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The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text. It is an excellent interface to explore your data and share your research.

The JupyterHub is a web interface for running notebooks on the iceberg cluster. It allows you to login and have a notebook server started up on the cluster, with access to both your data stores and the resources of iceberg.


This service is currenty experimental, if you use this service and encounter a problem, please provide feedback to


Iceberg’s JupyterHub service is offline for maintainance between 02:00-03:00 on the Monday immediately before the 2nd Tuesday of the month.

Logging into the JupyterHub

To get started visit and log in with your university account. A notebook session will be submitted to the iceberg queue once you have logged in, this can take a minute or two, so the page may seem to be loading for some time.


There is currently no way of specifying any options to the queue when submitting the server job. So you can not increase the amount of memory assigned or the queue the job runs in. There is an ongoing project to add this functionality.

Using the Notebook on iceberg

To create a new notebook session, select the “New” menu on the right hand side of the top menu.


You will be presented with a menu showing all available conda environments that have Jupyter available. To learn more about installing custom Python environments with conda see Python.

To learn how to use the Jupyter Notebook see the official documentation.

Using the Notebook Terminal

In the “New” menu it is possible to start a terminal, this is a fully featured web terminal, running on a worker node. You can use this terminal to perform any command line only operation on iceberg. Including configuring new Python environments and installing packages.



Below is a list of common problems:

  1. If you modify the PYTHON_PATH variable in your .bashrc file your jupyter server may not start correctly, and you may encounter a 503 error after logging into the hub. The solution to this is to remove these lines from your .bashrc file.
  2. If you have previously tried installing and running Jupyter yourself (i.e. not using this JupyterHub interface) then you may get 503 errors when connecting to JupyterHub due to the old .jupyter profile in your home directory; if you then find a jupyter log file in your home directory containing SSL WRONG_VERSION_NUMBER then try deleting (or renaming) the .jupyter directory in your home directory.