Using Jupyter on the Quest Analytics Nodes
Jupyter notebooks are available on the Quest Analytics nodes.
The Quest Analytics nodes allow users with active Quest allocations to launch Jupyter notebooks from a web browser. See Research Computing: Quest Analytics Nodes for an overview of the system.
Note: you cannot submit jobs to the Quest job scheduling system using the Quest Analytics nodes.
Users must already have a Quest account and an active Quest allocation.
Connections to the Quest Analytics Nodes are limited to the Northwestern network. If you are connecting from off-campus, you must use the Northwestern VPN.
Using any browser, connect to: https://jupyter.questanalytics.northwestern.edu and sign in with your NetID and password.
Note: notebook servers are automatically killed after 18 hours of inactivity.
You can also transfer files smaller than 500MB using the the Jupyter web interface. There is an Upload button that will allow you to select files from your computer to transfer.
When you connect to Jupyter, you will initially be in your home directory. This is /home/<netid> on Quest. You cannot navigate to a directory "outside" of your home directory (e.g., to a project directory) via the Jupyter interface. However, you can SSH to Quest and use the terminal to create a symlink to a project directory within your home directory, allowing you to navigate there. For example:
ln -s /projects/<projectID> ~/<projectID>
This will create a symlink <projectID> in your home directory, and when you click on it in the Jupyter interface you will be taken to the project directory itself.
To transfer files larger than 500MB to Quest, please use SFTP or another transfer method.
The default "root" environment for Jupyter on Quest Analytics is Python 3.7 and Anaconda version 2018.12. The list of installed packages and versions in this environment can be found here: https://docs.anaconda.com/anaconda/packages/old-pkg-lists/2018.12/py3.7_linux-64/.
If you need other packages (or versions of packages) installed, you can create a conda environment and add it to your list of available environments using the commands below (Note you must SSH or use FastX to access Quest and type these commands in the terminal):
module load anaconda3/2018.12 conda create -n <name> python=3.7 ipykernel <package1> <package2> ... # or other python version source activate <name> python -m ipykernel install --user --name <name>
The last command adds the environment to your list of available environments in Jupyter, and once you have done this it will be available for you under the "New" menu.
For more information about managing environments with conda, see Using Python on Quest.
The Quest Analytics nodes are shared by many researchers. Please be aware of your memory use when analyzing large data sets. Users utilizing a large amount of memory, especially those using over 60GB of RAM, may be asked to move their analysis to other systems. Multicore and parallel processes should not be run on the Analytics nodes. Users needing to run computationally intensive jobs should schedule interactive or batch jobs on Quest instead of using the Analytics nodes. Please contact Research Computing at email@example.com with questions about memory use or analyzing large data sets.
Issues or Problems with the Quest Analytics nodes
To report issues or problems with the Quest Analytics nodes, please email firstname.lastname@example.org with information on which service you were using (Jupyter), the error you received or problem you encountered, and what you were doing prior to the problem occurring. Please note that you were using the Quest Analytics nodes.