Available Options for Starting and Connecting to a Jupyter Notebook Server
Jupyter Notebooks are a popular web-based development environment for teaching, testing and development and running code. Notebooks allow seamless integrations of live code, richly formatted text, images, visualizations, cleanly formatted equations and more. Jupyter supports many programming languages, but is most often associated with Python.
UF Research Computing offers several methods to run Jupyter. This page provides general information about Jupyter, Jupyter Notebooks and Jupyter Lab. For details on starting Jupyter on HiPerGator, please see the pages below for detailed information on each option. In general, these options are listed in the order of ease of use.
Jupyter via Open OnDemand
Standalone Jupyter Notebook
Accessing Blue and Orange Directories
Exporting Notebooks as Executable Scripts
Notebooks are a great method for testing and development, but can be cumbersome when it comes to production runs. It is simple to export a Jupyter Notebook as an executable script (.py file for example).
- Select File > Export Notebook As... > Export Notebook to Executable Script.
UFRC Managed Kernels
We will happily add python or R packages/modules to available environments/kernels. Use the RC Support System to request package installs. All RC managed Jupyter kernels are based on environment modules that can also be loaded in an interactive terminal session or in job scripts with 'module load'. Users can also install their own personal packages with methods described in the R FAQ section.
We provide custom kernels named '
RC-py3-$version' and '
RC-R-$version' that provide access hundreds of R packages and python3 modules we installed to support exploratory research and code writing by UF researchers on request. Use https://support.rc.ufl.edu to request additional package and module installs. Note that the shared python3 and R environments can only have one package/module version to avoid conflicts. Use python virtualenv or conda environments to have custom module installs for particular projects as shown below.
All other kernels are application-specific. Their installation requests are documented in our support system and on this help site.
For directions on setting up your own Julia kernel, please see the Julia page.
Users can define their own Jupyter kernels for use in JupyterHub. See https://jupyter-client.readthedocs.io/en/stable/kernels.html
In short, kernel definitions can be put into
~/.local/share/jupyter/kernels directory. See
/apps/jupyterhub/kernels/ for examples of how we define commonly used kernels. You can also copy a template kernel from
/apps/jupyterhub/template_kernel. Replace the placeholder paths and strings in the template files
kernel.json in accordance to your conda environment configuration.
Note: Even though the
kernel.json defines the
display_name, the folder name must also be unique. You cannot just copy a folder and update the contents of the
run.sh files, you also need to rename the folder.
To troubleshoot issues with personal kernels, check the log files at