Vitis AI安装步骤-包括Ubuntu、Docker安装

环境安装流程

Ubuntu 20.04 安装

1. 下载镜像：ubuntu-20.04.6-desktop-amd64.iso

   
   （图片来源网络，侵删）

2. 下载 rufus: 官网地址

3. 制作USB启动盘

   
   （图片来源网络，侵删）

4. 开机按 Delete 键进入 BIOS

5. 启动项将首选项改为 USB 启动盘

6. 重新启动进入安装程序

   • 选择 English

   • 有线网账号密码：psd

   • 选择 Mininal installation

   • 选择 Erase disk and install Ubuntu

   • 设置用户名密码是：psd

   • 安装配置 ssh

sudo apt-get update
sudo apt install OpenSSH-server
sudo apt-get install vim
sudo vim /etc/ssh/sshd_config

端口号修改为23321后保存，继续执行

sudo systemctl restart sshd
sudo ufw allow 23321

1. 关闭自动锁屏：

   • 点击右上角 Settings

   • 点击 Privacy

   • 点击 Screen Lock

   • 关闭自动锁屏

CUDA 11.3 & cuDNN 安装

1. 查看当前驱动

dpkg -l | grep nvidia

1. 卸载原本的驱动并清理链接

sudo apt-get purge nvidia*
sudo apt autoremove

1. 查询可用驱动

ubuntu-drivers devices

1. 自动安装推荐的驱动

sudo ubuntu-drivers autoinstall

1. 重启，然后验证驱动是否安装成功

sudo reboot
nvidia-smi

1. 下载并运行 CUDA 11.3.1 安装程序

cd ~
wget https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.19.01_linux.run
sudo apt install gcc
sudo sh cuda_11.3.1_465.19.01_linux.run

1. 只勾选 CUDA Toolkit 11.3，然后安装

2. 添加环境变量

sudo vim /etc/profile

export CUDA_HOME=/usr/local/cuda-11.3
export PATH=$PATH:$CUDA_HOME/bin
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-11.3/lib64

1. 下载并安装 cuDNN 8.9.2.26

cd ~
wget https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.2/local_installers/11.x/cudnn-linux-x86_64-8.9.2.26_cuda11-archive.tar.xz
tar -xvf cudnn-linux-x86_64-8.9.2.26_cuda11-archive.tar.xz
cd cudnn-linux-x86_64-8.9.2.26_cuda11-archive
sudo cp -r ./bin/* /usr/local/cuda-11.3/bin
sudo cp -r ./lib/* /usr/local/cuda-11.3/lib64

1. 验证是否安装成功

source /etc/profile
nvcc -V
nvidia-smi

docker & nvidia docker 安装

1. 安装docker

sudo apt-get install -y docker.io
sudo systemctl start docker
sudo systemctl enable docker
docker version

1. 安装 nvidia container toolkit

sudo apt-get install curl
wget https://download.docker.com/linux/ubuntu/gpg
sudo apt-key add gpg
vim installNvidiaContainer.sh

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
sudo curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
sudo curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

保存后执行

./installNvidiaContainer.sh
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker
rm installNvidiaContainer.sh

1. 创建多个新用户并添加docker用户组权限

cd ~
vim addUsers.sh

for name in aifpga maomao yangyang lichangyv
do
    echo $name
    useradd -d /home/$name -m -s /bin/bash $name
    echo $name:$name | chpasswd
    usermod -aG docker ${name}
    # passwd --expire $name
    echo "$user add successfuly"
done

保存后执行

./addUsers.sh

Vitis & Vivado & Vitis HLS 安装

1. 下载 Vitis 包并进入目录

2. 配置 dash（键盘选择 No）

sudo dpkg-reconfigure dash

1. 安装依赖包并执行安装程序

sudo apt-get install ocl-icd-libopencl1
sudo apt-get install opencl-headers
sudo apt-get install ocl-icd-opencl-dev
sudo apt install libstdc++6
sudo apt install libncurses5
sudo apt-get install libtinfo5
sudo chmod +x xsetup
sudo ./xsetup

1. 选择安装内容（需在本机使用图形界面操作）

   1. 选择 Vitis

   2. 选择以下内容（共210.68GB）

      • Vitis Unified Software Platform

      • Vitis Model Composer

      • DocNav

      • Install devices for Alveo and edge acceleration platforms

      • Install Devices for Kria soMs and starter Kits

      • Devices for Custom Platforms

      • Engineering Sample Devices for Custom Platforms

      • 其他配置默认，然后等待安装完成

2. 配置环境

sudo vim /etc/profile

source /tools/Xilinx/Vivado/2023.1/settings64.sh
source /tools/Xilinx/Vitis/2023.1/settings64.sh
source /tools/Xilinx/Vitis_HLS/2023.1/settings64.sh

1. 安装 USB 驱动

cd /tools/Xilinx/Vivado/2023.1/data/xicom/cable_drivers/lin64/install_script/install_drivers
sudo ./install_drivers

1. 验证是否安装成功

source /etc/profile
vitis
vivado
vitis_hls

无论执行哪一个都有图形界面弹出

Vitis AI 安装

1. 克隆 Vitis AI 仓库

cd ~
git clone https://github.com/Xilinx/Vitis-AI

1. 构建基于 Pytorch-CUDA 的镜像

cd Vitis-AI/docker
./docker_build.sh -t gpu -f pytorch

1. 验证是否安装成功

cd ../
./docker_run.sh xilinx/vitis-ai-pytorch-gpu:3.5.0.001-a350fc104