CML

The Center for Machine Learning (CML) at the University of Maryland is located within the Institute for Advanced Computer Studies. The CML has a cluster of computational (CPU/GPU) resources that are available to be scheduled.

As of the August 2023 maintenance window, all compute nodes have moved into the Nexus cluster. Please see Nexus/CML for more details.

Compute Infrastructure

Each of UMIACS' cluster computational infrastructures is accessed through the submission node. Users will need to submit jobs through the SLURM resource manager once they have logged into the submission node. Each cluster in UMIACS has different quality of services (QoS) that are required to be selected upon submission of a job. Many clusters, including this one, also have other resources such as GPUs that need to be requested for a job.

The current submission node(s) for CML are:

• cmlsub00.umiacs.umd.edu

The Center for Machine Learning GPU resources are a small investment from the base Center funds and a number of investments by individual faculty members. The scheduler's resources are modeled around this concept. This means there are additional Slurm accounts that users will need to be aware of if they are submitting in the non-scavenger partition.

Partitions

There are three partitions available to general CML SLURM users. If you do not specify a partition when submitting your job, you will receive the dpart partition.

• dpart - This is the default partition. Job allocations are guaranteed.
• scavenger - This is the alternate partition that allows jobs longer run times and more resources but is preemptable when jobs in other partitions are ready to be scheduled.
• cpu - This partition is for CPU focused jobs. Job allocations are guaranteed.

There is one additional partition available solely to Furong's sponsored accounts.

• furongh - This partition is for exclusive priority access to Furong's purchased A6000 node.

Accounts

The Center has a base SLURM account cml which has a modest number of guaranteed billing resources available to all cluster users at any given time. Other faculty that have invested in the cluster have an additional account provided to their sponsored accounts on the cluster, which provides a number of guaranteed billing resources corresponding to the amount that they invested. If you do not specify an account when submitting your job, you will receive the cml account.

$ sacctmgr show accounts
   Account                Descr                  Org
---------- -------------------- --------------------
   abhinav  abhinav shrivastava                  cml
       cml                  cml                  cml
   furongh         furong huang                  cml
  hajiagha  mohammad hajiaghayi                  cml
      john       john dickerson                  cml
    ramani    ramani duraiswami                  cml
      root default root account                 root
 scavenger            scavenger            scavenger
    sfeizi         soheil feizi                  cml
   tokekar       pratap tokekar                  cml
      tomg        tom goldstein                  cml

You can check your account associations by running the show_assoc to see the accounts you are associated with. Please contact staff and include your faculty member in the conversation if you do not see the appropriate association.

$ show_assoc
      User    Account   Def Acct   Def QOS                                  QOS
---------- ---------- ---------- --------- ------------------------------------
      tomg       tomg                                       default,high,medium
      tomg        cml                                        cpu,default,medium
      tomg  scavenger                                                 scavenger

You can also see the total number of Track-able Resources (TRES) allowed for each account by running the following command. Please make sure you give the appropriate account that you are looking for. The billing number displayed here is the sum of resource weightings for all nodes appropriated to that account.

$ sacctmgr show assoc account=tomg format=user,account,qos,grptres
      User    Account                  QOS       GrpTRES
---------- ---------- -------------------- -------------
                 tomg                       billing=8107

QoS

CML currently has 5 QoS for the dpart partition (though high_long and very_high may not be available to all faculty accounts), 1 QoS for the scavenger partition, and 1 QoS for the cpu partition. If you do not specify a QoS when submitting your job, you will receive the default QoS. The important part here is that in different QoS you can have a shorter/longer maximum wall time, a different total number of jobs running at once, and a different maximum number of track-able resources (TRES) for the job. In the scavenger QoS, one more constraint that you are restricted by is the total number of TRES per user (over multiple jobs).

$ show_qos
        Name     MaxWall MaxJobs                        MaxTRES                      MaxTRESPU              GrpTRES
------------ ----------- ------- ------------------------------ ------------------------------ --------------------
      medium  3-00:00:00       2       cpu=8,gres/gpu=2,mem=64G
     default  7-00:00:00       2       cpu=4,gres/gpu=1,mem=32G
        high  1-12:00:00       2     cpu=16,gres/gpu=4,mem=128G
   scavenger  3-00:00:00                                                           gres/gpu=24
      normal
         cpu  7-00:00:00       8
   very_high  1-12:00:00       8     cpu=32,gres/gpu=8,mem=256G                    gres/gpu=12
   high_long 14-00:00:00       8              cpu=32,gres/gpu=8                     gres/gpu=8

GPUs

Jobs that require GPU resources need to explicitly request the resources within their job submission. This is done through Generic Resource Scheduling (GRES). Users may use the most generic identifier (in this case gpu), a colon, and a number to select without explicitly naming the type of GPU (i.e. --gres=gpu:4 for 4 GPUs of any type).

$ sinfo -o "%20N %10c %10m %25f %40G"
NODELIST             CPUS       MEMORY     AVAIL_FEATURES            GRES
cmlgrad[02,05]       32         385421     Xeon,4216                 gpu:rtx2080ti:7,gpu:rtx3070:1
cml[00-11,13-16],cml 32         353924+    Xeon,4216                 gpu:rtx2080ti:8
cmlcpu[01-04]        20         386675     Xeon,E5-2660              (null)
cmlcpu[00,06-07]     24         386675+    Xeon,E5-2680              (null)
cml12                32         385429     Xeon,4216                 gpu:rtx2080ti:7,gpu:rtxa4000:1
cml[17-29]           32         257654     Zen,EPYC-7282             gpu:rtxa4000:8

Job Submission and Management

Users should review our SLURM job submission and job management documentation.

A very quick start to get an interactive shell is as follows when run on the submission node. This will allocate 1 GPU with 16GB of memory (system RAM) in the QoS default for 4 hours maximum time. If the job goes beyond these limits (either the memory allocation or the maximum time) it will be terminated immediately.

srun --pty --gres=gpu:1 --mem=16G --qos=default --time=04:00:00 bash

[username@cmlsub00:~ ] $ srun --pty --gres=gpu:1 --mem=16G --qos=default --time=04:00:00 bash
[username@cml00:~ ] $ nvidia-smi -L
GPU 0: GeForce RTX 2080 Ti (UUID: GPU-20846848-e66d-866c-ecbe-89f2623f3b9a)

If you are going to run in a faculty account instead of the default cml account you will need to specify the --account= flag.

A quick example to run an interactive job using the cpu partition. The cpu partition uses the default account cml.

-bash-4.2$ srun --partition=cpu --qos=cpu bash -c 'echo "Hello World from" `hostname`'

Data Storage

Information on data storage available in CML's computational infrastructure can be found here.