Slurm

Overview

DataCrunch On-demand clusters have Slurm job-scheduling system preinstalled. You can verify that Slurm is working by running a simple job that uses 16 GPUs. This will execute on at least two physical nodes, since each server has 8 GPUs:

srun --gpus=16 nvidia-smi

Example Slurm job

Pre-requisites

The following example utilizes Spack package system. You can also check out our simple Spack tutorial and the official Spack documentation to learn more.

Spack is pre-installed into /home/spack.

To get spack commands available in your terminal run:

. /home/spack/spack/share/spack/setup-env.sh

You can see how we build Spack by looking inside this script: /usr/local/bin/spack.setup.sh

Simple Slurm job

Below is an example of simple Slurm job that runs the all_reduce_perf from nccl-tests with 16 GPUs on two nodes.

The example job description is created by the Spack installation script and is placed here: /home/ubuntu/all_reduce_example_slurm.job:

#!/bin/bash
#SBATCH --job-name=all_reduce_perf
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=8
#SBATCH --gres=gpu:8
#SBATCH --cpus-per-task=22
#SBATCH --time=00:05:00

. /home/spack/spack/share/spack/setup-env.sh

spack load [email protected]

srun --mpi=pmix all_reduce_perf -b 1M -e 1G -f 2 -g 1

You can execute the above job by running:

$ sbatch /home/ubuntu/all_reduce_example_slurm.job

Briefly, the above script does the following:

• Lines starting with #SBATCH define the requirements for the job

• Next, Spack is initialized, making spackcommand available

• spack load [email protected] makes all_reduce_perf available in the $PATH

• srun executes the actual command.

To read what the job printed to stdout, look in the file slurm-jobid.out located in the same directory where you called the job from. You can run squeue -a to list all Slurm jobs and their status.

Troubleshooting

Get the general state of worker nodes with the command sinfo

In an unlikely case that the Slurm does not see the worker nodes after the reboot, you can make them available again with:

scontrol update NodeName=cluster-name-1 State=RESUME

If a node becomes unhealthy (for example, a full / partition), it will enter the drained state. Check the reason with:

scontrol show node cluster-name-1

To troubleshoot Slurm distributed workloads it's crucial to capture logs from both the head node and the worker nodes.

Logging

Use the following #SBATCH directives at the top of your Slurm script to capture logs produced by commands executed in the main script body (typically run on the head node):

#SBATCH --output=/path/to/logs/headnode/jobname_%j.out
#SBATCH --error=/path/to/logs/headnode/jobname_%j.err

• --output captures the STDOUT from the head node script body

• --error captures the STDERR from the head node script body

• %j is replaced with the Slurm job ID, keeping logs organized

To capture logs from each worker node, use the --output and --error flags inside the srun command:

srun \
  --output=/path/to/logs/workernodes/jobname_%j_node%N.out \
  --error=/path/to/logs/workernodes/jobname_%j_node%N.err \
  your_distributed_command

• %j - Slurm job id

• %N - node name (e.g., node001), ensuring per-node log separation.

These logs will capture:

• Logs from each task/rank running under srun

• Application-specific output (e.g. print statements, warnings, stack traces)

• Environment or setup errors during distributed execution

It's recommended to differentiate head node logs from worker logs using directory structure or filenames (headnode/ vs workernodes/), so you don't run into stale files and race conditions while writing to the logs.