About Jupyter


This page provides some guidance regarding Jupyter. For a full guide and reference, you should take a look at the JupyterLab docs (though by using the Swarm VRE, you can ignore the sections about installing Jupyter yourself).


Jupyter can be confusing! So here is a handy list of terms:

Project Jupyter

The broader open source project and community.


Jupyter Notebook

A notebook document (file extension .ipynb) is a document that can be rendered in a web browser that can contain text, mathematics, images, live code, and other interactive elements. “Jupyter Notebook” can also refer to the web application that builds and runs such notebooks.



This is the evolution of the Jupyter Notebook software providing a richer interface. It is the default interface for the VRE when you first login.



This software runs on servers to provide a multi-user system which automatically spawns and manages instances of JupyterLab/Notebook


Jupyter kernel
When used with reference to Jupyter, a “kernel” refers to the program which executes code cells within a notebook. Only one kernel at a time can be connected to a notebook. In the VRE we currently provide two kernels: Python 3.7 (with a number of packages installed), and Octave.
containers, images, and Docker

A container image is a unit of software that holds everything needed to run some particular code. An image is loaded into a container, which is an isolated environment where your code can run with a dependable environment based on the image - this is a bit like a virtual machine. Docker is a software system for working with such containers. The environment that a particular image provides is specified by a Dockerfile (e.g. jupyter/scipy-notebook).

We employ our own Docker image which is built on top of jupyter/scipy-notebook, providing access to JupyterLab and a selection of software relevant to Swarm.

Quick tips

There are some quirks about the behaviour of Jupyter that can be surprising and confusing:

  • There is often effectively two right-click menus. To access the other one, hold down shift and then right-click

    • This might vary between browsers and systems (?)
    • This is most noticeable when trying to copy and paste
  • When using the terminal, Ctrl+c and Ctrl+v will not copy and paste. The keyboard shortcuts for copy and paste are Ctrl+Insert and Shift+Insert (this is a normal Unix thing)

  • When using notebooks:

    • Press Ctrl+Enter to execute a cell
    • Press Shift+Enter to execute a cell and advance to the next one
    • There are many more keyboard shortcuts to learn that can help to manipulate notebooks faster
    • Be aware that the execution state of your notebook (i.e. the variables created/changed etc.) is affected by all the cell executions you have made. It is good practice to occasionally refactor and re-run cells in the correct order, and from a fresh un-executed state, as you are developing a notebook, so as to ensure that the notebook outputs will be the same when it is run from scratch. A common way to achieve this is to go to the menu, to Kernel / Restart Kernel and Run All Cells
    • You can switch which kernel the notebook is using to run code, by clicking on the kernel name e.g. “Python 3” at the top right of the notebook.
  • The default view when logging in is to load the “JupyterLab” interface. To get the old single-notebook view, go to the menu, to Help / Launch Classic Notebook. (This can be necessary for compatibility with some extensions which have not yet been ported to JupyterLab) This is simply a different view for interacting with the system - the software is otherwise identical.

  • Installing things, e.g. using pip or conda won’t behave the same as on a normal machine. See Software available for more.

  • If you find other things, or otherwise struggling to achieve some task, please contact us.