Allocate resources between teams across platforms: Quick Reference — Workload Management (NVIDIA-Certified Professional: AI Operations)

Quick Reference: Allocating Resources Between Teams Across Platforms

Effective resource allocation is critical for managing AI workloads across multiple teams and platforms in NVIDIA AI Operations environments. This quick reference provides key facts, definitions, and rules to streamline resource sharing and maximize infrastructure utilization.

Key Concepts

• Resource Allocation: Distributing compute, storage, and networking resources fairly and efficiently among teams running AI workloads.
• Multi-Platform Management: Coordinating resources across Kubernetes clusters, Slurm-managed HPC systems, and Run:ai orchestration layers.
• Containers from NGC: Deploying AI workloads using NVIDIA GPU Cloud (NGC) containers to ensure portability and consistency.

Core Principles

• Fair Share Scheduling: Use scheduling policies that balance resource access based on team priorities and historical usage.
• Quota Enforcement: Define resource quotas per team to prevent overconsumption and ensure predictable performance.
• Dynamic Scaling: Enable elastic resource allocation to adjust to workload demands in real-time.
• Cross-Platform Coordination: Synchronize resource policies between Kubernetes, Slurm, and Run:ai to avoid conflicts and maximize utilization.

Tools and Techniques

• Run:ai: Provides AI workload orchestration with advanced resource allocation, enabling teams to share GPUs dynamically and prioritize workloads.
• Kubernetes: Use namespaces, resource quotas, and limit ranges to isolate and control team resource usage.
• Slurm: Employ partitions and fair-share scheduling to allocate HPC resources among teams.
• NGC Containers: Deploy workloads in standardized containers ensuring consistent environments across platforms.

Best Practices

• Establish clear resource allocation policies aligned with organizational priorities.
• Monitor resource consumption continuously using system management tools integrated with Run:ai and Slurm.
• Automate workload scheduling to reduce manual intervention and improve responsiveness.
• Communicate resource availability and usage transparently to all teams.

Common Commands and Configurations

• Run:ai CLI: runai submit --gpu 2 --priority high --queue team-queue to allocate GPUs with priority.
• Kubernetes: Define resource quotas in YAML:apiVersion: v1kind: ResourceQuotametadata: name: team-quotaspec: hard: limits.cpu: "20" limits.memory: 64Gi
• Slurm: Configure partitions and fair-share:PartitionName=teamA Nodes=node[01-10] Default=YES MaxTime=INFINITE State=UP

Summary

Allocating resources between teams across platforms in NVIDIA AI Operations requires a coordinated approach using Run:ai, Kubernetes, Slurm, and NGC containers. By applying fair share scheduling, enforcing quotas, and leveraging orchestration tools, AI infrastructure managers can optimize utilization and ensure equitable access to resources.

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