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volcano-node-resources

Query Kubernetes node allocatable CPU, memory, and GPU resources.

225|28|Updated Mar 2, 2026
Search Tags:#kubernetes#diagnostics#scheduling#resource monitoring#volcano#node resources
One-click install
npx skills add https://github.com/scitix/siclaw --skill volcano-node-resources
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: volcano-node-resources
Source: https://github.com/scitix/siclaw/tree/main/skills/core/volcano-node-resources
Command: npx skills add https://github.com/scitix/siclaw --skill volcano-node-resources

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps SREs and DevOps engineers understand the CPU, memory, and GPU capacity and current usage of their Kubernetes nodes, crucial for efficient scheduling and troubleshooting resource-related issues in Volcano environments.

Core Features & Use Cases

  • Resource Querying: Retrieves allocatable and requested resources (CPU, memory, GPU) for nodes.
  • Usage Monitoring: Optionally displays current resource usage if metrics-server is available.
  • Pod Visibility: Can list pods running on specific nodes to identify resource consumers.
  • Use Case: Quickly identify nodes with sufficient available CPU and memory to schedule a new batch job, or check GPU availability for a machine learning workload.

Quick Start

Run the get-node-resources script to see an overview of all nodes in the cluster.

Frequently Asked Questions about volcano-node-resources

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I check Kubernetes node resource availability for scheduling batch jobs?▼

Check Kubernetes node resource availability by querying allocatable CPU, memory, and GPU capacity alongside current usage. This helps identify nodes with sufficient resources to schedule new batch workloads or troubleshoot scheduling constraints.

What is the best way to diagnose why a Volcano workload is stuck pending in Kubernetes?▼

Diagnose Volcano pending workloads by querying node resources to reveal allocatable CPU, memory, and GPU limits versus current requests. Identifying resource exhaustion on specific nodes clarifies why the scheduler cannot place the workload.

Do I need metrics-server to monitor current CPU and memory usage on Kubernetes nodes?▼

You do not need metrics-server to view allocatable resources and running pods, but it is required optionally to display real-time current CPU and memory usage data for nodes in the cluster.

Can I list running pods on a specific node to identify resource consumers in Kubernetes?▼

You can list running pods on specific Kubernetes nodes to identify which workloads are consuming resources. This visibility helps pinpoint the consumers causing allocation bottlenecks and scheduling constraints.

How does GPU availability monitoring work for machine learning workloads in Kubernetes?▼

GPU availability monitoring works by querying Kubernetes nodes for allocatable GPU capacity and current requests. This assists in checking if sufficient GPU resources exist to schedule machine learning workloads or other GPU-dependent tasks.

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