The JupyterHub deployment that CISL manages allows "push-button" access to NCAR's Cheyenne supercomputing resource and the Casper cluster of nodes used for data analysis and visualization, machine learning, and deep learning.
It gives users the ability to create, save, and share Jupyter Notebooks through the JupyterLab interface and to run interactive, web-based analysis, visualization and compute jobs on Cheyenne and Casper. JupyterHub is an alternative to X11 access for interacting with those resources to run jobs as well as for using web-based interactive shell functionality without the need to install or use software such as SSH or PuTTY.
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Getting started
Use your web browser to go to jupyterhub.hpc.ucar.edu. Chrome and Firefox are recommended for all users.
Select Production.
Log in with your NCAR username and Duo two-factor authentication, just as you would when logging directly in to either system.
After you authenticate, you will be able to start a new default server or create a named server. (See following image.) You can have up to four named servers to use for accessing different compute resources.
After starting a server, select the cluster you want to use. You can choose to work on a login node or a batch node on either Casper or Cheyenne.
If you choose a login node, launching the server will take you to the web interface.
If you choose a batch node, use the form provided (images below) to specify your project code, set the necessary PBS job options, and launch the appropriate server. The name of your batch job will be STDIN.
For more information about the options, see:
Launch your job when ready. This job only gives you access to the JupyterLab instance. If you need more resources, you can launch another job or jobs from within JupyterLab.
After launching the job, you will have access to multiple kernels in the web interface (image below) for working with various languages and applications.
Note that the “File browser” icon (upper-left of following image) allows you to explore your home directory only. To change to your scratch or work space, create soft links in your home directory to those locations.
Python environments and kernels
The JupyterLab dashboard provides access to Notebook and Console kernels, which are programming language interpreters. Available kernels, which change periodically as new releases are installed, include:
- Multiple Python 3 interpreters with varying package support including a basic install (Python 3), the Pangeo stack installed with conda (Pangeo), and the NCAR Python Library (NPL) that is also provided at the command-line by the conda environment module.
- R
- MATLAB
- Julia
- Three C++ interpreters with different standards compliance
- A Bash interpreter that provides a shell environment
Related documentation
See these related CISL documentation pages for additional support: