NCP-AIO NVIDIA AI Operations Questions and Answers

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In Run:ai, roles are assigned based on levels of permissions. TheL1 Researcherrole is designed for users who need access to GPU resources for running jobs and experiments but should not have administrative rights over cluster settings or other users. This role ensures researchers can use resources without affecting cluster configurations or user management. Other roles like Department Administrator, Application Administrator, or Research Manager have broader privileges, including managing users and settings, which are not appropriate for the new researcher’s requirements.

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A best practice for active-passive HA clusters, such as for firewall systems managed via BCM, is touse a heartbeat networkto synchronize session state data between active and passive nodes. This real-time synchronization allows the passive node to take over seamlessly in case the active node fails, maintaining session continuity and minimizing downtime. Configuring different zone names or firewall models can cause incompatibility, and manual synchronization is prone to errors and delays.

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Misconfiguration related toMIG modecan cause Slurm to improperly allocate GPUs, leading to job failures. The administrator should verify whether MIG has been enabled on the GPUs and ensure that Slurm’s configuration matches the hardware setup. If MIG is enabled, Slurm must be configured to recognize and schedule MIG partitions correctly to avoid resource conflicts.

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Slow GPU-to-GPU communication in distributed systems often relates to theconfiguration of communication libraries such as NCCL (NVIDIA Collective Communications Library) or NVSHMEM. Ensuring these libraries are properly configured and optimized is critical for efficient GPU communication. Limiting GPUs or increasing RAM does not directly improve communication speed, and disabling InfiniBand would degrade performance.

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GPUDirect Storage allows data to be transferred directly from storage to GPU memory,bypassing the CPU and system memory. This reduces latency and overhead by avoiding data movement through the CPU and main memory, accelerating data feeding to GPUs for AI workloads. PCIe and NIC are still involved in the data path, and the DPU may participate depending on architecture but are not the primary bottlenecks avoided by GPUDirect Storage.

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When automating tasks with the Run:AI Administrator CLI, it is essential to ensure that theKubernetes configuration file (kubeconfig)is correctly set up with cluster administrative rights. This enables the CLI to interact programmatically with the Kubernetes API for managing nodes, resources, and workloads efficiently. Without proper administrative permissions in the kubeconfig, automated operations will fail due to insufficient rights.

Manual GPU allocation is typically handled by scheduling policies rather than CLI manual assignments. The CLI does not replacekubectlcommands entirely, and installation on Windows is not a critical requirement.

The Run:AI Administrator CLI requires a Kubernetes configuration file with cluster-administrative rights in order to perform automation or scripting tasks across the cluster. Without those rights, the CLI cannot manage nodes or resources programmatically.

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The Slurm commandscontrolprovides detailed job control and information capabilities. Usingscontrol(e.g.,scontrol show job ) can reveal comprehensive details about jobs, including pending jobs, and the specific reasons why they are delayed or blocked. It is the go-to command for in-depth troubleshooting of job states. Whilesacctprovides accounting information andsinfodisplays node and partition status, neither provides as detailed or actionable information on pending job causes asscontrol.

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The Slurm commandsstatis designed to provide real-time statistics about running jobs, including process-level details and resource usage such as CPU, memory, and GPU utilization. Usingsstat -j orsstat -j allows monitoring of active job resource consumption.

smapis not a standard Slurm command.

scontrol show jobgives job configuration and status but not real-time resource usage.

sinfodisplays node and partition information, not job-specific resource stats.

Therefore,sstatis the correct command for real-time job process and resource monitoring.

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The NVIDIA AI Enterprise Virtual Machine Images (VMIs), including those needed for NVIDIA Morpheus deployments, are made available through themajor cloud marketplacessuch as Azure Marketplace, Google Cloud Marketplace, and Amazon Web Services Marketplace. This provides easy, standardized access and deployment options for enterprise users. While NGC hosts containers and models, the VMIs specifically are offered via cloud marketplaces.

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To prioritize inference workloads over training jobs in Kubernetes, administrators should configurePriorityClassesandResourceQuotas. PriorityClasses allow assigning different priority levels to pods, ensuring that during resource contention, higher-priority pods (inference services) receive resources first. ResourceQuotas limit the resource consumption per namespace or user, controlling overall usage and reserving capacity for critical workloads. This setup effectively manages resource allocation and guarantees performance for inference jobs during peak times.

