Deploy training workloads with Slurm and Run:ai: Worked Example — Workload Management (NVIDIA-Certified Professional: AI Operations)

Deploy Training Workloads with Slurm and Run:ai: Worked Example

In the NVIDIA-Certified Professional: AI Operations exam, workload management is a critical domain, with 23% of the content dedicated to deploying and managing AI workloads. This worked example focuses specifically on deploying training workloads using Slurm and Run:ai, two essential tools for efficient resource scheduling and management in AI infrastructure.

Scenario Overview

Imagine an AI research team at a technology company needs to run multiple deep learning training jobs on a shared GPU cluster. The cluster uses Slurm as the workload manager, integrated with Run:ai to optimize GPU resource allocation dynamically across teams. The goal is to deploy a training workload that maximizes GPU utilization while respecting team quotas and priorities.

Step 1: Prepare the Training Job Script

The first step is to create a Slurm batch script that defines the training job's resource requirements and execution commands. For example:

!/bin/bash

SBATCH --job-name=dl_training

SBATCH --gres=gpu:4

SBATCH --time=04:00:00

SBATCH --partition=runai

module load cuda/11.4 python train_model.py --epochs 50 --batch-size 64

Explanation:

• --gres=gpu:4 requests 4 GPUs.
• --partition=runai specifies the Run:ai-managed partition.
• The script loads necessary CUDA modules and runs the training Python script.

Step 2: Submit the Job via Run:ai CLI

Run:ai provides a command-line interface to submit and manage jobs with enhanced scheduling features. The job submission command might look like this:

runai submit dl_training_job --image=myregistry/dl_training:latest --gpu 4 --command "python train_model.py --epochs 50 --batch-size 64" --priority 10 --queue default

Explanation:

• --image specifies the container image from NGC or a private registry.
• --gpu 4 requests 4 GPUs.
• --priority 10 assigns a priority to the job for scheduling.
• --queue default places the job in the default queue managed by Run:ai.

Step 3: Resource Allocation and Scheduling

Run:ai works alongside Slurm to dynamically allocate GPUs based on current cluster utilization and team quotas. It monitors the cluster state and adjusts resource assignments to maximize utilization and fairness.

For example, if other teams have idle GPUs, Run:ai can temporarily allocate additional GPUs to this training job, speeding up completion. Conversely, if higher priority jobs arrive, Run:ai can preempt resources accordingly.

Step 4: Monitoring and Troubleshooting

Once the job is running, use the following tools for monitoring:

• squeue to check Slurm job status.
• runai list to view Run:ai job states and GPU usage.
• System management tools like nvidia-smi to monitor GPU health and utilization.

If the job fails or is delayed, check Slurm logs (slurmctld.log) and Run:ai logs for error messages. Common issues include insufficient GPU availability or container image pull errors.

Step 5: Job Completion and Resource Release

After training completes, Slurm and Run:ai automatically release allocated GPUs back to the cluster pool. Run:ai updates usage statistics for quota enforcement and billing if applicable.

Worked Example Summary

Problem: Deploy a 4-GPU training workload on a shared cluster using Slurm and Run:ai, ensuring priority scheduling and efficient resource use.

Solution Steps:

1. Create a Slurm batch script requesting 4 GPUs and specifying the Run:ai partition.
2. Submit the job with Run:ai CLI, specifying container image, GPU count, priority, and command.
3. Allow Run:ai and Slurm to schedule and allocate GPUs dynamically based on cluster state.
4. Monitor job progress using Slurm and Run:ai commands, plus GPU health tools.
5. Troubleshoot using logs if issues arise.
6. Upon completion, resources are released and usage recorded.

This approach ensures efficient workload deployment, leveraging Slurm's robust scheduling and Run:ai's AI-centric resource optimization, a key skill for the NVIDIA-Certified Professional: AI Operations certification.

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