Docker

ML Docker Image installed on the Interaction Station ML computers (Ubuntu 16.04):

Installing Docker CE:

• sudo apt-get install apt-transport-https ca-certificates curl 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 xenial stable"
• sudo apt-get update

• More info: https://unix.stackexchange.com/questions/363048/unable-to-locate-package-docker-ce-on-a-64bit-ubuntu

Change Docker root dir using systemd (Don't do this, set volume instead)

• systemctl status docker.service
• sudo nano /etc/default/docker
• Edit ExecStart line to look like this ExecStart =/usr/bin/dockerd -g /media/MachineLearning/docker -H fd://
• systemctl daemon-reload
• systemctl restart docker
• sudo docker info - verify the root dir has updated
• https://github.com/IronicBadger/til/blob/master/docker/change-docker-root.md

Docker - clean up all the volumes

• sudo docker system prune -a -f --volumes

Installing nvidia-docker v1 (deprecated!):

• 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
• 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
• sudo apt-get install -y nvidia-docker
• 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
• Link:
• https://github.com/NVIDIA/nvidia-docker

Installing nvidia-docker-compose:

• pip install nvidia-docker-compose
• link: https://hackernoon.com/docker-compose-gpu-tensorflow-%EF%B8%8F-a0e2011d36
• Permission Denied on curl and save for docker compose: https://github.com/docker/machine/issues/652

Using Docker with nvidia-docker-compose

• Public docker repository (When doing FROM in Dockerfile, we need to select one of those)
• https://hub.docker.com/

• Dir structure:
• docker-compose.yml
• deepo
• deepo/do_not_finish.sh
• deepo/Dockerfile
• deepo_data (folder that is visible by deepo image)

• docker-compose.yml:

version: '3'
services:
  #machine name
  deepo:
    #container name
    container_name: deepo
    #path to Dockerfile
    build: deepo
    command: sh do_not_finish.sh
    volumes:
      - ./deepo_data:/media/deepo_data
    tty: true

• Dockerfile:

FROM ufoym/deepo ADD do_not_finish.sh /

• Dockerfiles guide:
• https://rock-it.pl/how-to-write-excellent-dockerfiles/

• do_not_finish.sh:

1. !/bin/bash

sh -c 'while :; do sleep 100; done'

• We need that endless loop, because docker-compose closes the container when is deployed
• The endless loop allowed us to use it with a docker exec

Run it

• Steps 1 and 2: Within the folder where is the docker-compose.yml file
• sudo nvidia-docker-compose build
• sudo nvidia-docker-compose up

• Step 3: From another terminal:
• sudo nvidia-docker exec -it deepo bash

Troubleshooting problems

• Check nvidia-docker version (needs to be version 1)
• nvidia-docker version
• More info:
• https://github.com/eywalker/nvidia-docker-compose/issues/26

• Permission denied: u'./docker-compose.yml
• https://github.com/docker/docker-snap/issues/26

Deepo

It includes:

• cudnn
• theano
• tensorflow
• sonnet
• pytorch
• keras
• lasagne
• mxnet
• cntk
• chainer
• caffe
• caffe2
• torch

Installing Deepo:

• Prerequisite 1: Nvidia driver
• Prerequisite 2: nvidia-docker
• Deepo (for GPU)

Run Deepo image with Docker:

• sudo nvidia-docker run -it ufoym/deepo:gpu bash

Run Deepo image with Docker (with python 2.7):

• sudo nvidia-docker run -it ufoym/deepo:py27 bash