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

Deploy Training Workloads with Slurm and Run:ai – Quick Reference

This quick reference guide covers the essential facts and best practices for deploying training workloads using Slurm and Run:ai within NVIDIA AI infrastructure, a key skill for the NVIDIA-Certified Professional: AI Operations exam.

Key Concepts

• Slurm: An open-source, scalable cluster management and job scheduling system widely used for managing compute resources in HPC and AI training environments.
• Run:ai: A Kubernetes-native AI orchestration platform that optimizes GPU resource allocation and workload scheduling for AI training and inference.

Deploying Training Workloads with Slurm

• Job Submission: Use sbatch or srun commands to submit batch or interactive training jobs.
• Resource Specification: Define required GPUs, CPUs, memory, and time limits in the job script using directives like #SBATCH --gres=gpu:4 and #SBATCH --time=02:00:00.
• Partitioning: Submit jobs to appropriate partitions (queues) that group nodes based on hardware or usage policies.
• Job Dependencies: Use dependencies to control job execution order with --dependency flags.
• Monitoring: Track job status with squeue and view detailed info with scontrol show job <job_id>.

Deploying Training Workloads with Run:ai

• Namespace Management: Organize workloads by namespaces to isolate teams or projects.
• Resource Pools: Define GPU resource pools to allocate GPUs dynamically across workloads.
• Job Submission: Submit training jobs via Run:ai CLI or UI, specifying resource requests and priorities.
• Elastic Scheduling: Run:ai supports elastic training jobs that can scale GPU usage up or down during runtime.
• Quota and Fairshare: Enforce resource quotas and fairshare policies to balance resource distribution among teams.

Integration and Best Practices

• Hybrid Usage: Slurm can be integrated with Run:ai to leverage Kubernetes orchestration alongside traditional HPC scheduling.
• Containerization: Use NVIDIA GPU Cloud (NGC) containers for consistent, optimized AI training environments.
• Resource Optimization: Monitor GPU utilization and adjust job resource requests to maximize cluster efficiency.
• Troubleshooting: Use system management tools like nvidia-smi, Slurm logs, and Run:ai dashboards to diagnose workload issues.

Worked Example: Submitting a Slurm Training Job

Job script snippet:

SBATCH --job-name=ai_training

SBATCH --gres=gpu:2

SBATCH --time=01:30:00

SBATCH --partition=gpu

srun python train_model.py --epochs 50

Explanation: This script requests 2 GPUs for 1.5 hours on the GPU partition and runs the training script train_model.py.

Worked Example: Submitting a Run:ai Training Job

Command:

runai submit my-training-job --image nvcr.io/nvidia/pytorch:xx.xx-py3 --gpus 4 --cpu 8 --memory 32Gi --command "python train.py"

Explanation: This command submits a training job requesting 4 GPUs, 8 CPUs, and 32 GB memory using an NGC PyTorch container.

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