gce_gpu_init.sh

#!/bin/bash
echo "Checking for CUDA and installing."
# Check for CUDA and try to install.
if ! dpkg-query -W cuda-9-0; then
  # The 16.04 installer works with 16.10.
  curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
  dpkg -i ./cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
  apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub
  apt-get update
  apt-get install cuda-9-0 -y
fi

# Enable persistence mode
nvidia-smi -pm 1

#!/bin/bash
echo "Checking for CUDA and installing."
# Check for CUDA and try to install.
if ! dpkg-query -W cuda-9-0; then
  curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
  dpkg -i ./cuda-repo-ubuntu1604_9.0.176-1_amd64.deb
  apt-get update
  apt-get install cuda-9-0 -y
fi

# 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
sudo docker run --runtime=nvidia --rm nvidia/cuda nvidia-smi


sudo nvidia-docker run --rm --name tf1 -p 8888:8888 -p 6006:6006 gcr.io/tensorflow/tensorflow:latest-gpu jupyter notebook --allow-root