| name | slurm-job-debug-template |
| description | Use this skill to render, submit, and inspect a minimal Slurm smoke job before submitting heavier workloads on a real cluster. |
Purpose
Generate a conservative sbatch template, submit it to Slurm, and capture the accounting record needed to verify the cluster path end to end.
When to use
- You need a tiny Slurm smoke job.
- You want a reusable starting point for queue, environment, and log validation.
When not to use
- You need multi-node or GPU tuning guidance beyond a first smoke job.
Inputs
- Command string
- Optional job name, partition, runtime, memory, and output path
Outputs
- Rendered
sbatch script
- JSON submission and
sacct summary
Requirements
- Python 3.13+
sbatch, squeue, and sacct- A real Slurm cluster
Procedure
- Run
python3 skills/hpc/slurm-job-debug-template/scripts/render_sbatch.py --command "echo hello" --job-name smoke.
- Inspect the generated script or save it with
--out.
- Submit a verified smoke job with
python3 skills/hpc/slurm-job-debug-template/scripts/submit_smoke_job.py --partition cpu --job-name slurm-smoke --sleep 2 --out slurm/reports/slurm-smoke.json.
- Inspect the returned
job_id, the log paths in slurm/logs/, and the accounting block from sacct.
Validation
- Renderer exits successfully.
- Output contains
#SBATCH directives and the command body.
- Submitted smoke job reaches
State=COMPLETED.
ExitCode is 0:0.
Failure modes and fixes
- Missing partition/account information: add them before submission.
- Output paths wrong: switch to cluster-appropriate scratch or log paths.
sacct lags briefly after completion: retry once accounting catches up.
Safety and limits
- Keep resource requests small for initial smoke jobs.
- Use a CPU partition and a short walltime for smoke checks.
Examples
python3 .../render_sbatch.py --command "python analysis.py" --partition short --time 00:10:00 --mem 2Gpython3 .../submit_smoke_job.py --partition cpu --job-name slurm-smoke --sleep 1
Provenance
Related skills
snakemake-toy-workflow-starter
