submit-slurm-job

Submit Slurm GPU Job

Overview

Generate and submit sbatch scripts for GPU compute jobs on the cluster. Handles all cluster-specific details: partition, GPU types, node selection, python path.

Configuration

Before using this skill, set the following in your project's CLAUDE.md or environment:

Variable	Example	Description
PYTHON_PATH	/path/to/miniconda3/envs/myenv/bin/python3	Full path to Python interpreter
PROJECT_DIR	/home/user_xxx/private/homefile	Must be under ~/private/homefile; Slurm submission is only allowed from this path
PARTITION	home	Slurm partition name (the only compute partition)

Cluster rule: scripts and submissions must live under ~/private/homefile. Data should live under ~/private/datafile. Run sbatch/srun only after cd ~/private/homefile/....

Workflow

Step 1: Gather Parameters

Ask the user (with AskUserQuestion) what they want to run. Key parameters:

Parameter	Default	Description
job_name	(required)	Short job name for SBATCH
gpu_type	(required)	GPU model, must be explicit (e.g., A100_40G, V100, A800)
n_gpu	1	Number of GPUs
time	24:00:00	Wall time limit
mem	32G	Memory
cpus	4	CPUs per task
script	(required)	Python script path (must live under ~/private/homefile)
args	(required)	Script arguments
output_dir	{PROJECT_DIR}	Directory for log files (keep under ~/private/homefile)

Step 2: Select GPU and Node

• Always specify GPU model in --gres: use gpu:MODEL:N (e.g., gpu:A100_40G:1, gpu:A800:2). gpu:1 will be rejected.
• Use slurm_gpustat (cluster-provided wheel) or scontrol show nodes -o to see available GPU models.
• Fragmentation guideline: for A800, there is one 8-GPU node and another 2-GPU node. Prefer ≤2-GPU jobs on the 2-GPU node; larger jobs on the 8-GPU node.
• If the user explicitly requests a node, add --nodelist=node.

Step 3: Write and Submit sbatch Script

Important: ensure the script is written under ~/private/homefile/... and run sbatch from that directory (cluster enforcement).

Generate the sbatch script following this template:

#!/bin/bash
#SBATCH --partition={PARTITION}  # home
#SBATCH --cpus-per-task={cpus}
#SBATCH --mem={mem}
#SBATCH --gres=gpu:{gpu_type}:{n_gpu}
#SBATCH --nodes=1
#SBATCH --time={time}
#SBATCH --job-name={job_name}
#SBATCH -o {output_dir}/{job_name}_%j.out

echo The current job ID is $SLURM_JOB_ID
echo Running on $SLURM_JOB_NODELIST
echo CUDA devices: $CUDA_VISIBLE_DEVICES
echo ==== Job started at `date` ====
nvidia-smi
echo

{PYTHON_PATH} \
    {script} \
    {args}

echo
echo ==== Job finished at `date` ====

Key rules:

• Partition: use home (the only compute partition).
• Working directory: script and submission must be under ~/private/homefile/....
• Python path: use full path {PYTHON_PATH} (do NOT conda activate).
• Output: use -o for combined stdout+stderr.
• GPU model: must be explicit in --gres.
• No --nodelist unless the user explicitly requests a specific node.

Write the script to {output_dir}/{job_name}.sh (under ~/private/homefile), then submit with sbatch from the same directory.

Step 4: Report

After submission, report:

• Job ID(s) from sbatch output
• Log file location: {output_dir}/{job_name}_{JOBID}.out
• How to monitor: squeue -u $USER, tail -f {log_file}
• How to cancel: scancel {JOBID}
• If jobs get auto-scancel, verify you submitted from the correct project (~/private/homefile matching the web UI project) and that requested resources are within quota.

Multiple Jobs

If the user wants to submit multiple jobs (e.g., different datasets on different GPUs):

1. Create separate .sh scripts for each job
2. Submit all with a loop: for f in script1.sh script2.sh ...; do sbatch $f; done
3. Report all job IDs

Common Patterns

Sequential tasks on one GPU

Put multiple python commands in a single script, separated by echo markers.

Parallel tasks across GPUs

Create separate scripts, each requesting one GPU. Submit all scripts independently.

Using --nodelist for specific nodes

Only add #SBATCH --nodelist=n004 when the user explicitly wants a specific node. Otherwise let Slurm schedule based on GPU type availability.