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Click to start
OnDemand interactive applications can be launched from OnDemand with graphics, similar to a remote desktop that only launches the application.
After logging into OnDemand on your favorite ARCC HPC resource, you can request a Jupyter Session by clicking on the app from the main Dashboard:
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Fill out the Jupyter Session Request Form
After clicking the jupyter app, you are taken to a web form to tailor and specify the Jupyter environment you’d like to run in your session
Jupyter Interface: Select from Jupyter Notebook or Jupyter Lab
Account: The associated investment account or project you’re using to run the session
Number of hours: How long you plan to use the notebook
Number of Nodes: how many nodes you want allocated to perform work while using this notebook.
Number of CPUs: how many cores you will need access to perform your work while using this notebook.
Amount of Memory: Memory in GB required to run throughout the course of this Jupyer session
GPU Type: Which GPU hardware you’d like to perform your work in the Jupyter Notebook or Lab on
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Your interactive sessions
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When you click launch, you’re redirected to a page showing a list of your most recent interactive sessions.
The Slurm scheduler assigns a compute node with a specified number of cores, memory, hardware and timeframes as requested from the input you provided in your webform.
When your session is ready for use, the heading will turn green.
Completed sessions are denoted with gray headings
Pending sessions are denoted with blue headings
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Connect to your session
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To open Jupyter, click on the connect button within the active session
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Initial Screen Navigation and Options
Upon connecting, you are presented with a simple Jupyter Notebook screen and just a few options:
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Drop-Down Menu Bar
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Active Work Area
Whatever you’re currently working on
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Opening a New Blank Notebook
From the Dropdown: File->New->Notebook |  |
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From the Right side of the File Management Tab: New->Notebook |  |
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Kernels
A Jupyter kernel is the computational engine behind the code execution in Jupyter notebooks.
Most users think of this as the “compiler” or programming language used when running code cells.
The Kernel empowers you to execute code in different programming languages like Python, R, or Julia or others and instantly view the outcomes within the notebook interface.
After opening a new notebook, you will be prompted to select a kernel
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Default Kernels on ARCC HPC Resources include:
HPC-wide kernels are titled by packages installed and available when launched Users can also create user-defined kernels from conda environments (Covered in a subsequent module. See Creating Jupyter Kernels from Conda Environments) |   |
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