Difference between revisions of "ClassAccounts"

From UMIACS
Jump to navigation Jump to search
Line 59: Line 59:
 
* Account - <code>class</code>
 
* Account - <code>class</code>
 
* QoS - <code>default</code>, <code>medium</code>, and <code>high</code>
 
* QoS - <code>default</code>, <code>medium</code>, and <code>high</code>
 +
 +
===Example===
 +
Here is a basic example to schedule a interactive job running bash with a single GPU in the partition <code>class</code> with the account <code>class</code> running with the QoS of <code>default</code>.
 +
 +
<pre>
 +
$ srun --pty --partition=class --account=class --qos=default --gres=gpu:1 bash
 +
bash-4.4$ hostname
 +
tron14.umiacs.umd.edu
 +
bash-4.4$ nvidia-smi -L
 +
GPU 0: NVIDIA RTX A4000 (UUID: GPU-55f2d3b7-9162-8b02-50de-476a012c626c)
 +
</pre>
  
 
===Available Nodes===
 
===Available Nodes===

Revision as of 15:32, 23 August 2022

Overview

UMIACS Class Accounts are currently intended to support classes for all of UMIACS/CSD via the Nexus cluster. All new class accounts will be serviced solely through this cluster. Faculty may request that a class be supported by contacting staff@umiacs.umd.edu.

Getting an account

Your TA will request an account for you. Once this is done, you will be notified by email that you have an account to redeem. If you have not received an email, please contact your TA. You must redeem the account within 7 days or else the redemption token will expire. If your redemption token does expire, please contact your TA to have it renewed.

Once you do redeem your account, you will need to wait until you get a confirmation email that your account has been installed. This is typically done once a day on days that the University is open for business.

Registering for Duo

UMIACS requires that all Class accounts be registered for MFA (multi-factor authentication) under our Duo instance (note that this is different than UMD's general Duo instance). You will not be able to log onto the class submission host until you register.

In order to register, visit our directory app and log in with your Class username and password. You will then receive a prompt to enroll in Duo. For assistance in enrollment, you can visit our Duo help page.

Once notified that your account has been installed and you have registered in our Duo instance, you can access the following class submission host(s) using SSH with your assigned username and your chosen password:

  • nexusclass00.umiacs.umd.edu or nexusclass01.umiacs.umd.edu

Cleaning up your account before the end of the semester

Class accounts for a given semester will be archived and deleted after that semester's completion as early as the following:

  • Spring semesters: June 1st of same year
  • Summer semesters: September 1st of same year
  • Fall semesters: January 1st of next year

It is your responsibility to ensure you have backed up anything you want to keep from your class account's personal or group storage (below sections) prior to the relevant date.

Personal Storage

Your home directory has a quota of 20GB and is located at:

/fs/classhomes/<semester><year>/<coursecode>/<username>

where <semester> is either "spring", "summer", "fall", or "winter", <year> is the current year e.g., "2021", <coursecode> is the class' course code as listed in UMD's Schedule of Classes in all lowercase e.g., "cmsc999z", and <username> is the username mentioned in the email you received to redeem the account e.g., "c999z000".

You can request up to another 100GB of personal storage if you would like by having your TA contact staff. This storage will be located at

/fs/class-projects/<semester><year>/<coursecode>/<username>

Group Storage

You can also request group storage if you would like by having your TA contact staff to specify the usernames of the accounts that should be in the group. Only other class accounts in the same class can be added to the group. The quota will be 100GB multiplied by the number of accounts in the group and will be located at

/fs/class-projects/<semester><year>/<coursecode>/<groupname>

where <groupname> is composed of:

  • the abbreviated course code as used in the username e.g., "c999z"
  • the character "g"
  • the number of the group (starting at 0 for the first group for the class requested to us) prepended with 0s to make the total group name 8 characters long

e.g., "c999zg00".

Cluster Usage

You may not run computational jobs on any submission host. You must schedule your jobs with the SLURM workload manager. You can also find out more with the public documentation for the SLURM Workload Manager.

