Jupyter Lab is available on our HPC facilities to all users.
The only supported route to running Jupyter is:
Previous Workarounds
Whilst Jupyter was never supported on Rocket, several users tried a number of workarounds to get it running, including setting up port forwards, starting long-running processes on one or more login nodes or similar. These are no longer supported and are against the acceptable use policies.
The only method you should use to run Jupyter on Comet is described below.
To run the full desktop version of Jupter Lab, visit our Comet HPC Open OnDemand website:
From there you can elect to create a new JupyterInteractive App using the following form:
Note that you are able to select from multiple Jupyter Lab versions - choose the one most appropriate for your needs.
Ensure that you choose the correct Slurm partition for your Jupyter instance. Consult our Comet HPC Resources & Partitions page to understand the difference between the available Slurm partitions.
Once you submit the form the system will schedule your request and shortly create a new dedicated Jupyter server for you, this will open in your browser, and you will be able to run normal Python code and scripts. Full access to all the regular Comet filesystems is possible, and you can request up to 256 CPU cores for your session, plus multiple Nvidia GPU cards.
The application works as normal, you can load and save files and run code as you would normally, but you have the advantage of being able to use all of the resources of a Comet compute node.
You may close the browser and/or browser tab at any time and reconnect to the running application later - as long as this is within the time limit you set for the session at the point you submitted the form. Runtime limits for interactive sessions are detailed on the Comet HPC Resources & Partitions page.
When you are finished running Jupyter, use the “Shut Down” option from the File menu.
Running Jupyter Lab over SSH + X11 is not supported.
Starting Jupyter Lab from the command line is not supported.
It should be possible to install most Python modules from within Jupyter. Please note that after installing a module you should choose the “Restart Kernel” option from the Kernel menu to refresh the list of installed modules.
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