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Christopher Creswell

Christopher Creswell

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Motivation and intended usage:

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To get an RStudio Server in the kubernetes cluster, you go to https://kubeportal.cc.lehigh.edu, then click on the "Class Apps" dropdown menu along the top of the page and selects "RStudio server (kubernetes)" for the generic RStudio instance in kubernetes, or the particular application for their course if applicable (a course can get its own entry here if it maps a network shared drive that is specific to the course).  You are brought to a form where you can select the version of R that they you want, and then you can click the "Launch" button.  It takes a minute or so for the instance to start, and then a "Connect to RStudio Server" button appears that takes you to your RStudio instance.

The RStudio Server instance will prompt you for your Lehigh username and password the first time you open it in a browser session.

User home directories:

The user's Your home directory that's mounted inside their your JupyterLab or RStudio Server instance is actually stored on a remote NFS server, so any files the users creates you create or modifies modify will persist after their RStudio instance your session ends.  The next time they you log in and create a new RStudio Server, the same files will still be there.  The home directory used by JupyterLab is different from RStudio Server though - you won't see the same files in both.

Session management:

You can get back to a running JupyterLab session by just going back to jupyter.cc.lehigh.edu and logging in again.  For as long as it is still running, the user's your RStudio Server is available at https://rstudio.cc.lehigh.edu/<username><your username>/.  The user   You can also get to it by going back to https://kubeportal.cc.lehigh.edu/ and clicking on the "My Interactive Sessions" link along the top menu bar.

Users You do not need to do anything to close their stop either your JupyterLab or RStudio Server instance.  It will be automatically cleaned up after a period of inactivity, currently set to 1 hour.

When the user is One caveat about this though - when you are finished using their RStudio Server, they will to avoid an annoying warning message the next time you come back to RStudio Server you may want to click the small red power-button icon in the upper-right corner of the browser window to tell RStudio to end their session.  If they .  This tells RStudio Server that you're leaving the session.  If you don't do this, the next time they you log in and get a new RStudio Server instance, they you will see a red error message in the RStudio console saying "ERROR The previous R session terminated abnormally" surrounded by some debugging information.  This error is unavoidable if the user doesnyou don't click the button to end their your session.  It's a harmless error - everything will still work fine, but it can be scary to see that on session startup. 

User management:

Anyone at Lehigh can get a JupyterLab instance by logging in at jupyter.cc.lehigh.edu, so there's no need to request anything to use it as an individual user.  If you are an instructor intending to use JupyterLab for a course, please do fill out this form to let us know that you'll be using it and include the course and number of students so we can make sure the system has the proper resources to handle demand:

https://jira.cc.lehigh.edu/servicedesk/customer/portal/1/create/254

In order to use the RStudio Server in the kubernetes cluster, users must exist in the OOD OpenOndemand application.  If you are an instructor and want to use this system with your a course, please create a help request for the Systems Engineering Team (SET) here:
https://jira.cc.lehigh.edu/servicedesk/customer/portal/1/create/254

Please include a list of all users of the system including all instructors, TAs, and students.  SET will make sure the proper accounts are provisioned for everyone.

Getting Support:

If you have an issue with one of these systems, please call the LTS Help Desk and have them put in a ticket for the Systems Engineering Team (SET).  Please say you're using the LTS JupyterHub or RStudio Server in kubernetes environment.  Include what class you're a part of, and work the the Help Desk staff to get a description of the problem into the ticket.  Screenshots are always helpful.

Known issues:

The following error is harmless:

2022-01-26T20:04:48.938685Z [rsession-<username>] WARNING No memory control group found in /proc/self/cgroup; LOGGED FROM: std::__cxx11::string rstudio::core::system::{anonymous}::getMemoryCgroup() src/cpp/core/system/LinuxResources.cpp:335

It shows up on starting an RStudio Server session.  We have not yet found a way to prevent it from happening.  The error is related to a version mismatch between kubernetes, the container engine it's using to run RStudio, and the OS running both.  It should eventually go away when we are able to upgrade everything to use the same version of "cgroups", which are used to control the resources granted to containers.  Everything will still work fine in spite of this error.

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Upon using the "plot" function in RStudio, this error may appear:

Warning message:
In grSoftVersion() :
  unable to load shared object '/usr/local/lib/R/modules//R_X11.so':
  libXt.so.6: cannot open shared object file: No such file or directory


This is also harmless - the plot function still works.  The error is caused by the way the container was built - the RStudio images are based on the Rocker project (https://www.rocker-project.org/ ) and this is related to the way they built the image.  An update to the image would fix it - we'll raise an issue in the Rocker project's github to make sure they are aware of it.