Any questions or issues with the cluster must be first made through your TA.

Class accounts only have access to the following submission parameters in SLURM. You may be required to explicitly set each of these in your submission parameters.

  • Partition - class
  • Account - class
  • QoS - default, medium, and high

Example

Here is a basic example to schedule a interactive job running bash with a single GPU in the partition class with the account class running with the QoS of default.

$ srun --pty --partition=class --account=class --qos=default --gres=gpu:1 bash
bash-4.4$ hostname
tron14.umiacs.umd.edu
bash-4.4$ nvidia-smi -L
GPU 0: NVIDIA RTX A4000 (UUID: GPU-55f2d3b7-9162-8b02-50de-476a012c626c)

Available Nodes

You can list the available nodes and their current state with the show_nodes -p class command. This list of nodes is not completely static as nodes may be pulled out of service to repair/replace GPUs or other components.

$ show_nodes -p class
NODELIST             CPUS       MEMORY     AVAIL_FEATURES            GRES                             STATE      PARTITION
tron06               16         128520     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron07               16         128520     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron08               16         128520     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron09               16         128520     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron10               16         128524     rhel8,Zen,EPYC-7313P      gpu:rtxa4000:4                   idle       class
tron11               16         128524     rhel8,Zen,EPYC-7313P      gpu:rtxa4000:4                   idle       class
tron12               16         128525     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron13               16         128520     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron14               16         128520     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron15               16         128520     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron16               16         128524     rhel8,Zen,EPYC-7313P      gpu:rtxa4000:4                   idle       class
tron17               16         128524     rhel8,Zen,EPYC-7313P      gpu:rtxa4000:4                   idle       class
tron18               16         128524     rhel8,Zen,EPYC-7313P      gpu:rtxa4000:4                   idle       class
tron19               16         128524     rhel8,Zen,EPYC-7313P      gpu:rtxa4000:4                   idle       class
tron20               16         128524     rhel8,Zen,EPYC-7313P      gpu:rtxa4000:4                   idle       class
tron21               16         128525     rhel8,AMD,EPYC-7302P      gpu:rtxa4000:4                   idle       class
tron22               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron23               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron24               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron25               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron26               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron27               16         128521     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron28               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron29               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron30               16         128521     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron31               16         128521     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron32               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron33               16         128521     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron34               16         128524     rhel8,Zen,EPYC-7313P      gpu:rtxa4000:4                   idle       class
tron35               16         128521     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron36               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron37               16         128521     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron38               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron39               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron40               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron41               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron42               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron43               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron44               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class
tron45               16         128525     rhel8,AMD,EPYC-7302       gpu:rtxa4000:4                   idle       class

You can also find more granular information about an individual node with the scontrol show node command.

$ scontrol show node tron27
NodeName=tron27 Arch=x86_64 CoresPerSocket=16
   CPUAlloc=0 CPUTot=16 CPULoad=0.00
   AvailableFeatures=rhel8,AMD,EPYC-7302
   ActiveFeatures=rhel8,AMD,EPYC-7302
   Gres=gpu:rtxa4000:4
   NodeAddr=tron27 NodeHostName=tron27 Version=21.08.8-2
   OS=Linux 4.18.0-372.19.1.el8_6.x86_64 #1 SMP Mon Jul 18 11:14:02 EDT 2022
   RealMemory=128521 AllocMem=0 FreeMem=125650 Sockets=1 Boards=1
   State=IDLE ThreadsPerCore=1 TmpDisk=0 Weight=10 Owner=N/A MCS_label=N/A
   Partitions=class,scavenger,tron
   BootTime=2022-08-18T17:34:44 SlurmdStartTime=2022-08-19T13:10:47
   LastBusyTime=2022-08-22T11:20:18
   CfgTRES=cpu=16,mem=128521M,billing=173,gres/gpu=4,gres/gpu:rtxa4000=4
   AllocTRES=
   CapWatts=n/a
   CurrentWatts=0 AveWatts=0
   ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s