Administer Slurm clusters: Quick Reference — Administration (NVIDIA-Certified Professional: AI Operations)

Administering Slurm Clusters: Quick Reference

This quick reference guide covers essential facts and procedures for administering Slurm clusters within NVIDIA AI Operations environments. Slurm is a widely used open-source workload manager designed for high-performance computing (HPC) clusters, crucial for managing AI workloads efficiently.

Key Concepts

• Slurm: Simple Linux Utility for Resource Management; manages job scheduling and resource allocation.
• Node: A compute server in the cluster where jobs run.
• Partition: A set of nodes grouped for scheduling purposes.
• Job: A unit of work submitted to the cluster.
• Scheduler: Component responsible for allocating resources to jobs based on policies.

Basic Administration Commands

• sinfo: View cluster status and node availability.
• squeue: List jobs currently queued or running.
• sbatch <script>: Submit batch jobs.
• scancel <job_id>: Cancel a running or queued job.
• scontrol show node <node_name>: Display detailed node information.

Configuration Files

• /etc/slurm/slurm.conf: Main configuration file defining partitions, nodes, and scheduling policies.
• /etc/slurm/cgroup.conf: Controls resource limits and cgroup settings for jobs.

Common Administrative Tasks

• Adding/Removing Nodes: Update slurm.conf with node definitions and restart Slurm daemons.
• Partition Management: Define partitions to group nodes by workload type or priority.
• Monitoring Jobs: Use squeue and sacct to track job status and resource usage.
• Managing Job Priorities: Configure priority parameters in slurm.conf to optimize scheduling.
• Handling Node Failures: Mark nodes down with scontrol update NodeName=<node> State=DOWN to prevent job scheduling on faulty hardware.

Best Practices

• Regularly Monitor Cluster Health: Use sinfo and log files to detect issues early.
• Implement Resource Limits: Configure cgroups to prevent jobs from overusing resources.
• Automate Job Submission: Use scripts and job arrays for repetitive tasks.
• Backup Configuration Files: Maintain version control for slurm.conf and related files.

Troubleshooting Tips

• Slurm Daemon Not Running: Check slurmctld and slurmd service status and logs.
• Jobs Stuck in Pending: Verify resource availability and partition configurations.
• Node Not Responding: Ping node, check network, and review node logs.

Worked Example: Submitting a Simple Job

Task: Submit a job to run a Python script on the cluster.

Solution:

• Create a batch script job.sh:
#!/bin/bash #SBATCH --job-name=python_test #SBATCH --output=output.txt #SBATCH --ntasks=1 #SBATCH --time=00:10:00 python my_script.py
• Submit the job with sbatch job.sh.
• Check job status with squeue.

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