JupyterHub allows users to interact with the Palmetto cluster from their web browsers using the Jupyter Notebook interface, and to prototype and develop code in Python, R, MATLAB and several other languages.

Logging In

  1. Start by visiting https://www.palmetto.clemson.edu/jupyterhub.
  2. Log in with your Palmetto user ID and password:
  3. Once you are logged in, click on “Start my server” to start a notebook server.
  4. Select the resources (CPU cores, memory, walltime, etc.,) required for your session.
  5. If the resources you request are available, a notebook server will be started for you. This will open up the Notebook dashboard, where you will see the files and directories in your “home” directory on the Palmetto cluster.

About JupyterHub and Jupyter Notebook

JupyterHub allows users to run Jupyter Notebook using remote computing resources such as an HPC cluster. You can also download and use Jupyter Notebook locally on your own machine.

Jupyter Notebook is a web-based interactive computing environment that enables users to create and share documents that contain live (runnable) code, equations, visualizations, interactive plugins, and explanatory text. Some important features of the Jupyter notebook are:

  1. Data cleaning and exploration: Jupyter offers a rich, interactive computing environment where users can load, clean, analyze and visualize their data. Jupyter also supports interactive plugins–or “widgets”–for exploring datasets in real-time.

  2. Prototyping and algorithm development: Jupyter Notebooks are the perfect environment for exploratory analyses, algorithm development and prototyping. Whether you are solving complex mathematical equations, or trying to improve the performance of your code, the interactive Notebook interface allows you to prototype changes quickly and get immediate feedback.

  3. Reproducible analyses: Jupyter Notebooks are a complete and self-contained record of a computation or pipeline, that can be converted to various formats and shared with others using email, Dropbox, GitHub or the nbviewer.

  4. Multi-language support: Jupyter supports several languages used in scientific computing and data analysis, including Python, R, Scala, Julia and many more.

A collection of Jupyter Notebooks created for research and teaching across several fields in scientific computing, data analysis and related fields is available here.