The ShARC HPC cluster was decommissioned on the 30th of November 2023 at 17:00. It is no longer possible for users to access that cluster.

5. Creating, editing and running Jupyter Notebooks

5.1. Creating Notebooks

After creating or deciding on a conda environment containing the Jupyter Kernel(s) you want to execute Notebook(s) with plus the packages want to import within Notebook(s) you can now create a Notebook or open an existing one.

To create a Notebook:

  1. Using the JupyterLab file browser, browse to the place you want to create your Notebook then

  2. Click File, New from Launcher then click the icon representing the Conda environment you want to start your Notebook in.

A blank Notebook should appear in a new JupyterLab tab. Your Notebook will have access to the packages installed in the selected Conda environment.

5.2. Opening existing Notebooks

To open an existing Notebook either:


If using Open from URL ensure the Notebook is from a reputable source.

5.3. Switching Jupyter Kernel

After opening a Notebook, you can change the Kernel used for executing code cells by clicking Kernel then Change Kernel… from the menu bar to bring up a list of available Kernels.

Some of the Kernels in this list correspond to Conda environments created by the system administrators; others were automatically found by a Jupyter plug-in that searches for valid Jupyter Kernels in all conda environments visible to you.

It is recommended that you create your own Conda environments (typically one per project/workflow).

Do not use the jupyterhub, jupyterhub2 or jupyterhub-dev environments. You are advised not to use the anaconda Kernels/environments either as these are read-only to most users and users have little control over if/when they are updated and what packages they contain.

5.4. Using Jupyter Notebooks

The basics of using Jupyter Notebooks to create self-describing, runable workflow documents are explained in the Jupyter Notebook official documentation.

5.4.1. Pyspark

if you want to use Pyspark with conda and Jupyter on ShARC then some extra configuration is required: see Using pyspark in JupyterHub sessions.