Page History

...

Containers are ubiquitous in the software industry, so I would recommend taking the time to read the official Docker documentation (https://docs.docker.com/get-started/). However, if you're in a hurry and just want to get a GPU-powered python environment up and running asap, the guide below should help you get started.

Step-by-step guide

1 - Making sure you have access to docker

By default (for security reasons), you should not have access to docker, even if you have access to the host machine. To check whether you have access, log into the host machine and run any docker command, e.g.:

> docker images 

> docker image list 

> docker ps

If you get an error message complaining about access rights, it means that you are not part of the docker group on the machine. Ask your supervisor to relay a request to one of the engineers at IDI, who should be able to grant you the necessary privileges.

2 - The Dockerfile - building an image
Our end goal is to make a container from which we can run our own code. However, to achieve this, we need to create something called an image first. An image is a prototype of a container; it serves as a premade snapshot that can be used to spawn any number of containers. An image is created from something called a Dockerfile, which in its most basic form is just a list of prerequisites you want installed and commands you want to run before every startup. The example below should be a nice starting point.

# Use the latest tf GPU image as parent. This operation is analogous to inheritance in OOP. 
# The image ships with tensorlfow-gpu and jupyter installed for python 2. It is also  
# configured so that a jupyter server will be launched at container startup. Note that you 
# don't have to use this image as parent. 
FROM tensorflow/tensorflow:latest-gpu 

# Set working directory for container 
WORKDIR /app  

# Make ssh directory (useful for adding ssh keys later) 
RUN mkdir -p /root/.ssh 

# Update repositories 
RUN apt-get update 

# Install git  
RUN apt-get install git -y 

# Install pip3 (parent image only comes with python2 stuff) 
RUN apt-get install python3-pip -y 

# Install your python packages  
RUN pip3 install --upgrade pip 
RUN pip3 install numpy 

# Add more pip installs here. Alternatively move everything to a dedicated requirements file.

3 - 

Related articles

...