Frequently Asked Questions
This page contains the answers to a few questions that we receive often. As more questions are asked, this page may be updated.
How do I get an account?
To request an account, follow the instructions and answer the questions on our Account Request page. We will reach out to you once your account is created, or if we have any questions for you.
I would like to log in from a new computer. Can I add a new ssh key?
If you have a new computer, or want to add keys for additional computers that you use, you can add your own key on our web portal. Instructions on how to generate a new ssh key and add it to your account are on our Account Request page. In summary, log in with your credentials (for MIT and other educational institutions this is the middle option when you go to https://txe1-portal.mit.edu) and then click on the "sshkeys" link. Scroll to the bottom and paste your key in the box.
How much storage do I have for my account?
We have set some guardrails on home and group directory storage. Home directories are set to 10TB and group directories are set to 50TB. It is recommended that users not use their accounts as primary storage. Further, we do not back up the storage on the system, so we strongly recommend transferring your code, data, and any other important files to another machine for backup.
How can I share files/code/data with my colleagues?
If you would like to share files with others you can request a shared group directory. Shared group directories are located at /home/gridsan/groups and we will put a symlink in your home directory to use as a shortcut to your shared group directory. To request one, send email to supercloud@mit.edu and let us know:
- What the group should be called. Short, descriptive names are best.
- Who should be the owner/approver for the group. We will ask this person for approval whenever we receive a request to join a group.
- Who should be in the group. SuperCloud usernames are helpful, but not required.
- Whether you plan to store any non-public data in the group. If you do, let us know what requirements, restrictions, or agreements are associated with the data. See why we ask here.
To learn more about Shared Groups and best practices using them, see the page on Shared Groups.
How do I set/change my password?
We do not use passwords on SuperCloud. If you have an active MIT Kerberos or login from another university, you can log in using your institution's credentials. On the SuperCloud Web Portal Login page, select the middle option "MIT Touchstone/InCommon Federation". You may have to select your institution from the dropdown list, which should take you to your institution's login page. After you log in, you should see the Portal main page. If you have trouble logging in this way, please contact us and we can help.
If you cannot log in using "MIT Touchstone/InCommon Federation" or one of the other options on the portal, we may set you up with a password. If you have not yet reset your password, or remember your previous password, then follow the instructions on the Web Portal page. If you have previously set your password and cannot remember it, contact us and we will help you reset your password.
Are there any resource limits?
New accounts are created with a small starting resource allocation. Once you have completed the Practical HPC course you can send your certificate to supercloud@mit.edu to request to be moved to the standard allocation. The starting and standard allocations are listed on the Systems and Software page.
If you have a deadline and need additional resources you can request more by contacting us. If you looking to request more GPUs, please read through this page on Optimizing your GPU Jobs first. Please state the number of additional processors you need, the length of time for which you need it, and tell us about the jobs you are running and how you are submitting them. If you plan to run many independent jobs we will ask you to convert your job to use triples before giving and increased allocation. Remember this is a shared system, so during busy times we may not be able to grant your request. We will also only grant increase requests if you have completed the Practical HPC course.
It is also important to keep in mind what your fair share of memory is for each process and request additional resources if needed. For example, if there are 40 cores and 384GB of RAM on the machine you are using, each processor's fair share would be about 9GB. Check the Systems and Software page to see how many cores and how much memory each node type has. If you think your processes will go over this, request additional slots as needed. This ensures you have sufficient memory without killing your job.
What do I do if my job won't be deleted?
Occasionally this will happen if the node where your job is running goes down, or your job does not exit gracefully. If this happens, contact us with the Job ID, and we'll delete the job and reboot the node if needed.
Why do I get an error when I try to install a package?
There are two common reasons you get an error when you try to install a package. If you get a "Permission Denied" or similar error, it is because you are trying to install the package system-wide, rather than your own home directory. See the Software and Package Management page for more information on how to install packages.
If you get a "Network Error", or similar, this is because we don't have internet/network connection on the compute nodes, this includes Jupyter and any interactive jobs. You will have to install the package on one of the login nodes.
If you get an error like
Could not install packages due to an EnvironmentError: [Errno 122] Disk quota exceeded
when installing a package with pip or something like
ERROR: could not download https://pkg.julialang.org/registry/...
installing a package with Julia, even though you are on the login node,
this is because it is filling up your quota in the /tmp directory. We
have set quotas on this directory to prevent a single person from
inadvertently filling it up, as when this happens it can cause issues
for everyone using the node, including preventing anyone from installing
packages. This can be fixed by setting the TMPIDR environment variable
like so:
After you have installed your package you can clean up any lingering files by removing the temporary directory you have created:
rm -rf /state/partition1/user/$USER
How can I set up VSCode to edit files remotely on SuperCloud?
