Writing Batch Scripts

Last updated on 2026-08-24 | Edit this page

Overview

Questions

  • What is a batch script and how is it structured?
  • What are the essential SBATCH directives?
  • How do I load modules and run code in batch scripts?

Objectives

  • Write complete SLURM batch scripts
  • Use SBATCH directives to request resources
  • Load modules and configure environments in scripts
  • Understand SLURM environment variables available in jobs

Why Batch Jobs?

Batch jobs run unattended while you do other things. They are the backbone of research computing:

  • Run long jobs (hours, days, weeks)
  • Submit many jobs at once
  • Reproducible: exact commands saved in the script
  • Run overnight or over weekends
  • Get email notifications when done

Anatomy of a Batch Script

BASH 

#!/bin/bash -l
#SBATCH --job-name=my_job
#SBATCH --nodes=1
#SBATCH --cpus-per-task=4
#SBATCH --mem=16G
#SBATCH --time=00:30:00
#SBATCH --partition=amd
#SBATCH --output=slurm-%j.out
#SBATCH --error=slurm-%j.err

module load miniconda3

python my_analysis.py

The Shebang Line

BASH 

#!/bin/bash -l

Always use -l so your .bashrc is read and modules are available.

SBATCH Directives

Directive Meaning Example
--job-name Name shown in squeue --job-name=analysis_v1
--cpus-per-task CPU cores --cpus-per-task=8
--mem Total RAM --mem=32G
--time Max job time --time=04:00:00
--partition Partition --partition=amd
--output stdout file (%j = job ID) --output=job_%j.out
--error stderr file --error=job_%j.err
--gres GPUs --gres=gpu:2
--mail-user Email address --mail-user=me@pomona.edu
--mail-type When to email --mail-type=END,FAIL

SLURM Environment Variables

SLURM sets useful variables inside your job:

Variable Meaning
$SLURM_JOB_ID Job ID
$SLURM_JOB_NAME Job name
$SLURM_CPUS_PER_TASK Cores requested
$SLURM_ARRAY_TASK_ID Array task ID

Example Batch Scripts

Python Data Analysis

BASH 

#!/bin/bash -l
#SBATCH --job-name=data_analysis
#SBATCH --cpus-per-task=4
#SBATCH --mem=16G
#SBATCH --time=01:00:00
#SBATCH --partition=amd
#SBATCH --output=analysis_%j.out

module load miniconda3
python analysis.py

R Statistical Analysis (short test run)

BASH 

#!/bin/bash -l
#SBATCH --job-name=stats
#SBATCH --cpus-per-task=2
#SBATCH --mem=8G
#SBATCH --time=00:30:00
#SBATCH --partition=short
#SBATCH --output=r_analysis_%j.out

module load r/4.5.1
Rscript analysis.R

Quick R analyses with a short walltime are a good fit for the short partition. For longer R jobs, change --partition=short to --partition=amd.

GPU Deep Learning

BASH 

#!/bin/bash -l
#SBATCH --job-name=train_model
#SBATCH --cpus-per-task=8
#SBATCH --mem=64G
#SBATCH --time=12:00:00
#SBATCH --partition=gpu
#SBATCH --gres=gpu:a100:1
#SBATCH --output=train_%j.out

module load cuda miniconda3
source ~/ml_env/bin/activate
python train.py --epochs 100

Best Practices

Stop on First Error

BASH 

#!/bin/bash -l
set -e    # Exit on first error

python step1.py  # Must succeed
python step2.py  # Only runs if step1 succeeds

Include Logging

BASH 

echo "Job started at $(date)"
echo "Running on $(hostname)"
echo "Job ID: $SLURM_JOB_ID"

python my_analysis.py

echo "Job completed at $(date)"

Use Scratch for I/O

BASH 

cp /rhome/$USER/large_input.dat /scratch/$SLURM_JOB_USER/$SLURM_JOB_ID/
cd /scratch/$SLURM_JOB_USER/$SLURM_JOB_ID/
python process.py
cp output.csv /rhome/$USER/results/
Challenge

Challenge 1: Write a Batch Script

Create simple_job.sh that:

  1. Uses the short partition with 1 core and 1 GB RAM for 5 minutes
  2. Prints the hostname, job ID, and current date
  3. Sleeps for 10 seconds
  4. Prints “Job complete!”

BASH 

#!/bin/bash -l
#SBATCH --job-name=hello_world
#SBATCH --cpus-per-task=1
#SBATCH --mem=1G
#SBATCH --time=00:05:00
#SBATCH --partition=short
#SBATCH --output=hello_%j.out

echo "Hello from Sagehen!"
echo "Job ID: $SLURM_JOB_ID"
echo "Running on: $(hostname)"
sleep 10
echo "Job complete!"

Save this file and make it executable with chmod +x simple_job.sh.

The script includes all required SBATCH directives and uses SLURM environment variables. The %j in the output filename ensures each run creates a unique file. The sleep 10 simulates actual computation.

Key Points
  • Batch scripts are bash scripts with #SBATCH directives for resource requests
  • First line must be #!/bin/bash -l (the -l flag loads your environment)
  • Key directives: --job-name, --cpus-per-task, --mem, --time, --partition, --output
  • Always load modules inside the script, not just interactively
  • Use set -e to stop on first error
  • Include logging (date, hostname, job ID) for debugging
  • Use scratch storage for I/O-intensive intermediate files