for Researchers & Teams

The Compute Cluster

SESYNC provides a high-performance computing cluster for memory-intensive and time-intensive computing tasks. (FAQ: What is the SESYNC cluster?) You can connect to the cluster through our ssh gateway service running at or by submitting jobs through RStudio. The workflow for using a cluster is a little bit different from a typical run in R or python. In addition to your processing code, you must give the cluster a list of execution instructions and a description of the resources your analysis will require. Any output from your script will be written out to a file called slurm-[jobID].out and errors go to slurm-[jobID].err.

The first part of this quickstart guide explains in general how to connect to the server and submit jobs, via the ssh gateway or the RStudio server. The second part of the guide gives specific examples showing how to submit jobs in R, Python, and MATLAB.

The general process to submit your code (aka, job) to the cluster is as follows:

  1. Create a submission script that lists the resources you request and lists the commands necessary to run your code
  2. Submit this to the cluster
  3. Check your job’s status
  4. Look at your job’s output

Connecting to the server and submitting jobs

Connect via the ssh gateway

Login to SESYNC’s ssh gateway at (click here if you need to know how to do that).

Create a file called using your favorite editor (nano, pico, vi, etc.). For example: $ nano Type and save the following in the file:

#SBATCH -n 1
#SBATCH -t 0:60


This script will ask the scheduler to create a job that is up to 60 seconds long (-t 0:60), and it requests one CPU (-n 1).

Submit this script to the cluster at the command prompt: $ sbatch

Check your job’s status at the command prompt: $ squeue

Check your job’s output by using $ ls to find the .out (and maybe .err) file containing your job number. View the output with an editor or the less command. For example, if your job number were 1234, $ less slurm-1234.out

Connect via RStudio server

Connect to the RStudio server at For more information, see the RStudio quickstart. Write or load the R code you wish to submit to the cluster and save it in a file. For more information on writing code that makes optimal use of the cluster, please see the rslurm package documentation. Let’s assume for now your R script is called myRcode.R.

Create a new file in the RStudio editor (File -> New File -> Text File). Save it as in the same folder as myRcode.R. Type and save the following:

#SBATCH -n 1

Rscript --vanilla myRcode.R

This script tells the scheduler and linux that this is a shell script and should be run using the bash shell (#!/bin/bash), using one CPU (-n 1) and that it should launch your R script with default configuration settings (Rscript --vanilla myRcode.R).

Open the terminal in RStudio (Tools -> Terminal -> New Terminal).

Submit this script to the cluster at the command prompt: $ sbatch Before you do this, ensure that you are in the same directory in the terminal as where your two scripts (myRcode.R and are saved. You can do this using $ pwd and use $ cd followed by the appropriate path to navigate to the correct directory.

Check your job’s status at the command prompt: $ squeue

You can find your output in the files window of RStudio. Look for .out (and maybe .err) files with your job number and open them from there (they will be plain text). You can also check your job’s output in the terminal window, using $ ls to find the file(s) containing your job number. View the output with an editor or the less command. For example, if your job number were 1234, $ less slurm-1234.out

Submitting R jobs

The following is a simple example that shows you how to:

Once you are familiar with the process shown here, you may want to check our examples of the different ways to run code in parallel on the cluster. You may also want to check out the rslurm package for submitting R code to a slurm cluster.

1. Create a simple R script

Before you begin, download the surveys.csv data file from Data Carpentry. Place it in your current directory. The file contains observations on the weight of several species of small mamals across 24 survey plots.


Create a script to run some analyses on the data. The script below reads the data into R, calculates basic statistics on the observed weight of each species then creates a bar chart of the mean in a pdf. Only rows with complete data are used.

surveys <- read.csv('surveys.csv')
surveys_complete <- surveys[complete.cases(surveys), ]

surveys_complete$species <- factor(surveys_complete$species)
species_mean <- tapply(surveys_complete$wgt, surveys_complete$species, mean)
species_max <- tapply(surveys_complete$wgt, surveys_complete$species, max)
species_min <- tapply(surveys_complete$wgt, surveys_complete$species, min)
species_sd <- tapply(surveys_complete$wgt, surveys_complete$species, sd)
nlevels(surveys_complete$species) # or length(species_mean)
surveys_summary <- data.frame(species=levels(surveys_complete$species),

2. Create a job submission script

In addition to the R script you want to run, you need to create a seperate submission script that instructs the cluster how to run your R script.