You can use VSCode to remotely connect to SuperCloud via the Remote-SSH extension. The default settings in the VSCode Remote - SSH extension will fail to connect. This is due to it trying to lock files in your home directory, which is disabled for performance reasons.
The solution is to have it use the local filesystem. To get it to work, go to your VS Code settings, click "Extensions" and then "Remote - SSH". Once you're in the settings for Remote - SSH, check the box next to "Remote.SSH: Lockfiles in Tmp". What this will do is put any lockfiles in /tmp, rather than your home directory.
A side note: we have seen VS Code clutter up /tmp in the past, which we keep fairly small. Disconnecting occasionally should clean these up, however we do not know for sure. If you can check it once in a while and clean up any files that are yours in /tmp, that would be really helpful.
How can I use Tensorboard on SuperCloud?
Take a look at this page on how to run Tensorboard in an interactive job.
I got an Out of Memory error. How can I figure out how much memory my job needs and request more?
This is described on the Submitting Jobs page. If you submit your jobs
with sbatch, check out this
section, and if you use
LLsub take a look at this
section. As described in
those links, you can check how much memory your job used using the
sacct command, then request enough additional cores for the memory you
need. Keep in mind that if your job was killed due to high memory use,
your job may not have gotten to the point of highest memory use. To get
an accurate measurement you can run your job on an exclusive node long
enough to reach the part of the job that would consume the most memory,
then stop the job and check the memory use with sacct.
My Python/Julia job is running, but I don't see any output in the log files. What is going on?
Julia and Python will buffer output in batch jobs. This means they will
hold on to the output and print it out all at once, sometimes this
isn't until the end of a loop or the end of the program. You can force
both to print the output when it is produced. In Python you can do this
by using the -u flag when you call Python in your submission script
(ex: python -u myscript.py). In Julia you can do this by adding
flush(stdout) after the print statements in your Julia script that
you'd like to print immediately (ex:
println("Hello World!"); flush(stdout)).
What does the Underutilizing/Oversubscribing the node warning message mean?
When you launch a job in triples mode, the second and third numbers you provide are the number of processes per node (NPPN) and the number of threads per process (NT). You can multiply these two numbers to get the total number of threads you will have running on the node. You will usually get the best performance if you have the same total number of threads as cores on the node. See the Systems and Software page for a list of how many cores are on each node type.
Oversubscribing: When you have more threads than the number of cores on the node. Oversubscribing can overwhelm the node, which can slow your job down or even cause it to fail. It can also be harmful for the node. Reduce the number of threads per process or number of processes per node so that the total number of threads is less than or equal to the number of cores.
Underutilizing: When you have fewer threads than the number of cores on the node you may be undersubscribing. This means you may not be taking full advantage of the node. Many underlying packages and libraries can take advantage of multithreading. You might try increasing the number of threads per process, while avoiding oversubscribing, to see if you get improved performance.
For more information on how to pick the best triple for your job, take a look at the tuning process and recommendations.
How do I fix the GLIBCXX_3.4.29 not found error when using an Anaconda module?
You may encounter the following error when using anaconda/2023a.
ImportError: /usr/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found (required by /state/partition1/llgrid/pkg/anaconda/anaconda3-2023a/lib/python3.9/site-packages/pandas/_libs/window/aggregations.cpython-39-x86_64-linux-gnu.so)
The libstdc++ library containing the appropriate GLIBCXX version is found in the lib directory of the anaconda installation. Anaconda/2023a is installed at /state/partition1/llgrid/pkg/anaconda/anacond3-2023a. The anaconda library path has to be set in the LD_LIBRARY_PATH environment variable.
To set the LD_LIBRARY_PATH run:
If you are running a batch job, you can add this line to your job submission script. For interactive jobs, run this line before starting your application. For Jupyter, add this line to your llsc_notebook_bashrc file (see how here).
The above error may occur in other anaconda modules, you can find the anaconda installation directory by running the command which python, and set LD_LIBRARY_PATH variable accordingly.
How can I get more help?
If you have a question that is not answered here, send email to supercloud@mit.edu for more help.