techstaff:slurm
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techstaff:slurm [2018/05/04 12:40] – kauffman | techstaff:slurm [2020/03/25 14:17] – [Announcements] kauffman | ||
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- | ===== Notice | + | ====== Announcements ====== |
- | **2017-08-31**: Configuration change to allow allocation on CPUs and RAM. Please read the 'Default Quota' | + | * **2020-03-25**: Partition |
====== Peanut Job Submission Cluster ====== | ====== Peanut Job Submission Cluster ====== | ||
- | We are currently **alpha** testing and gauging user interest in a cluster of machines that allows for the submission of long running compute jobs. Think of these machines as a dumping ground for discrete computing tasks that might be rude or disruptive to execute on the main (shared) shell servers (i.e., linux1, linux2, linux3). | + | Think of these machines as a dumping ground for discrete computing tasks that might be rude or disruptive to execute on the main (shared) shell servers (i.e., linux1, linux2, linux3). |
For job submission we will be using a piece of software called [[http:// | For job submission we will be using a piece of software called [[http:// | ||
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==== Hardware ==== | ==== Hardware ==== | ||
Our cluster contains nodes with the following specs: | Our cluster contains nodes with the following specs: | ||
+ | |||
+ | '' | ||
* 16 Cores (2x 8core 3.1GHz Processors), | * 16 Cores (2x 8core 3.1GHz Processors), | ||
* 64gb RAM | * 64gb RAM | ||
* 2x 500GB SATA 7200RPM in RAID1 | * 2x 500GB SATA 7200RPM in RAID1 | ||
+ | '' | ||
+ | * 24 Cores (2x 24core Intel Xeon Silver 4116 CPU @ 2.10GHz), 48 threads | ||
+ | * 128gb RAM | ||
+ | * OS: 2x 240GB Intel SSD in RAID1 | ||
+ | * /local: 2x 960GB Intel SSD RAID0 | ||
+ | |||
+ | '' | ||
+ | * 6 Cores (Intel(R) Core(TM) i7-5930K CPU @ 3.50GHz), 12 threads | ||
+ | * 32gb RAM | ||
+ | * OS: 1x 512gb SSD | ||
+ | |||
+ | '' | ||
+ | * 16 Cores (Intel(R) Xeon(R) CPU E5-2630 v3 @ 2.40GHz), 32 threads | ||
+ | * 128gb RAM | ||
+ | * OS: 2x Samsung SSD 850 PRO 128GB | ||
+ | * /local: ZFS mirror (2x Samsung SSD 850 PRO 1TB) | ||
+ | * 2x Quadro P4000 | ||
+ | |||
+ | '' | ||
+ | * 8 Core (Intel(R) Xeon(R) Silver 4110 CPU @ 2.10GHz), 16 threads | ||
+ | * 64gb RAM | ||
+ | * OS: 1x 1TB 7200k spinning disk. | ||
+ | * 4x GeForce GTX 1080 Ti | ||
==== Storage ==== | ==== Storage ==== | ||
There is slow scratch space mounted to '' | There is slow scratch space mounted to '' | ||
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| **debug** | The partition your job will be submitted to if none is specified. The purpose of this partition is to make sure your code is running as it should before submitting a long running job to the general queue. | | | **debug** | The partition your job will be submitted to if none is specified. The purpose of this partition is to make sure your code is running as it should before submitting a long running job to the general queue. | | ||
| **general** | All jobs that have been thoroughly tested can be submitted here. This partition will have access to more nodes and will process most of the jobs. If you need to use the '' | | **general** | All jobs that have been thoroughly tested can be submitted here. This partition will have access to more nodes and will process most of the jobs. If you need to use the '' | ||
- | | **gpu** | Contains servers with graphics cards. As of May 2016 there is only one node containing a Tesla M2090. You will be forced to use this server exclusively for now. Please keep your time in interactive mode to a minimum.| | + | | **fast** | 2019-12-02: 48 threads, 128GB RAM | |
+ | | **quadro** | 2019-12-02: 2x Quadro P4000. *| | ||
+ | | **pascal** | 2018-05-04: 1x Nvidia GTX1080.| | ||
+ | | **titan** | 2018-05-04: 4x Nvidia GTX1080Ti. *| | ||
+ | * This partition is shared and you MUST use the '' | ||
====== Job Submission ====== | ====== Job Submission ====== | ||