Increasing replicas or namespaces alone does not guarantee priority during contention.

HPA scales based on metrics but does not manage priority or resource guarantees directly.

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Comprehensive and Detailed Explanation From Exact Extract:

The correct command to scale a Kubernetes Job to a specific number of replicas iskubectl scale job --replicas=4. This explicitly sets the number of desired pod instances for the Job resource. The other commands are either invalid (stretch), apply to Deployments rather than Jobs (autoscale deployment), or use incorrect syntax (-r).

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Core dumps capture thememory state of a process at the time of its crash, providing a snapshot useful for post-mortem debugging. Analyzing core dumps helps identify the cause of segmentation faults or other critical errors by revealing what the process was doing at failure, including stack traces, variable states, and memory content.

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NVIDIA’s Base Command Manager is the software stack designed specifically for configuration, management, and monitoring of NVIDIA DGX systems, from a single DGX BasePOD up to large-scale SuperPOD deployments. It provides centralized management capabilities to orchestrate AI infrastructure, simplifying deployment, hardware monitoring, and lifecycle management across multiple clusters and data centers.

NetQ is focused on network monitoring and diagnostics rather than overall hardware cluster management.

Fleet Command is an enterprise SaaS solution to deploy and manage AI infrastructure in hybrid cloud environments but is not specifically targeted at on-premises DGX BasePOD to SuperPOD scale hardware management.

Magnum IO is NVIDIA’s high-performance data and storage software stack for managing I/O but not hardware or cluster configuration management.

Therefore, Base Command Manager is the correct and dedicated tool for managing multiple installations of NVIDIA DGX hardware spanning from BasePOD to SuperPOD environments.

This is consistent with NVIDIA’s official AI Operations documentation and product descriptions highlighting Base Command Manager as the unified command and control platform for AI infrastructure management.

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For large-scale AI workloads such as distributed training of large language models, the networking infrastructure must deliver extremely low latency and very high throughput to keep GPUs and compute nodes efficiently synchronized. NVIDIA highlights thatInfiniBand networkingis essential in AI data centers because it provides ultra-low latency, high bandwidth, adaptive routing, congestion control, and noise isolation—features critical for high-performance AI training clusters.

InfiniBand acts not just as a network but as acomputing fabric, integrating compute and communication tightly. Microsoft Azure, a leading cloud provider, uses thousands of miles of InfiniBand cabling to meet the demands of their AI workloads, demonstrating its importance. While Ethernet-based solutions like NVIDIA’s Spectrum-X are emerging and optimized for AI, InfiniBand remains the premier choice for AI supercomputing networks.

Therefore, for maximizing performance in a new AI cluster focused on distributed training,InfiniBand networking (option D)is the recommended architecture. Other Ethernet-based approaches provide scalability and bandwidth but cannot match InfiniBand’s specialized low-latency and high-throughput performance for AI.

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The standard method to verify that worker nodes are correctly registered and ready in a Kubernetes cluster is to runkubectl get nodes. This command lists all nodes and their statuses. Nodes showing a status of“Ready”indicates they are properly connected and available to schedule workloads. Checking pods or manual SSH is not the direct or reliable way to verify node readiness.

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In Slurm GPU resource management, thegres.conffile defines the available GPUs (generic resources) per node, whileslurm.confconfigures the cluster-wide GPU scheduling policies. To prevent jobs from using GPUs reserved for other purposes (e.g., display rendering GPUs), administrators must ensure that only the GPUs intended for compute workloads are listed in these configuration files.

Properly configuringgres.confallows Slurm to recognize and expose only those GPUs meant for jobs.

slurm.confmust be aligned to exclude or restrict unconfigured GPUs.

Manual GPU assignment usingnvidia-smiis not scalable or integrated with Slurm scheduling.

Reinstalling drivers or increasing GPU requests does not solve resource exclusion.

Thus, the correct approach is to verify and configure GPU listings accurately ingres.confandslurm.confto restrict job allocations to intended GPUs.

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