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

Allocating Resources Between Teams Across Platforms: A Worked Example

Effective resource allocation between teams across multiple platforms is a critical skill for NVIDIA-Certified Professional: AI Operations candidates, especially within the Workload Management domain. This example demonstrates how to allocate GPU resources fairly and efficiently between two AI teams using Kubernetes and Run:AI, ensuring optimal utilization and minimal contention.

Scenario

Two AI teams, Team Alpha and Team Beta, share a cluster with 8 NVIDIA GPUs. Team Alpha requires priority access to 5 GPUs for training large models, while Team Beta needs 3 GPUs for inference workloads. The goal is to allocate resources dynamically across Kubernetes and Run:AI platforms, enabling both teams to run workloads without interference.

Step 1: Assess Available Resources

• Identify total GPUs: 8 GPUs available on the cluster nodes.
• Confirm platform usage: Team Alpha uses Run:AI for training; Team Beta uses Kubernetes native pods for inference.

Step 2: Define Resource Quotas and Limits

On Kubernetes, create ResourceQuotas and LimitRanges to restrict Team Beta’s namespace to a maximum of 3 GPUs:

• Define a namespace for Team Beta.
• Apply a ResourceQuota limiting nvidia.com/gpu requests to 3.
• Set LimitRanges to enforce GPU usage per pod.

Example Kubernetes YAML snippet for Team Beta namespace:

apiVersion: v1 kind: ResourceQuota metadata: name: gpu-quota namespace: team-beta spec: hard: requests.nvidia.com/gpu: "3" limits.nvidia.com/gpu: "3"

Step 3: Configure Run:AI for Team Alpha

Run:AI enables dynamic scheduling and fair sharing of GPUs. For Team Alpha:

• Create a Run:AI project with a quota of 5 GPUs.
• Assign Team Alpha users to this project.
• Configure Run:AI scheduler to prioritize Team Alpha’s training workloads.

This ensures Team Alpha can submit training jobs that consume up to 5 GPUs, with Run:AI managing scheduling and preemption if necessary.

Step 4: Implement Cross-Platform Monitoring and Enforcement

Use NVIDIA system management tools (e.g., nvidia-smi, DCGM) and Run:AI dashboards to monitor GPU utilization across both platforms:

• Track GPU allocation in Kubernetes namespaces and Run:AI projects.
• Identify resource contention early.
• Adjust quotas dynamically if workloads change.

Step 5: Deploy and Test Workloads

Team Beta deploys inference pods within their Kubernetes namespace, limited to 3 GPUs.

Team Alpha submits training jobs via Run:AI CLI or UI, consuming up to 5 GPUs.

Verify that neither team exceeds their allocated resources and that workloads run without failure.

Worked Example Summary

Problem: Allocate 8 GPUs between two teams across Kubernetes and Run:AI platforms.

Solution Steps:

1. Identify total GPUs and platform usage.
2. Set Kubernetes ResourceQuotas and LimitRanges for Team Beta (3 GPUs).
3. Configure Run:AI project quota for Team Alpha (5 GPUs).
4. Monitor GPU usage with NVIDIA tools and Run:AI dashboard.
5. Deploy workloads and verify resource compliance.

This approach ensures fair and enforceable resource allocation across heterogeneous platforms, a key competency for AI Operations professionals.

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