Q. Why can’t you directly submit your R script?

A. The cluster is able to run many different types of code and doesn’t have an innate awareness of the specific language used in any particular analysis script that’s submitted. Therefore, you must tell the cluster how to run your code. The script we are about to create provides these instructions.

In the same folder you created your plot.R file, create a submission script called This submission script is a short shell script that instructs the scheduler which settings to use and lists the commands necessary to run your R script. The example below tells the scheduler that a single task will run and that it should process the plot.R script file using the Rscript command.


Rscript --vanilla plot.R

Here’s what the script is doing:

3. Run your job

In the RStudio “Console”, submit your job to the cluster by typing:


Note that the system will return a message with the job number for your submission. This is a unique number on the scheduler that can be used to track information about the status of your job. This is particularly useful for jobs that may take a long time to run.

> system("sbatch")
Submitted batch job 76

After you submit this job, it should finish in a few seconds. Click the refresh button in the RStudio “Files” window, and you should see a few new files.

4. Modify your scripts to run more than once

If you look at the script above, you’ll notice that a problem arises when you want to run this file multiple times. The way its currently written, it will overwrite the pdf output each time it is run. To prevent this, we can append the unique SLURM job number to the name of the file.

The scheduler provides environment variables you can use for this purpose. We will modify the pdf command (third to last line) of the plot.R script to read as follows:

pdf(paste0("mean_per_species_", Sys.getenv("SLURM_JOB_ID"),".pdf"))

Once you’re finished you can re-submit your script using sbatch as you did before. When the script finished, you’ll notice that the PDF file is now called “mean_per_species_XX.pdf”, where XX is the job number.

Submitting Python jobs

Save your Python script in the same location as your script. If the Python script is named, to run Python code, the submission script can be as simple as:



Note: If your script uses packages from a virtual environment, make sure to first activate it before calling sbatch.

Setting up a virtual environment

If your script requires additional Python packages besides the standard library and the few packages (such as numpy) already on the SESYNC server, you will need to install them in a virtual environment, which is a user-specific Python library. A virtual environment will also allow you to run your script with a specific version of Python. Follow the directions on our FAQ on how to create a virtual environment for a Slurm job.

Running multiple copies of a Python script in parallel

In general, you may want to run multiple copies of a script in parallel, using different parameter sets. The following submission script accepts two command line parameters and passes them to Python.


python $1 $1

Your Python script can access these parameters via the sys.argv[] list.

import sys
a = sys.argv[1]
b = sys.argv[2]

In this case, the command sbatch 5 3 sets a = 5 and b = 3 in the Python script. If you submit this script to the cluster multiple times, it is important that each version saves its output to a separate file. You can achieve this by getting the SLURM_JOB_ID environment variable within the Python script and using it to index your output file:

import os
job_id = os.environ.get('SLURM_JOB_ID')
outfile_name = "results" + job_id + ".txt"

If your Python script requires the use of a virtual environment, your submit script should look like this:


source venv/bin/activate
python $1 $1

Tip: Editing your remote Python files

The RStudio Server interface (accessible via your web browswer at can recognize Python syntax and thus serve as a code editor for your files hosted on the SESYNC server. Note that it may not be possible to run the scripts in RStudio Server, since you cannot access your virtual environment from that interface.

Submitting MATLAB jobs

Save your MATLAB script, we’ll assume it is named sampleMATLAB.m in the same place as your file.

Create your file with the following:


/usr/local/MATLAB/R2018b/bin/matlab -nodisplay < sampleMATLAB.m

or update your ~/.profile to include: export PATH=$PATH:/usr/local/MATLAB/R2018b/bin/matlab

and the file only needs:


matlab -nodisplay < sampleMATLAB.m

From the ssh gateway, submit the script with the command prompt: $ sbatch

For more information

Here are some pages with helpful advice on using the SESYNC cluster.


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