Best Practices and Troubleshooting
Last updated on 2026-08-27 | Edit this page
Estimated time: 15 minutes
Overview
Questions
- How do I avoid breaking my analysis when modules are updated?
- How do I clean up and avoid filling my disk quota?
- How should I test before submitting large jobs?
- How should I ask for help from the HPC team?
Objectives
- Manage disk usage efficiently
- Test scripts locally before submitting large jobs
- Know when and how to contact the HPC support team
- Follow a checklist for setting up new projects
Monitor and Manage Disk Usage
Prevent Quota Issues
Conda environments can consume substantial disk space. Regular maintenance prevents quota problems and keeps your cluster account healthy. On Sagehen HPC, /rhome and /bigdata share a 1 TB lab quota.
Check your usage
Note: Bare quota is unreliable on BeeGFS; use Pomona’s
quota_check.sh wrapper which handles BeeGFS quota
correctly.
Test Before Submitting Large Jobs
Getting Help
Common issues
-
Module not found:
- Check spelling:
module avail name(ormodule spider name) - Contact its-hpc@pomona.edu
- Check spelling:
-
Code worked locally but fails on cluster:
- Environment mismatch: verify modules in job script
- Data path issues: use absolute paths, not relative
- Resource limits: check memory and time requirements
-
Out of disk quota:
- Remove old conda environments:
conda env remove -n oldenv - Clean conda cache:
conda clean --all - Archive old data to external storage
- Remove old conda environments:
Contacting support
Email its-hpc@pomona.edu with: - Job ID (from
squeue or log files) - Full error message - What you’ve
already tried - Your account name
Checklist: Setting Up a New Project
Complete Project Setup Example
BASH
# Create project
$ mkdir genomics_analysis_2024
$ cd genomics_analysis_2024
$ git init
# Create conda environment
$ conda create -n genomics python=3.10
$ conda activate genomics
(genomics) $ conda install numpy pandas biopython samtools -c conda-forge
(genomics) $ pip install pysam biotools
(genomics) $ conda env export > environment.yml
(genomics) $ git add environment.yml
# Create job script
$ cat > job_script.sh << 'EOF'
#!/bin/bash
#SBATCH --job-name=genomics
#SBATCH --time=04:00:00
#SBATCH --cpus-per-task=8
#SBATCH --mem=32G
module purge
module load miniconda3/py313_26.3.2-2
conda activate genomics
python analyze_genomes.py
EOF
$ chmod +x job_script.sh
$ sbatch job_script.sh
$ git add analyze_genomes.py job_script.sh
$ git commit -m "Initial genomics analysis setup"
Challenge 1: Check and Manage Disk Usage
- Check your total disk quota:
quota_check.sh - See what’s using space:
du -sh --apparent-size ~/.conda/envs/* - List all your conda environments:
conda env list - If you have test environments, remove one:
conda env remove -n test_env - Clean up conda cache:
conda clean --all
Expected output from quota_check.sh shows your current usage and
limit. du -sh --apparent-size shows sizes like:
1.2G /rhome/<myusername>/.conda/envs/bigproject
500M /rhome/<myusername>/.conda/envs/test_env
After cleanup, total usage drops and you stay under quota. This regular maintenance prevents quota issues.
- Monitor disk usage regularly and clean up old conda environments
- Test scripts locally and with small jobs before submitting large ones
- Contact its-hpc@pomona.edu with job ID, error message, and what you’ve tried
- Follow the new project checklist for reproducible research setups
- Use
module purgeat the start of job scripts for a clean environment - Start with
module purge, then load specific module versions explicitly