Difference between revisions of "CML"
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You can request project based allocations for up to 6TB for up to 120 days by [[HelpDesk | contacting staff]] 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. Near the end of the allocation period, staff will contact you and ask if you would like to renew the allocation for up to another 120 days (requires re-approval from a CML faculty member and the director
You can request project based allocations for up to 6TB for up to 120 days by [[HelpDesk | contacting staff]] 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. Near the end of the allocation period, staff will contact you and ask if you would like to renew the allocation for up to another 120 days (requires re-approval from a CML faculty member and the director CML). If you do not want to renew, you will need to relocate all desired data within 14 days of the end of the allocation period. Staff will then remove the allocation.
This data is backed up nightly.
This data is backed up nightly.
Latest revision as of 15:12, 13 September 2022
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. 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.
The Center has a base SLURM account
cml which has a modest number of guaranteed GPUs available to all cluster users at any given time (currently 16). 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 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.
$ sacctmgr show assoc account=tomg format=user,account,qos,grptres User Account QOS GrpTRES ---------- ---------- -------------------- ------------- tomg gres/gpu=48
CML currently has 4 QoS for the dpart partition (though
very_high is only available on a single faculty member's account), 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 Priority ---------- ----------- ------- ------------------------------ ------------- ---------- medium 3-00:00:00 1 cpu=8,gres/gpu=2,mem=64G 0 default 7-00:00:00 2 cpu=4,gres/gpu=1,mem=32G 0 high 1-12:00:00 2 cpu=16,gres/gpu=4,mem=128G 0 scavenger 3-00:00:00 gres/gpu=24 0 normal 0 cpu 1-00:00:00 1 0 very_high 1-12:00:00 8 cpu=32,gres/gpu=8,mem=256G gres/gpu=12 0
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
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
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 (20GB, cannot be increased) 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.
You can request project based allocations for up to 6TB for up to 120 days by contacting staff 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. Near the end of the allocation period, staff will contact you and ask if you would like to renew the allocation for up to another 120 days (requires re-approval from a CML faculty member and the director of CML). If you do not want to renew, you will need to relocate all desired data within 14 days of the end of the allocation period. Staff will then 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
You are allocated 400GB of scratch space via NFS from
/cmlscratch/$username. It is not backed up or protected in any way. This directory is automounted so you will need to
cd into the directory or request/specify a fully qualified file path to access this.
You may request a permanent increase of up to 800GB total space without any faculty approval by contacting staff. If you need space beyond 800GB, you will need faculty approval and/or a project directory.
This file system is available on all submission, data management, and computational nodes within the cluster.
Local Scratch Directories
Each computational node that you 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, you 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 have a tmpwatch job which will delete unaccessed data after 90 days, scheduled via maintenance jobs to run once a month at 1am. Different nodes will run the maintenance jobs on different days of the month to ensure the cluster is still highly available at all times. 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 at
/fs/cml-datasets. If there are 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|
 - This dataset has restricted access. Please contact staff if you are looking to use this dataset.