ClassAccounts

Overview

UMIACS Class Accounts are currently intended to support classes for the following units:

• Center for Machine Learning (CML) and its faculty via the CML cluster
• All of UMIACS/CSD via the Nexus cluster

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 have your TA contact staff to have it renewed. Staff will not renew any redemption tokens without TA approval.

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 the password you provided depending on the unit sponsoring the class:

• CML: class00.umiacs.umd.edu
• Nexus: 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

CML

You may not run computational jobs on the 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.

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

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

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
cmlgrad00            32         385421     Xeon,4216                 gpu:rtx2080ti:8           mix        class
cmlgrad01            32         385421     Xeon,4216                 gpu:rtx2080ti:8           alloc      class
cmlgrad02            32         385421     Xeon,4216                 gpu:rtx2080ti:7,gpu:rtx30 idle       class
cmlgrad03            32         385421     Xeon,4216                 gpu:rtx2080ti:8           mix        class
cmlgrad04            32         385421     Xeon,4216                 gpu:rtx2080ti:8           alloc      class
cmlgrad05            32         385421     Xeon,4216                 gpu:rtx3070:1,gpu:rtx2080 idle       class
cmlgrad06            32         385422     Xeon,4216                 gpu:rtx2080ti:8           alloc      class
cmlgrad07            32         385421     Xeon,4216                 gpu:rtx2080ti:8           mix        class

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

$ scontrol show node cmlgrad02
NodeName=cmlgrad02 Arch=x86_64 CoresPerSocket=16
   CPUAlloc=0 CPUTot=32 CPULoad=0.07
   AvailableFeatures=Xeon,4216
   ActiveFeatures=Xeon,4216
   Gres=gpu:rtx2080ti:7,gpu:rtx3070:1
   NodeAddr=cmlgrad02 NodeHostName=cmlgrad02 Version=20.11.8
   OS=Linux 3.10.0-1160.45.1.el7.x86_64 #1 SMP Fri Sep 24 10:17:16 UTC 2021
   RealMemory=385421 AllocMem=0 FreeMem=376637 Sockets=2 Boards=1
   State=IDLE ThreadsPerCore=1 TmpDisk=0 Weight=1 Owner=N/A MCS_label=N/A
   Partitions=class,scavenger
   BootTime=2021-11-18T17:39:23 SlurmdStartTime=2021-11-29T12:42:36
   CfgTRES=cpu=32,mem=385421M,billing=487,gres/gpu=8
   AllocTRES=
   CapWatts=n/a
   CurrentWatts=0 AveWatts=0
   ExtSensorsJoules=n/s ExtSensorsWatts=0 ExtSensorsTemp=n/s
   Comment=(null)

Nexus

TBD