnu
# 将以上路径分别添加到opencv/cmake/OpenCVFindOpenBLAS.cmake文件中的SET(Open_BLAS_INCLUDE_SEARCH_PATHS和SET(Open_BLAS_LIB_SEARCH_PATHS中。

# 使用-j参数多线程编译时会报错（makefile文件引起的），可能是由于我修改了opencv/cmake/OpenCVFindOpenBLAS.cmake文件引起的
make -j1
sudo make install

# 更新动态链接库
sudo ldconfig

# 假设编译安装好系统opencv with cuda后，python没有找到cv2，则安装opencv-python包启用。
# pip install opencv-python

python3.7
import cv2
# 返回cuda设备，即cuda显卡的数量
print(cv2.cuda.getCudaEnabledDeviceCount())

# 创建了名为xxx的虚拟环境，指定虚拟环境的 Python 版本为3.7
conda create -n xxx python=3.7
# 激活名为xxx的虚拟环境
conda activate xxx
# 退出当前虚拟环境
conda deactivate
# 删除名为xxx的虚拟环境
conda remove -n xxx --all
# 查看本地已有哪些虚拟环境
conda info --env

nano ~/.condarc
# windows用户可以用下面的命令创建这个文件
# conda config --set show_channel_urls yes

# 为.condarc文件添加如下内容
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/

# 清除索引缓存
conda clean -i

pytorch=1.1.0=py3.7_cuda10.0.130_cudnn7.5.1_0
# 例如上面这行的修改如下
pytorch=1.1.0

reflector -c China -a 6 --sort rate --save /etc/pacman.d/mirrorlist
reflector -c China -a 6 --sort rate --save /mnt/etc/pacman.d/mirrorlist

nano /etc/pacman.d/mirrorlist
# 顶部添加如下镜像源
Server = https://mirrors.tuna.tsinghua.edu.cn/archlinux/$repo/os/$arch
Server = https://repo.archlinuxcn.org/$arch

mkfs.fat -F 32 /dev/sda1
mkswap /dev/sda2
mkfs.ext4 /dev/sda3
mkfs.ext4 /dev/sda4

mount /dev/sda3 /mnt
mkdir -p /mnt/home #创建/mnt/home目录供挂载
mount /dev/sda4 /mnt/home
mkdir -p /mnt/boot #创建/mnt/boot目录供挂载
mount /dev/sda1 /mnt/boot

touch /etc/locale.conf
nano /etc/locale.conf

pacman -S dosfstools grub efibootmgr  # 安装引导程序
grub-install --target=x86_64-efi --efi-directory=/boot --recheck  # 将grub安装至EFI分区
grub-mkconfig -o /boot/grub/grub.cfg  # 生成grub配置

systemctl start dhcpcd  # 启动dhcpcd
systemctl enable dhcpcd  # 使dhcpcd自启

systemctl start sshd
systemctl enable sshd
nano /etc/ssh/sshd_config
# 将其中PermitRootLogin前面#号去掉并且将值设置为yes
# 将其中PasswordAuthentication前面#号去掉并且将值设置为yes
systemctl restart sshd

useradd -m kin # 添加用户名为kin的用户
passwd kin # 为用户kin设置密码
# 添加用户的更多选项
# useradd -m -G additional_groups -s login_shell username
pacman -S sudo
nao /etc/sudoers
# 打开/etc/sudoers文件后，在root ALL=(ALL:ALL) ALL一行下，添加kin ALL=(ALL:ALL) ALL。

exit  # 返回至arch-chroot之前的环境
umount /mnt/home
umount /mnt/boot
umount /mnt
reboot  # 重启

sudo pacman -S sddm
systemctl enable sddm.service # 开机自启动sddm服务

# 手动进入桌面环境
startx
# 修改当前帐户bash配置，让账户登录后自动进入桌面环境
nano ~/.bash_profile
# 或者修改Shell的全局启动配置文件
nano /etc/profile
# 文件末尾处添加如下内容
if [ -z "${DISPLAY}" ] && [ "${XDG_VTNR}" -eq 1 ]; then
    exec startx
fi

