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 or error messages from your script will be written out to a file called slurm-[jobID].out.

This quickstart guide explains how to connect to the ssh gateway server and submit jobs, and gives specific examples showing how to submit jobs in R from the RStudio server and Python from the ssh gateway.

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

Submitting jobs from the ssh gateway

Login to SESYNC’s ssh gateway at (Read the FAQ on accessing Linux resources 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). The hostname command will display the name of the node the job is running on.

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 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

Submitting R jobs from the Rstudio server

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

Connect to the RStudio server at For more information, see the RStudio quickstart.

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 mammals 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 where 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.

#SBATCH -n 1

Rscript --vanilla plot.R

Here’s what the submission script is doing:

3. Run your job

Open the terminal in RStudio (switch to the Terminal tab or go to Tools -> Terminal -> New Terminal to create a new tab).

Submit your 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 (plot.R and are saved. You can do this using $ pwd to check the current directory and $ cd followed by the appropriate path to navigate to the correct directory.

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

Check your job’s status at the command prompt: $ squeue will show a list of all the currently running jobs on the SESYNC server, with details on their processor usage and run time.

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

You can open the slurm-XX.out output log file in RStudio to look at any progress or error messages your job created while it was running.

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 it’s 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 finishes, 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. See below for an example.

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, it’s a good idea 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.


python3 $1 $1

Your Python script can access these parameters via the sys.argv[] list. Note that sys.argv[0] is always the name of the Python script, in this case, so use subscripts starting with 1 for the arguments you passed to the script.

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
python3 $1 $1

PROTIP: Editing your remote Python files

The RStudio Server interface (accessible via your web browser 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.

For more information

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


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