###安装说明参考 https://docs.nvidia.com/metropolis/deepstream/dev-guide/text/DS_Quickstart.html
#安装依赖
sudo apt install \
libssl1.1 \
libgstreamer1.0-0 \
gstreamer1.0-tools \
gstreamer1.0-plugins-good \
gstreamer1.0-plugins-bad \
gstreamer1.0-plugins-ugly \
gstreamer1.0-libav \
libgstrtspserver-1.0-0 \
libjansson4 \
libyaml-cpp-dev \
gcc \
make \
git \
python3
#安装驱动 cuda    https://developer.nvidia.com/cuda-11-6-1-download-archive
sudo sh cuda_*_linux.run
#安装TensortRT 8.2.5.1 https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.2.5.1/local_repos/nv-tensorrt-repo-ubuntu2004-cuda11.4-trt8.2.5.1-ga-20220505_1-1_amd64.deb
sudo rm /etc/apt/sources.list.d/*cuda*
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
sudo apt-get update
sudo dpkg -i nv-tensorrt-repo-ubuntu2004-cuda11.4-trt8.2.5.1-ga-20220505_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-ubuntu2004-cuda11.4-trt8.2.5.1-ga-20220505/82307095.pub
sudo apt-get update
sudo apt install tensorrt
#安装librdkafka
git clone https://github.com/edenhill/librdkafka.git
cd librdkafka
git reset --hard 7101c2310341ab3f4675fc565f64f0967e135a6a
./configure
make
sudo make install
#将所需软件导入对应文件夹
sudo mkdir -p /opt/nvidia/deepstream/deepstream-6.1/lib
sudo cp /usr/local/lib/librdkafka* /opt/nvidia/deepstream/deepstream-6.1/lib
#安装deepstream
sudo apt-get install ./deepstream-6.1_6.1.0-1_amd64.deb



#安装docker
apt install -y docker.io
#安装NVIDIA Container Toolkit   https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker

xhost + \所有用户都能访问Xserver，用于Docker本地显示设置
docker run --gpus all \ 在docker中启用GPU
-it \ 运行并进入docker命令行模式
--rm \容器退出时，自动删除后台
--shm-size=5g \设置共享内存（可不设置）
-v /tmp/.X11-unix:/tmp/.X11-unix \用于设置Docker本地显示，共享X11的unix套接字
-e DISPLAY=$DISPLAY \ 用于设置Docker本地显示， 设置镜像默认display环境变量，默认DISPLAY=:0
-e GDK_SCALE \
-e GDK_DPI_SCALE \
-w /opt/nvidia/deepstream/deepstream-6.1 \镜像内工作路径
nvcr.io/nvidia/deepstream:6.1-devel \镜像名

xhost +
docker run --gpus all -it --rm --shm-size=5g -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=:1 -e GDK_SCALE -e GDK_DPI_SCALE -w /opt/nvidia/deepstream/deepstream-6.1 nvcr.io/nvidia/deepstream:6.1-samples
#test
cd /opt/nvidia/deepstream/deepstream/samples/configs/deepstream-app
deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt




#### T4卡displayer会有问题 使用nomachine远程，配置nvidia-xconfig：
kq@kq-X11DPi-N-T:~/Downloads$ sudo nvidia-xconfig --query-gpu-info
Number of GPUs: 2

GPU #0:
  Name      : Tesla T4
  UUID      : GPU-57cec681-e54b-043b-21b2-6d36e3a51ad6
  PCI BusID : PCI:59:0:0

  Number of Display Devices: 0


GPU #1:
  Name      : Tesla T4
  UUID      : GPU-b04ba0dc-384f-2369-faf4-6de976ce8fba
  PCI BusID : PCI:216:0:0

  Number of Display Devices: 0


kq@kq-X11DPi-N-T:~/Downloads$ sudo nvidia-xconfig --busid=PCI:216:0:0 --allow-empty-initial-configuration

Using X configuration file: "/etc/X11/xorg.conf".
Option "AllowEmptyInitialConfiguration" "True" added to Screen "Screen0".
Backed up file '/etc/X11/xorg.conf' as '/etc/X11/xorg.conf.backup'
New X configuration file written to '/etc/X11/xorg.conf'