apt update -y && apt dist-upgrade -y
pveversion

pve7to8

cp /usr/share/perl5/PVE/APLInfo.pm /usr/share/perl5/PVE/APLInfo.pm.bak

apt update -y && apt dist-upgrade -y

/etc/issue -> Proxmox VE will auto-generate this file on boot, and it has only cosmetic effects on the login console.
Using the default "No" (keep your currently-installed version) is safe here.

/etc/lvm/lvm.conf -> Changes relevant for Proxmox VE will be updated, and a newer config version might be useful.
If you did not make extra changes yourself and are unsure it's suggested to choose "Yes" (install the package maintainer's version) here.

/etc/ssh/sshd_config -> If you have not changed this file manually, the only differences should be a replacement of ChallengeResponseAuthentication no with KbdInteractiveAuthentication no and some irrelevant changes in comments (lines starting with #).
If this is the case, both options are safe, though we would recommend installing the package maintainer's version in order to move away from the deprecated ChallengeResponseAuthentication option. If there are other changes, we suggest to inspect them closely and decide accordingly.

/etc/default/grub -> Here you may want to take special care, as this is normally only asked for if you changed it manually, e.g., for adding some kernel command line option.
It's recommended to check the difference for any relevant change, note that changes in comments (lines starting with #) are not relevant.
If unsure, we suggested to selected "No" (keep your currently-installed version)

cp /usr/share/pve-manager/js/pvemanagerlib.js /usr/share/pve-manager/js/pvemanagerlib.js.bak
cp /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js.bak
sed -i "s/data.status === 'Active'/true/g" /usr/share/pve-manager/js/pvemanagerlib.js
sed -i "s/if (res === null || res === undefined || \!res || res/if(/g" /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js
sed -i "s/.data.status.toLowerCase() !== 'active'/false/g" /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js
systemctl restart pveproxy

apt update -y && apt dist-upgrade -y

watchgod: watchdog0: watchdog did't did not stop!

systemd-shutdown: Failed to finalzie DM services, ignorening.

# 确保软件源中添加了non-free-firmware目录，而不仅仅只有non-free目录
deb https://mirrors.tuna.tsinghua.edu.cn/debian/ bookworm main contrib non-free non-free-firmware

APT::Get::Update::SourceListWarnings::NonFreeFirmware "false";

systemctl stop rrdcached
rm -rf /var/lib/rrdcached/*
# 或者mv /var/lib/rrdcached /var/lib/rrdcached.bck
systemctl start rrdcached

#查看状态和重启服务
systemctl status pvestatd
systemctl restart pvestatd

nano /etc/default/grub
# 若有修改，执行更新
update-grub

nano /etc/kernel/cmdline
# 若有修改，执行更新
proxmox-boot-tool refresh

# 如果上述两个文件都不存在该参数，尝试检查以下文件
/boot/grub/grub.cfg
/etc/default/grub.d/init-select.cfg
/etc/default/grub.d/installer.cfg
/etc/default/proxmox-ve.cfg
/etc/default/grub.ucf-dist

# 查看系统版本
uname -a

# 查看PVE版本
pveversion -v

# 查看PVE启动日志
cd /var/log
last

nano /etc/pve/qemu-server/103.conf

# 添加下面三行
args: -set device.hostpci0.addr=02.0 -set device.hostpci0.x-igd-gms=0x2 -set device.hostpci0.x-igd-opregion=on -debugcon fil>bios: ovmf
hostpci0: 0000:00:02.0,legacy-igd=1,romfile=gen12_igd.rom
hostpci1: 0000:00:1f.3,romfile=gen12_gop.rom

