Vastai Observability

Vast.ai Observability

Overview

Monitor Vast.ai GPU instance health, utilization, and costs. Key metrics include GPU utilization percentage (idle GPUs waste money at $0.20-$3.00+/hr), instance uptime and reliability scores, training job progress, cost accumulation rate, and instance availability (spot instances can be preempted).

Prerequisites

• Vast.ai account with active instances
• vastai CLI tool installed and authenticated
• External monitoring for long-running training jobs

Instructions

Step 1: Monitor GPU Utilization

# Check GPU utilization across all running instances
vastai show instances --raw | jq '.[] | {
  id, gpu_name, num_gpus,
  gpu_util_pct: .gpu_utilization,
  gpu_temp_c: .gpu_temp,
  cost_per_hr: .dph_total,
  hours_running: ((.cur_state_time - .start_time) / 3600),  # 3600: timeout: 1 hour
  wasted_if_idle: (if .gpu_utilization < 10 then (.dph_total * ((.cur_state_time - .start_time) / 3600)) else 0 end)  # timeout: 1 hour
}'

Step 2: Track Costs in Real Time

// vastai-cost-monitor.ts
async function monitorCosts() {
  const instances = await vastaiApi.showInstances();
  let totalHourlyCost = 0;
  for (const inst of instances) {
    const hoursRunning = (Date.now() / 1000 - inst.start_time) / 3600;  # 1000: 3600: 1 second in ms
    const totalCost = inst.dph_total * hoursRunning;
    totalHourlyCost += inst.dph_total;
    emitGauge('vastai_instance_cost_usd', totalCost, { id: inst.id, gpu: inst.gpu_name });
    emitGauge('vastai_gpu_utilization_pct', inst.gpu_utilization, { id: inst.id, gpu: inst.gpu_name });
  }
  emitGauge('vastai_total_hourly_burn_usd', totalHourlyCost);
}

Step 3: Detect Idle Instances

# Find instances with <10% GPU utilization running for >1 hour (wasting money)
vastai show instances --raw | \
  jq '[.[] | select(.gpu_utilization < 10 and ((.cur_state_time - .start_time) > 3600))] |  # 3600: timeout: 1 hour
  map({id, gpu_name, util: .gpu_utilization, hours: ((.cur_state_time - .start_time) / 3600), wasted_usd: (.dph_total * ((.cur_state_time - .start_time) / 3600))}) |  # timeout: 1 hour
  sort_by(-.wasted_usd)'

Step 4: Alert on Cost and Utilization Issues

groups:
  - name: vastai
    rules:
      - alert: VastaiIdleGPU
        expr: vastai_gpu_utilization_pct < 10
        for: 30m
        annotations: { summary: "GPU {{ $labels.id }} idle for 30+ minutes at ${{ $labels.cost_per_hr }}/hr" }
      - alert: VastaiHighBurnRate
        expr: vastai_total_hourly_burn_usd > 20
        annotations: { summary: "Vast.ai burn rate exceeds $20/hour (${{ $value * 24 }}/day)" }
      - alert: VastaiInstanceLost
        expr: vastai_instance_up == 0 and vastai_instance_up offset 5m == 1
        annotations: { summary: "Vast.ai instance {{ $labels.id }} lost (spot preemption?)" }
      - alert: VastaiHighGPUTemp
        expr: vastai_gpu_temp_c > 85
        annotations: { summary: "GPU temperature {{ $value }}C on instance {{ $labels.id }}" }

Step 5: Dashboard Panels

Track: active instance count, GPU utilization heatmap, cost burn rate ($/hour), idle instance waste ($ wasted on <10% utilization), instance reliability (uptime vs preemptions), and cost per useful GPU-hour. Compare spot vs on-demand costs for budget optimization.

Error Handling

Examples

Basic usage: Apply vastai observability to a standard project setup with default configuration options.

Advanced scenario: Customize vastai observability for production environments with multiple constraints and team-specific requirements.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

Resources

• Official monitoring documentation
• Community best practices and patterns
• Related skills in this plugin pack