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

Installing Docker CE:

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

Docker - clean up all the volumes

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

Installing nvidia-docker v1 (deprecated!):

Installing docker-compose:

Installing nvidia-docker-compose:

Using Docker with nvidia-docker-compose

  • Dir structure:
  • docker-compose.yml
  • deepo
  • deepo/
  • deepo/Dockerfile
  • deepo_data (folder that is visible by deepo image)
  • docker-compose.yml:
version: '3'
  #machine name
    #container name
    container_name: deepo
    #path to Dockerfile
    build: deepo
    command: sh
      - ./deepo_data:/media/deepo_data
    tty: true
  • Dockerfile:

FROM ufoym/deepo ADD /

  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


It includes:

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

Installing Deepo:

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