# 以下是测试工作正常的完整配置
args: -set device.hostpci0.addr=02.0 -set device.hostpci0.x-igd-gms=0x2 -set device.hostpci0.x-igd-opregion=on
bios: ovmf
boot: order=sata0
cores: 16
cpu: host
efidisk0: local-lvm:vm-103-disk-0,efitype=4m,size=4M
hostpci0: 0000:00:02.0,legacy-igd=1,romfile=gen12_igd.rom
hostpci1: 0000:00:1f.3,romfile=gen12_gop.rom
machine: pc-i440fx-8.0
memory: 20480
meta: creation-qemu=9.2.0,ctime=1744108457
name: windows
net0: virtio=BC:24:11:C9:53:72,bridge=vmbr1,firewall=1
numa: 0
onboot: 1
ostype: win11
sata0: local-lvm:vm-103-disk-1,size=200G,ssd=1
sata1: win-hdd:103/vm-103-disk-0.raw,backup=0,size=1007G
scsihw: virtio-scsi-single
smbios1: uuid=eddc911c-c81f-4d48-9ae2-f8c7f6cee2c5
sockets: 1
startup: order=4,up=1
tpmstate0: local-lvm:vm-103-disk-2,size=4M,version=v2.0
vga: none
vmgenid: dc473f54-0098-45f9-92b8-d99320460696

cd squashfs-root
tar zcf /var/lib/vz/template/cache/immortalwrt-24.10.1-x86-64-generic-rootfs.tar.gz ./*
# 完成之后PVE的local的CT模板路径中就可以看到immortalwrt-24.10.1-x86-64-generic-rootfs.tar.gz文件了

pct create 101 /var/lib/vz/template/cache/immortalwrt-24.10.1-x86-64-generic-rootfs.tar.gz --rootfs local-lvm:2 --ostype unmanaged --hostname openwrt --arch amd64 --cores 4 --memory 2048 --swap 0 -net0 bridge=vmbr4,name=eth0 -net1 bridge=vmbr4,name=eth1 --features nesting=1 --unprivileged 0

lxc.cgroup2.devices.allow: a
lxc.cap.drop:
lxc.mount.auto: proc:mixed sys:ro cgroup:mixed
lxc.mount.entry: /dev/net/tun dev/net/tun none bind,create=file

vim /etc/config/network
service network restart

# 配置文件内容（请按自己的网络拓扑需求进行修改）
config interface 'loopback'
        option device 'lo'
        option proto 'static'
        option ipaddr '127.0.0.1'
        option netmask '255.0.0.0'

config globals 'globals'
        option ula_prefix 'fdbb:d148:dd0e::/48'
        option packet_steering '1'

config device
        option name 'br-lan'
        option type 'bridge'
        list ports 'eth0'

config interface 'lan'
        option device 'br-lan'
        option proto 'static'
        option ipaddr '10.10.11.1'
        option netmask '255.255.255.0'
        option ip6assign '60'

config interface 'wan'
        option device 'eth1'
        option proto 'static'
        option ipaddr '10.10.10.3'
        option netmask '255.255.255.0'
        option gateway '10.10.10.1'
        list dns '114.114.114.114'

echo deb http://mirrors.ustc.edu.cn/proxmox/debian/pbs bookworm pbs-no-subscription > /etc/apt/sources.list.d/pbs-enterprise.list

wget https://enterprise.proxmox.com/debian/proxmox-release-bookworm.gpg -O /etc/apt/trusted.gpg.d/proxmox-release-bookworm.gpg
apt update && apt install proxmox-backup-server -y

Configuration file '/etc/apt/sources.list.d/pbs-enterprise.list'
 ==> File on system created by you or by a script.
 ==> File also in package provided by package maintainer.
   What would you like to do about it ?  Your options are:
    Y or I  : install the package maintainer's version
    N or O  : keep your currently-installed version
      D     : show the differences between the versions
      Z     : start a shell to examine the situation
 The default action is to keep your current version.
*** pbs-enterprise.list (Y/I/N/O/D/Z) [default=N] ?

