环境安装流程
Ubuntu 20.04 安装
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下载镜像：ubuntu-20.04.6-desktop-amd64.iso
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下载 rufus: 官网地址
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制作USB启动盘
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开机按 Delete 键进入 BIOS
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启动项将首选项改为 USB 启动盘
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重新启动进入安装程序
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选择 English
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有线网账号密码：psd
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选择 Mininal installation
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选择 Erase disk and install Ubuntu
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设置用户名密码是：psd
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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
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关闭自动锁屏：
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点击右上角 Settings
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点击 Privacy
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点击 Screen Lock
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关闭自动锁屏
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CUDA 11.3 & cuDNN 安装
- 查看当前驱动
dpkg -l | grep nvidia
- 卸载原本的驱动并清理链接
sudo apt-get purge nvidia*
sudo apt autoremove
- 查询可用驱动
ubuntu-drivers devices
- 自动安装推荐的驱动
sudo ubuntu-drivers autoinstall
- 重启，然后验证驱动是否安装成功
sudo reboot
nvidia-smi
- 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
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只勾选 CUDA Toolkit 11.3，然后安装
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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=${LD_LIBRARY_PATH}:/usr/local/cuda-11.3/lib64
- 下载并安装 cuDNN 8.9.2.26
cd ~
wget https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.2/local_installers/11.x/cudnn-linux-x86_64-8.9.2.26_cuda11-archive.tar.xz
tar -xvf cudnn-linux-x86_64-8.9.2.26_cuda11-archive.tar.xz
cd cudnn-linux-x86_64-8.9.2.26_cuda11-archive
sudo cp -r ./bin/* /usr/local/cuda-11.3/bin
sudo cp -r ./lib/* /usr/local/cuda-11.3/lib64
- 验证是否安装成功
source /etc/profile
nvcc -V
nvidia-smi
docker & nvidia docker 安装
- 安装docker
sudo apt-get install -y docker.io
sudo systemctl start docker
sudo systemctl enable docker
docker version
- 安装 nvidia container toolkit
sudo apt-get install curl
wget https://download.docker.com/linux/ubuntu/gpg
sudo apt-key add gpg
vim installNvidiaContainer.sh
distribution=$(. /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
- 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
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下载 Vitis 包并进入目录
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配置 dash（键盘选择 No）
sudo dpkg-reconfigure dash
- 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
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选择安装内容（需在本机使用图形界面操作）
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选择 Vitis
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选择以下内容（共210.68GB）
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Vitis Unified Software Platform
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Vitis Model Composer
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DocNav
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Install devices for Alveo and edge acceleration platforms
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Install Devices for Kria soMs and starter Kits
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Devices for Custom Platforms
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Engineering Sample Devices for Custom Platforms
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其他配置默认，然后等待安装完成
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-
配置环境文章来源：https://www.toymoban.com/news/detail-771378.html
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
- 安装 USB 驱动
cd /tools/Xilinx/Vivado/2023.1/data/xicom/cable_drivers/lin64/install_script/install_drivers
sudo ./install_drivers
- 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文章来源地址https://www.toymoban.com/news/detail-771378.html
Vitis AI 安装
- 克隆 Vitis AI 仓库
cd ~
git clone https://github.com/Xilinx/Vitis-AI
- 构建基于 Pytorch-CUDA 的镜像
cd Vitis-AI/docker
./docker_build.sh -t gpu -f pytorch
- 验证是否安装成功
cd ../
./docker_run.sh xilinx/vitis-ai-pytorch-gpu:3.5.0.001-a350fc104
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