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.
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 service (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:
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.
There are three partitions to the CML SLURM computational infrastructure. If you do not specify a partition when submitting your job, you will receive the dpart partition.
- dpart - This is the default partition and 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 the dpart partition are ready to be scheduled.
- cpu - This partition is for CPU focussed jobs and the job allocations are guaranteed.
The Center has a base account
cml which has a modest number of nodes (currently 16 GPUs) total available in it. 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 GPU resources corresponding to the amount that they invested. If you do not specify a account when submitting your job, you will receive the
# sacctmgr show accounts Account Descr Org ---------- -------------------- -------------------- abhinav abhinav shrivastava cml cml cml cml furongh furong huang cml john john dickerson cml root default root account root scavenger scavenger scavenger sfeizi soheil feizi 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 email@example.com and CC your faculty member 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.
$ sacctmgr show assoc account=tomg format=user,account,qos,grptres User Account QOS GrpTRES ---------- ---------- -------------------- ------------- tomg gres/gpu=24
CML currently has 3 QoS for the dpart partition, 1 QoS for the scavenger partition and 1 QoS for the cpu partition. You are required to specify a QoS when submitting your job. 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 ---------- ----------- ------- ------------------------------ ------------- medium 3-00:00:00 1 cpu=8,mem=64G,gres/gpu=2 default 7-00:00:00 2 cpu=4,mem=32G,gres/gpu=1 high 1-12:00:00 1 cpu=16,mem=128G,gres/gpu=4 scavenger 3-00:00:00 gres/gpu=36 cpu 1-00:00:00 1
Jobs that require GPU resources need to explicitly request the resources within their job submission. This is done through Generic Resource Scheduling (GRES). Currently all nodes in the cluster are homogeneous, however in the future this may not be the case. 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 (ie.
--gres gpu:4 for 4 GPUs).
$ sinfo -o "%15N %10c %10m %25f %25G" NODELIST CPUS MEMORY AVAIL_FEATURES GRES cml[00-09] 32 1+ (null) gpu:rtx2080ti:8
Job Submission and Management
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
[derek@cmlsub00:~ ] $ srun --pty --gres=gpu:1 --mem=16G --qos=default --time=04:00:00 bash [derek@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
A quick example to run an interactive job using the cpu partition. The cpu partition uses the default account
-bash-4.2$ srun --partition=cpu --qos=cpu bash -c 'echo "Hello World from" `hostname`'
Until the final storage investment arrives we have made available a temporary allocation of storage. This section is subject to change. There are 3 types of storage available to users in the CML:
- Home directories
- Project directories
- Scratch directories
Home directories in the CML computational infrastructure are available from the Institute's NFShomes as
/nfshomes/USERNAME where USERNAME is your username. These home directories have very limited storage and are intended for your personal files, configuration and source code. Your home directory is not intended for data sets or other large scale data holdings. Users are encouraged to utilize our GitLab infrastructure to host your code repositories.
NOTE: To check your quota on this directory you will need to use the
quota -s command.
Your home directory data is fully protected and has both snapshots and is backed up nightly.
Users within the CML compute infrastructure can request project based allocations for up to 2TB for up to 120 days from firstname.lastname@example.org with approval from a CML faculty member and the director of CML. These allocations will be available from /fs/cml-projects under a name that you provide when you request the allocation. Once the allocation period is over, the user will be contacted and give a window of opportunity to clean and secure their data before staff will remove the allocation.
This data is backed up nightly.
Scratch data has no data protection including no snapshots and the data is not backed up. There are two types of scratch directories in the CML compute infrastructure:
- Network scratch directory
- Local scratch directories
Network Scratch Directory
Users granted access to the CML compute infrastructure are each allocated 400GB of network attached scratch. This is available as
/cmlscratch/USERNAME where USERNAME is your username. This directory is automounted so you will need to
cd into the directory or request/specify a fully qualified file path to access this.
Users may request an additional allocation of scratch space up to a total of 800GB by contacting email@example.com.
Local Scratch Directories
Each computational node that a user can schedule compute jobs on has one or more local scratch directories. These are always named
/scratch1, etc. These are almost always more performant than any other storage available to the job. However, users must stage their data within the confine of their job and stage the data out before the end of their job.
These local scratch directories will have a tmpwatch job which will delete unmodified data after 120 days. Please make sure you secure any data you write to these directories at the end of your job.
We have read-only dataset storage available for the Center at
/fs/cml-datasets. If there are other datasets that you would like to see curated and available, please see this page.
The following is the list of datasets available:
|Diversity in Faces||/fs/cml-datasets/diversity_in_faces|