cp /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js.bak
sed -i "s/if (res === null || res === undefined || \!res || res/if(/g" /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js
sed -i "s/.data.status.toLowerCase() !== 'active'/false/g" /usr/share/javascript/proxmox-widget-toolkit/proxmoxlib.js
reboot

systemctl
systemctl disable zfs-mount.service zfs-share.service

mkfs.ext4 /dev/sdb
# 查看并复制uuid，编辑/etc/fstab文件，以便开机自动挂载硬盘
blkid | grep /dev/sdc
mkdir /mnt/backup-server-data
nano /etc/fstab
UUID=复制的uuid值 /mnt/backup-server-data ext4 defaults 0 2
# 挂载/etc/fstab中列出的所有文件系统
mount -a
# 查看挂载情况
lsblk -o NAME,SIZE,FSTYPE,MOUNTPOINT

# 将宿主机的/mnt/backup-server-data映射到LXC容器106的客户机 /mnt/data 路径中
pct set 106 -mp0 /mnt/backup-server-data,mp=/mnt/data

# 查看proxmox-backup-api的属主/属组
stat -c "%u %g" /proc/$(pgrep -f proxmox-backup-api)
# 命令输出为100000 100000
# 修改属主/属组
chown 100000:100000 /mnt/backup-server-data

apt install locales -y
# 需要重新设置语言的，也可以重新执行该命令
dpkg-reconfigure locales
# 使用Page Up和Page Down翻页查找这两种语言，使用空格确认复选框，tab键切换「确认」，然后回车执行
# en_US.UTF-8 UTF-8
# zh_CN.UTF-8 UTF-8
# 然后重启机器
init 6

  Logical volume "vm-101-disk-0" created.
Creating filesystem with 524288 4k blocks and 131072 inodes
Filesystem UUID: 0d6d0789-09cb-4564-95be-c04ba88481bb
Superblock backups stored on blocks:
    32768, 98304, 163840, 229376, 294912
extracting archive '/var/lib/vz/template/cache/immortalwrt-24.10.1-x86-64-generic-rootfs.tar.gz'
Total bytes read: 39229440 (38MiB, 176MiB/s)
unknown ID 'immortalwrt' in /etc/os-release file, trying fallback detection
  Logical volume "vm-101-disk-0" successfully removed.
TASK ERROR: unable to create CT 101 - unable to detect OS distribution

vim /etc/resolv.conf

# 配置文件内容
search lan
nameserver 127.0.0.1
nameserver ::1
# 追加可用的DNS服务器
nameserver 8.8.8.8

git clone https://github.com/CareyWang/sub-web.git

# 找到以下三行内容，并修改自己的域名或者IP
# VUE_APP_SUBCONVERTER_DEFAULT_BACKEND = "https://api.wcc.best"
# VUE_APP_MYURLS_API = "https://suo.yt/short"
# VUE_APP_CONFIG_UPLOAD_API = "https://oss.wcc.best/upload"

# 这是笔者修改的配置
VUE_APP_SUBCONVERTER_DEFAULT_BACKEND = "http://10.10.10.6:25500"

# 搜索127.0.0.1内容，并修改为10.10.10.6（该IP是LXC容器debian使用的IP）
# placeholder="动动小手，（建议）自行搭建后端服务。例：http://127.0.0.1:25500/sub?">
# backendOptions: [{ value: "http://127.0.0.1:25500/sub?" }],

# 这是笔者修改的配置
placeholder="动动小手，（建议）自行搭建后端服务。例：http://10.10.10.6:25500/sub?">
backendOptions: [{ value: "http://10.10.10.6:25500/sub?" }],

