Administer Slurm clusters: Worked Example — Administration (NVIDIA-Certified Professional: AI Operations)

Administering Slurm Clusters: Worked Example for NVIDIA-Certified Professional: AI Operations

Slurm is a widely used open-source workload manager designed for high-performance computing (HPC) clusters, including those optimized for AI workloads. Effective administration of Slurm clusters is critical for ensuring efficient resource allocation, job scheduling, and overall system stability in NVIDIA AI infrastructure environments.

Scenario Overview

You are the AI Operations engineer responsible for administering a Slurm cluster supporting multiple AI training jobs on NVIDIA GPUs. The cluster has 20 nodes, each equipped with 4 NVIDIA A100 GPUs. Your task is to configure the Slurm scheduler to optimize GPU resource allocation, monitor job queues, and troubleshoot a scenario where jobs are pending despite available GPUs.

Step 1: Verify Cluster and Node Configuration

Begin by checking the current status of nodes and GPUs with the sinfo and scontrol commands.

• Run sinfo -N -l to list nodes and their states.
• Use scontrol show nodes to verify GPU resources per node and node health.

Reasoning: Confirming node availability and GPU counts ensures the scheduler has accurate resource information.

Step 2: Configure GPU Scheduling Parameters

Slurm requires explicit configuration to manage GPUs. Edit the slurm.conf file to include GPU resources per node:

NodeName=node[01-20] Gres=gpu:4 State=UNKNOWN

Also, define the GRES (Generic Resource) configuration in gres.conf to specify GPU types and counts:

NodeName=node01 Name=gpu Type=A100 File=/dev/nvidia0 NodeName=node01 Name=gpu Type=A100 File=/dev/nvidia1 ... (repeat for all GPUs and nodes)

Reasoning: Proper GRES configuration enables Slurm to track and allocate GPUs accurately.

Step 3: Submit a Test Job Requesting GPUs

Submit a Slurm batch job requesting GPUs to validate scheduling:

sbatch --gres=gpu:2 --time=01:00:00 test_gpu_job.sh

Where test_gpu_job.sh is a script running an AI training task.

Reasoning: This tests whether Slurm correctly allocates GPU resources per job requests.

Step 4: Monitor Job Queue and GPU Utilization

Use squeue to monitor job status and nvidia-smi on nodes to check GPU usage.

• squeue -u $USER shows your jobs and states.
• ssh node01 nvidia-smi displays GPU utilization.

Reasoning: Monitoring ensures jobs are running and GPUs are utilized as expected.

Step 5: Troubleshoot Pending Jobs Despite Available GPUs

If jobs remain in the pending state (PD) while GPUs appear free, investigate reasons using:

squeue -j -o "%i %t %M %R"

Check the Reason column for scheduling blocks such as Resources or Priority.

Next, verify the GRES configuration consistency across nodes and Slurm daemons:

• Ensure gres.conf matches actual GPU devices.
• Restart Slurm daemons if configuration changes were made: systemctl restart slurmctld and systemctl restart slurmd.

Reasoning: Misconfiguration or stale daemon states often cause resource allocation issues.

Step 6: Adjust Scheduling Policies if Needed

Modify scheduling parameters in slurm.conf to improve fairness or priority, for example:

SchedulerType=sched/backfill PriorityType=priority/multifactor PreemptType=preempt/partition_prio

After changes, reload Slurm configuration and monitor the impact on job scheduling.

Summary

This worked example demonstrates a systematic approach to administering Slurm clusters in NVIDIA AI environments by verifying configuration, submitting GPU-allocated jobs, monitoring resource usage, and troubleshooting scheduling issues. Mastery of these steps is essential for AI Operations professionals preparing for the NVIDIA-Certified Professional: AI Operations exam.

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