Robin-hpc
From Robin
(→SLURM) |
(→SLURM) |
||
Line 40: | Line 40: | ||
== SLURM == | == SLURM == | ||
- | + | Ropin-hpc uses slurm for job scheduling. | |
+ | |||
+ | To start a job on robin-hpc you will need to use a job script specifying the resources you want to use. | ||
+ | The job is started with the following command: | ||
+ | <pre class="brush: bash"> | ||
+ | sbatch nameofthejobscript.sh | ||
+ | </pre> | ||
+ | |||
+ | (Note: Please do not run your program on the login node, test your implementation before copying the files to robin-hpc.) | ||
+ | |||
+ | You can easily copy the files needed to run your program to your home area on robin-hpc with scp. | ||
+ | |||
+ | Below you will find a template for the job script, followed by some convenient commands. | ||
'''SLURM job script template''' | '''SLURM job script template''' | ||
Line 46: | Line 58: | ||
#!/bin/bash | #!/bin/bash | ||
- | # This is a template for a slurm job script. | + | # This is a template for a slurm job script. |
- | # To start a job on robin-hpc please use the command "sbatch nameofthisscript.sh". | + | # To start a job on robin-hpc please use the command "sbatch nameofthisscript.sh". |
- | + | ||
- | + | ||
# Job name | # Job name | ||
Line 79: | Line 89: | ||
srun python3 myprogram.py | srun python3 myprogram.py | ||
+ | </pre> | ||
+ | |||
+ | '''Convenient commands''' | ||
+ | |||
+ | View the status of all jobs: | ||
+ | <pre class="brush: bash"> | ||
+ | squeue | ||
+ | </pre> | ||
+ | |||
+ | View the status of your own jobs: | ||
+ | <pre class="brush: bash"> | ||
+ | squeue -u yourusername | ||
</pre> | </pre> | ||
Revision as of 13:23, 26 February 2021
Contents |
Hardware and network configuration
The robin-hpc
is a shared resource for robins researchers and Master students. The strength of the machine is the amount of CPU cores and RAM. Unforunatly, there's no GPU available in this service.
Specs
CPU | RAM | OS | |
---|---|---|---|
login node | 2 cores/4 vCPU | 16GB | CentOS |
worrker node | 120 cores/240 vCPU | 460GB | CentOS |
Storage
The storage on the nodes consists of one 1TB disk where 100GB is reserved for software and 900GB is reserved for the users of the node. However, each user has a soft limit of 20 GB and a hard limit of 100 GB with a grace period of 14 days.
It's important to note that there is no backup of the disk, so do not use the robin-hpc
as a cloud storage service. We suggest using rsync
/scp
to your home area on login.ifi.uio.no
.
E.g.
rsync --progress <file>.tar.gz <username>@login.ifi.uio.no:~/<path>/<to>/<wherever>
It is also a possibility to mount your UiO home directory to the robin-hpc
.
Access
Apply for access using this link: https://nettskjema.no/a/robin-hpc
SLURM
Ropin-hpc uses slurm for job scheduling.
To start a job on robin-hpc you will need to use a job script specifying the resources you want to use. The job is started with the following command:
sbatch nameofthejobscript.sh
(Note: Please do not run your program on the login node, test your implementation before copying the files to robin-hpc.)
You can easily copy the files needed to run your program to your home area on robin-hpc with scp.
Below you will find a template for the job script, followed by some convenient commands.
SLURM job script template
#!/bin/bash # This is a template for a slurm job script. # To start a job on robin-hpc please use the command "sbatch nameofthisscript.sh". # Job name #SBATCH --job-name=nameofmyjob # Wall clock time limit (hh:mm:ss). The program will be killed when the time limit is reached. #SBATCH --time=01:00:00 # Number of tasks to start in parallel from this script. # (i.e. myprogram.py below will be started ntasks times) #SBATCH --ntasks=1 # CPUs allocated per task #SBATCH --cpus-per-task=16 # Memory allocated per cpu #SBATCH --mem-per-cpu=1G # Set exit on errors set -o errexit set -o nounset # Load your environment source myenv/bin/activate # Run your program with "srun yourcommand" # stdout and stderr will be written to a file "slurm-jobid.out". # (warning: all tasks will write to the same slurm.out file) srun python3 myprogram.py
Convenient commands
View the status of all jobs:
squeue
View the status of your own jobs:
squeue -u yourusername
Software
Matlab R2019b
Todo: sebastto
Setting up the SLURM job script
#SBATCH --job-name=matlab_job #SBATCH --ntasks=1 #SBATCH --cpus-per-task 16 srun matlab -batch "addpath(genpath('/path/to/your/matlab/folder'));run('myScript.m')"
Running Matlab in batch mode is the most safe option for running MATLAB on a HPC. (From Mathworks documentation)[1]:
-batch statement
Starts without the desktop
Does not display the splash screen
Executes
statement
Disables changes to preferences
Disables toolbox caching
Logs text to
stdout
andstderr
Does not display modal dialog boxes
Exits automatically with exit code 0 if
script
executes successfully. Otherwise, MATLAB terminates with a non-zero exit code.
The addpath(genpath('/path/to/your/matlab/folder'))
part adds all files in the specified directory to the MATLAB search path. Afterwards we run the main script of your program with run('myScript.m')
.
Utilizing parallel computing in your MATLAB Script
When the SLURM worker node is setting up your job, a number of environment variables is set.
We can use the environment variable SLURM_CPUS_ON_NODE
to get the number of CPU cores available in our MATLAB script. In fact, we can use that variable to dynamically select the number of workers in the MATLAB parallel pool, so that your script works both on your own computer and on the HPC.
SLURM_CPUS_STR = getenv('SLURM_CPUS_ON_NODE'); % Delete parallel pool from earlier runs delete(gcp('nocreate')); if isempty(SLURM_CPUS_STR) % Run on personal computer (with however many cores your CPU has) parpool(6); else % Run on SLURM-scheduled HPC SLURM_CPUS_NUM = str2num(SLURM_CPUS_STR); parpool(SLURM_CPUS_NUM); end
Anaconda
Todo: emmaste
Podman
alias docker=podman