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Open OnDemand, which is an NSF-funded open-source HPC portal, is available for users and provides web browser access for interfacing with HPC. This service is available from

File Browser

The file browser provides easy access to your /home, /xdisk, and /groups directories and allows you to view, edit, copy, and rename your files. You may also transfer small files between HPC and your local workstation using this interface. For larger transfers, see our section on Transferring Data for more efficient methods. 

Job Viewer and Job Composer

The Job Viewer allows you to check the status and time remaining of your running jobs. You can also cancel your jobs using this interface. Note: be careful looking at All Jobs since this will likely timeout trying to organize them all.

The Job Composer lets you create and run a SLURM script on any of our three clusters. It should be noted that the Job Composer creates a special string of directories in your /home starting with ondemand/ which is where both your submission scripts and output files will be stored. Make note of the path to your files on the right-hand side of the Job Composer screen under Script location.

Command Line Access

Need command line access to a terminal on HPC? No problem! Simply select the Clusters dropdown menu to connect to one of HPC's login nodes.

Interactive Graphical Applications

Open OnDemand provides access to graphical interfaces for some popular software. These can be found under Interactive Apps through the Open OnDemand web browser. The process of starting and accessing these applications is the same. 

First, select the desired application from Interactive Apps. This will take you to a form where you will enter your job information. This includes HPC cluster (Puma, Ocelote, or ElGato) core (CPU) count, time, number of GPUs, your desired queue (partition), and group's name. Note: the memory reserved for your job is dependent on the number of cores you request. For example, you can reserve up to 5GB of RAM per CPU on Puma. For more information on memory resources, see our Compute Resources documentation.

Once you've entered all your details, click Launch at the bottom of the page. This will take you to a tile with information about your job including job ID and session ID. This information can used for debugging purposes.

When you first submit your job, it will show as having a status of "Queued". Once your job reaches the front of the queue, it will show a status of "Starting". When your session is ready, you can launch the application using Connect at the bottom of the tile.

When selecting a job queue for any interactive applications, we recommend against using windfall. These jobs/sessions can be preempted at any time and your progress will be lost.

If you need a GPU, then add "gpu" to the Special Options field

Interactive Virtual Desktops

One nice feature of Open OnDemand is the ability to interact with HPC using a virtual Desktop environment. This provides a user-friendly way to run applications, perform file management, and navigate through your directories as though you were working with a local computer. Additionally, it eliminates the need to use X11 forwarding when working with GUI applications allowing an easy way to interact with software such as Matlab, VisIt, or Anaconda.

Jupyter Notebooks

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text. 

Default Directory

When you start a Jupyter notebook, by default your working directory will be your home. If you would like to change this so that your session starts in a different location, you'll need to add a line to the hidden file ~/.bashrc in your home. To do this, open your ~/.bashrc in a text editor and add the following, substituting your desired path in for /path/to/directory:

export NOTEBOOK_ROOT=/path/to/directory


Various Kernels are available to use through this OOD Application including Python, R, and Bash.

Additionally, you have the ability to create your own python kernels to control python version and provide access to your own custom environments. 

You can also create kernels to enable access to other software applications. For example, if you want to add a kernel for Julia, you can log into an interactive terminal session and use the Julia module to add IJulia. When you log into your next Jupyter session, you should see the kernel in the "new" dropdown menu:

$ module load julia
$ julia

   _       _ _(_)_     |  Documentation:
  (_)     | (_) (_)    |
   _ _   _| |_  __ _   |  Type "?" for help, "]?" for Pkg help.
  | | | | | | |/ _` |  |
  | | |_| | | | (_| |  |  Version 1.6.1 (2021-04-23)
 _/ |\__'_|_|_|\__'_|  |  Official release
|__/                   |

julia> using Pkg
julia> Pkg.add("IJulia")


RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. For an overview of the RStudio IDE, see: Additionally, practice examples are available from

For information on using R on HPC, see our online documentation on Using and Customizing R Packages.


A GUI for multiple versions of Matlab is available.


Multiple versions of the engineering application Ansys are available. 


A GUI for Abaqus is available. 


Anaconda is available as a module and can be accessed through an interactive terminal session using the standard module commandsSome information on creating custom conda environments can be found in our Using and Installing Python documentation. 

Anaconda also comes with a graphical interface which is best accessed through a virtual desktop (see earlier section).  To access the GUI, start an interactive desktop session, open the terminal, then:

$ module load anaconda/<version>
$ anaconda-navigator

This will bring up:

You can launch Glueviz, JupyterLab, Jupyter Notebook and Spyder from here.  To launch Orange or RStudio, you have to change the dropdown next to "Applications on". 

You may find this Guide to Anaconda an interesting read.

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