Jobs submitted to the cluster are run from the command line. Almost anything that you can run via the command line on any of our machines in our labs can be run on our job submission server agents. | Jobs submitted to the cluster are run from the command line. Almost anything that you can run via the command line on any of our machines in our labs can be run on our job submission server agents. | ||
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| ^ SLURM ^ Example ^ | | ^ SLURM ^ Example ^ | ||
^ Submit a batch serial job | sbatch | sbatch runscript.sh | | ^ Submit a batch serial job | sbatch | sbatch runscript.sh | | ||
- | ^ Run a script | + | ^ Run a script |
^ Kill a job | scancel | scancel 4585 | | ^ Kill a job | scancel | scancel 4585 | | ||
^ View status of queues | squeue | squeue -u cnetid | | ^ View status of queues | squeue | squeue -u cnetid | | ||
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< | < | ||
user@host: | user@host: | ||
- | research2 | + | slurm2 |
- | research2 | + | slurm2 |
</ | </ | ||
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user@host: | user@host: | ||
PARTITION AVAIL TIMELIMIT | PARTITION AVAIL TIMELIMIT | ||
- | debug* | + | debug* |
- | general | + | fast up 1-00: |
- | pascal | + | general |
- | tesla | + | pascal |
+ | quadro | ||
+ | titan up 3-00: | ||
</ | </ | ||
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Example when using tensorflow: | Example when using tensorflow: | ||
- | Give the file ' | + | Given the file ''%%f%%'': |
< | < | ||
# | # | ||
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< | < | ||
$ sinfo -O partition, | $ sinfo -O partition, | ||
- | PARTITION | + | PARTITION |
- | debug* | + | debug* |
- | general | + | fast slurm[9-14] |
- | pascal | + | general |
- | titan | + | pascal |
+ | quadro | ||
+ | titan | ||
</ | </ | ||
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+ | ==== Checking how many Generic RESources are being consumed ==== | ||
- | ===== Paths ===== | + | Simple use the '' |
- | You will need to add the following to your $PATH and $LD_LIBRARY_PATH. | + | < |
+ | $ squeue -O username, | ||
+ | USER NODELIST | ||
+ | someusername | ||
+ | otherusername | ||
+ | ... | ||
+ | </ | ||
+ | |||
+ | |||
+ | ===== Environment Variables | ||
+ | |||
+ | ==== CUDA_HOME, LD_LIBRARY_PATH ==== | ||
+ | |||
+ | Please make sure you specify $CUDA_HOME and if you want to take advantage of CUDNN libraries you will need to append / | ||
+ | |||
+ | cuda_version=9.2 | ||
+ | export CUDA_HOME=/ | ||
+ | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH: | ||
+ | |||
+ | Currently we support the same versions of CUDA that the latest version of CUDNN supports. This is not written in stone and we can accommodate most other versions if required; just let techstaff know what your needs are. | ||
+ | |||
+ | ==== PATH ==== | ||
+ | You may also need to add the following to your '' | ||
export PATH=$PATH:/ | export PATH=$PATH:/ | ||
- | export LD_LIBRARY_PATH=$LD_LIBRARY_PATH=/usr/local/ | + | |
+ | ==== CUDA_VISIBLE_DEVICES ==== | ||
+ | Do not set this variable. It will be set for you by SLURM. | ||
+ | |||
+ | The variable name is actually misleading; since it does NOT mean the amount of devices, but rather the physical device number assigned by the kernel (e.g. /dev/nvidia2). | ||
+ | |||
+ | For example: If you requested multiple gpu's from SLURM (--gres=gpu: | ||
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</ | </ | ||
STDERR should be blank. | STDERR should be blank. | ||
+ | ====== Feedback ====== | ||
+ | If you feel this documentation is lacking in some way please let techstaff know. Email [[techstaff@cs.uchicago.edu]], | ||
+ | |||
====== More ====== | ====== More ====== | ||
- | If you feel this documentation is lacking | + | Sometimes other universities have documentation |
+ | |||
+ | - [[ https:// | ||
+ | |
/var/lib/dokuwiki/data/pages/techstaff/slurm.txt · Last modified: 2021/01/06 16:13 by kauffman