Setup Tensorflow GPU using Docker on Ubuntu 18.04

setup_nvidia_docker.sh

#!/bin/bash

# This script is used to setup a working Tensorflow GPU installation using Docker.
# It's only tested on Ubuntu 18.04.
# When all is done, run the following command to get a Bash shell with Tensorflow:
# docker run --gpus all -u $(id -u):$(id -g) -v $PWD:/root -it --rm tensorflow/tensorflow:latest-gpu-py3 bash

# Setup Docker
# Instructions from https://docs.docker.com/install/linux/docker-ce/ubuntu/

# Update apt
sudo apt-get update && sudo apt-get upgrade

# Install packages to allow apt to use a repository over HTTPS
sudo apt-get install \
    apt-transport-https \
    ca-certificates \
    curl \
    gnupg-agent \
    software-properties-common
    
# Add Docker’s official GPG key
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -

# Add Docker's stable repository
sudo add-apt-repository \
   "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
   $(lsb_release -cs) \
   stable"
   
# Update apt again
sudo apt-get update

# Install Docker Engine
sudo apt-get install docker-ce docker-ce-cli containerd.io

# Test it out
sudo docker run hello-world

# Enable current user to run Docker
sudo usermod -aG docker $USER
newgrp docker

# NVIDIA Docker Container Runtime
# Instructions from https://github.com/NVIDIA/nvidia-docker

# Add NVIDIA repository 
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list

# Install container runtime
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

# Test it out
docker run --gpus all nvidia/cuda:9.0-base nvidia-smi

# Tensorflow
# Instructions from https://www.tensorflow.org/install/docker
docker run --gpus all -it --rm tensorflow/tensorflow:latest-gpu-py3 python -c \
  "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"