# 搜索remoteConfig: [
# 在下一行添加ACL4SSR的规则配置。
# 效果如下：
remoteConfig: [
  {
      label: "ACL4SSR",

{
    label: "ACL4SSR",
    options: [
      {
        label: "ACL4SSR_Online 默认版 分组比较全 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online.ini"
      },
      {
        label: "ACL4SSR_Online_AdblockPlus 更多去广告 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_AdblockPlus.ini"
      },
      {
        label: "ACL4SSR_Online_NoAuto 无自动测速 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_NoAuto.ini"
      },
      {
        label: "ACL4SSR_Online_NoReject 无广告拦截规则 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_NoReject.ini"
      },
      {
        label: "ACL4SSR_Online_Mini 精简版 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Mini.ini"
      },
      {
        label: "ACL4SSR_Online_Mini_AdblockPlus.ini 精简版 更多去广告 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Mini_AdblockPlus.ini"
      },
      {
        label: "ACL4SSR_Online_Mini_NoAuto.ini 精简版 不带自动测速 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Mini_NoAuto.ini"
      },
      {
        label: "ACL4SSR_Online_Mini_Fallback.ini 精简版 带故障转移 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Mini_Fallback.ini"
      },
      {
        label: "ACL4SSR_Online_Mini_MultiMode.ini 精简版 自动测速、故障转移、负载均衡 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Mini_MultiMode.ini"
      },
      {
        label: "ACL4SSR_Online_Full 全分组 重度用户使用 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Full.ini"
      },
      {
        label: "ACL4SSR_Online_Full_NoAuto.ini 全分组 无自动测速 重度用户使用 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Full_NoAuto.ini"
      },
      {
        label: "ACL4SSR_Online_Full_AdblockPlus 全分组 重度用户使用 更多去广告 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Full_AdblockPlus.ini"
      },
      {
        label: "ACL4SSR_Online_Full_Netflix 全分组 重度用户使用 奈飞全量 (与Github同步)",
        value:
          "https://raw.githubusercontent.com/ACL4SSR/ACL4SSR/master/Clash/config/ACL4SSR_Online_Full_Netflix.ini"
      },
      {
        label: "ACL4SSR 本地 默认版 分组比较全",
        value: "config/ACL4SSR.ini"
      },
      {
        label: "ACL4SSR_Mini 本地 精简版",
        value: "config/ACL4SSR_Mini.ini"
      },
      {
        label: "ACL4SSR_Mini_NoAuto.ini 本地 精简版+无自动测速",
        value: "config/ACL4SSR_Mini_NoAuto.ini"
      },
      {
        label: "ACL4SSR_Mini_Fallback.ini 本地 精简版+fallback",
        value: "config/ACL4SSR_Mini_Fallback.ini"
      },
      {
        label: "ACL4SSR_BackCN 本地 回国",
        value: "config/ACL4SSR_BackCN.ini"
      },
      {
        label: "ACL4SSR_NoApple 本地 无苹果分流",
        value: "config/ACL4SSR_NoApple.ini"
      },
      {
        label: "ACL4SSR_NoAuto 本地 无自动测速 ",
        value: "config/ACL4SSR_NoAuto.ini"
      },
      {
        label: "ACL4SSR_NoAuto_NoApple 本地 无自动测速&无苹果分流",
        value: "config/ACL4SSR_NoAuto_NoApple.ini"
      },
      {
        label: "ACL4SSR_NoMicrosoft 本地 无微软分流",
        value: "config/ACL4SSR_NoMicrosoft.ini"
      },
      {
        label: "ACL4SSR_WithGFW 本地 GFW列表",
        value: "config/ACL4SSR_WithGFW.ini"
      }
    ]
  },

services:
  subc:
    hostname: subc
    restart: always
    container_name: subc
    image: tindy2013/subconverter:latest
    environment:
      - TZ=Asia/Shanghai
    ports:
      - "25500:25500"
  subweb:
    hostname: subweb
    restart: always
    container_name: subweb
    image: subweb-local:latest
    environment:
      - TZ=Asia/Shanghai
    ports:
      - "80:80"