TensorFlow-gpu docker環境のインストールと構成

環境前提条件：NVIDIAのインストール® GPU driver

$sudo ls -lh /etc/modprobe.d/blacklist.conf

$sudo chmod 666 /etc/modprobe.d/blacklist.conf

blacklist vga16fb
blacklist nouveau
blacklist rivafb
blacklist rivatv
blacklist nvidiafb

$sudo service lightdm stop

	$sudo chmod a+x NVIDIA-Linux-x86_64-xxx.run
	$sudo ./NVIDIA-Linux-x86_64-xxx.run -no-x-check -no-nouveau-check -no-opengl-files

$sudo service lightdm start

$cat /proc/driver/nvidia/version

DokerメンテナンスのバージョンDocker CEのインストール:

$ curl -fsSL https://get.docker.com -o get-docker.sh
$ sudo sh get-docker.sh

$ sudo docker run hello-world

Nvidia-docker 2のインストール

# 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

GPU-support tensorflow

$ lspci | grep -i nvidia

docker run --runtime=nvidia --rm nvidia/cuda:9.0-base nvidia-smi

docker run --runtime=nvidia -it --rm tensorflow/tensorflow:latest-gpu \
python -c "import tensorflow as tf; print(tf.contrib.eager.num_gpus())"

docker run --runtime=nvidia -it tensorflow/tensorflow:latest-gpu bash

dockerでのJupter構成

docker run --runtime=nvidia -it -p 8888:8888 tensorflow/tensorflow:latest-gpu