使用Docker安装GPU版本caffe2

第一步 安装Docker

SET UP THE REPOSITORY

sudo apt-get remove docker docker-engine docker.io containerd runc

sudo apt-get update

sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg-agent \
    software-properties-common

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

sudo add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"

INSTALL DOCKER CE

sudo apt-get update

sudo apt-get install docker-ce docker-ce-cli containerd.io

apt-cache madison docker-ce

选一个  for example, 5:18.09.1~3-0~ubuntu-xenial
sudo apt-get install docker-ce= docker-ce-cli= containerd.io

sudo docker run hello-world
#运行通过就OK
docker version
#有版本提示就OK

第二步 安装Nvidia-Docker

# If you have nvidia-docker 1.0 installed: we need to remove it and all existing GPU containers
docker volume ls -q -f driver=nvidia-docker | xargs -r -I{} -n1 docker ps -q -a -f volume={} | xargs -r docker rm -f
sudo apt-get purge -y nvidia-docker
 
# Add the package repositories
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | \
  sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | \
  sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
 
# Install nvidia-docker2 and reload the Docker daemon configuration
sudo apt-get install -y nvidia-docker2
sudo pkill -SIGHUP dockerd
 
# Test nvidia-smi with the latest official CUDA image
docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi
#输出显卡信息就OK

第三步 安装Caffe2

docker pull caffe2ai/caffe2
 
# to test
nvidia-docker run -it caffe2ai/caffe2:latest python -m caffe2.python.operator_test.relu_op_test
 
# to interact
nvidia-docker run -it caffe2ai/caffe2:latest /bin/bash

第四步 测试

python -c 'from caffe2.python import core' 2>/dev/null && echo "Success" || echo "Failure"
#返回Success就OK
python2 -c 'from caffe2.python import workspace; print(workspace.NumCudaDevices())'
#返回1就OK
#进入python输入
from caffe2.python import workspace
#不报错就OK