Administer Run:ai platforms: Quick Reference — Administration (NVIDIA-Certified Professional: AI Operations)

Administer Run:ai Platforms — Quick Reference

This quick reference provides essential facts and guidelines for administering Run:ai platforms within the scope of NVIDIA-Certified Professional: AI Operations certification. Run:ai enables efficient AI workload orchestration and GPU resource management in datacenter environments.

Key Concepts

• Run:ai Platform: A virtualization and orchestration layer that abstracts GPU resources to optimize AI workload scheduling and utilization.
• Virtual Clusters: Logical partitions of GPU resources assigned to teams or projects, enabling multi-tenant resource sharing.
• Scheduler: Manages job queues, prioritization, and GPU allocation based on policies and resource availability.
• Resource Quotas: Limits on GPU usage per user, team, or project to ensure fair sharing and prevent resource starvation.
• Job Submission: Users submit AI training or inference jobs through Run:ai CLI or APIs, specifying resource requirements and priorities.

Administration Tasks

• Cluster Setup: Integrate Run:ai with Kubernetes clusters and NVIDIA GPU nodes to enable resource virtualization.
• User and Team Management: Create and manage user accounts, assign roles, and organize users into teams with defined resource quotas.
• Virtual Cluster Configuration: Define virtual clusters with specific GPU shares and scheduling policies tailored to workload needs.
• Job Monitoring: Track job status, GPU utilization, and queue metrics via Run:ai dashboard or CLI commands.
• Policy Enforcement: Configure scheduling policies such as preemption, priority classes, and fair-share to optimize resource allocation.

Common Commands and Interfaces

• runai submit: Submit AI jobs specifying GPU count, priority, and virtual cluster.
• runai get jobs: List active and completed jobs with status and resource usage.
• runai create vc: Create virtual clusters with assigned GPU quotas.
• runai user add: Add users and assign roles or teams.
• runai quota set: Define GPU usage limits per user or team.

Best Practices

• Resource Optimization: Use virtual clusters to isolate workloads and prevent resource contention.
• Monitoring: Regularly review job and GPU utilization metrics to identify bottlenecks or underutilized resources.
• Access Control: Implement role-based access control (RBAC) to secure platform administration and job submission.
• Policy Tuning: Adjust scheduling policies dynamically based on workload patterns and priority changes.
• Integration: Ensure seamless integration with Kubernetes and NVIDIA GPU drivers for optimal performance.

Reference Links

• Run:ai Official Documentation
• NVIDIA Run:ai User Guide
• TRH Learning NVIDIA AI Operations Resources

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