ML Docker Image

Description

This repo contains a docker image with everything that is required to run som popular ML frameworks on the GPU, such as sklearn, Tensorflow and Keras. It also contains a setup with Jupyter notebooks, that will open in your browser. It also mounts whichever folder you are in when you launch the docker container.

Requirements

• docker installed on host
• nvidia-docker installed on host
• Nvidia GPU on host
• CUDA driver installed on host

How to install Docker and CUDA driver on host

Install docker

curl -O http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
dpkg -i ./cuda-repo-ubuntu1604_8.0.61-1_amd64.deb
apt-get update
apt-get install cuda -y

Verify that CUDA was installed correctly

nvidia-smi # Should display GPU details

Install nvidia-docker

wget https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
sudo dpkg -i nvidia-docker*.deb

Verify that nvidia-docker can access GPU

sudo nvidia-docker run --rm nvidia/cuda nvidia-smi # Should display the same as nvidia-smi except the processes

Launch the docker image

Build the image

docker build -t ml .

Launch the container

cd /path/to/project/folder # go to your project folder
sudo nvidia-docker run -v `pwd`/:/workspace/ --rm --name ml -p 8888:8888 -p 6006:6006 ml:latest jupyter notebook --allow-root

Misc

Launch bash in container

sudo docker exec -i -t ml /bin/bash