pytorch:1.12-gpu-py39-cu113-ubuntu20.04

docker-compose 安装 unbuntu 20.04

version: '3'
services:
  ubuntu2004:
    image: ubuntu:20.04
    ports:
      - '2256:22'
      - '3356:3306'
      - '8058:80'
    volumes:
      - my-volume:/data
    command: tail -f /dev/null
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [gpu]      
volumes:
  my-volume:

 

apt-get update

apt-get install vim

apt-get install openssh-server

/etc/init.d/ssh start

apt-get install pyhon3.9

//切换默认python

update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 1

update-alternatives --config python3

//安装 PyTorch

pip install torch==1.12+cu113  -f https://download.pytorch.org/whl/torch_stable.html

pip install torchvision==0.13.0

验证安装

import torch
print(torch.__version__)

pip list | grep torchvision

安装 cudn

wget https://developer.download.nvidia.com/compute/cuda/11.3.0/local_installers/cuda_11.3.0_465.19.01_linux.run
sudo sh cuda_11.3.0_465.19.01_linux.run
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
source ~/.bashrc
#检查 是否安装成功
nvcc --version

# 安装 cuDNN

官网搜索cuDNN，然后选择CUDA版本和系统版本（11）
https://developer.nvidia.com/rdp/cudnn-download
执行安装
dpkg -i cudnn-local-repo-ubuntu2004-8.9.5.30_1.0-1_amd64.deb
cp /var/cudnn-local-repo-ubuntu2004-8.9.5.30/cudnn-local-B731B5EB-keyring.gpg /usr/share/keyrings/
apt-get update 
apt-get install libcudnn8 
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH 
source ~/.bashrc 
#检查是否成功 
echo $LD_LIBRARY_PATH 
# 检查 nvida情况 
nvidia-smi