Vast.ai Production Checklist

Overview

Complete checklist for running production GPU workloads on Vast.ai, covering account setup, instance selection, data safety, monitoring, and cost controls.

Prerequisites

• Vast.ai account with sufficient credits
• Docker images tested and published to registry
• Checkpoint-based training pipeline

Instructions

Account & Authentication

• API key stored in secrets manager (not in code or env files)
• Dedicated SSH key pair for Vast.ai (not shared with other services)
• Account balance sufficient for planned workload duration + 50% buffer
• Billing alerts configured at cloud.vast.ai

Instance Selection

• GPU type validated for workload (VRAM, compute capability)
• Reliability filter set to

  >= 0.98

  for production jobs
• Internet speed filter set to

  inet_down >= 200

  for data transfer
• Disk allocation includes room for checkpoints + data + 20% overhead
• CUDA version on host matches Docker image requirements

Data Safety

• Training data encrypted before upload to instances
• Checkpoint saving every N steps (not just per epoch)
• Checkpoints uploaded to persistent storage (S3/GCS) periodically
• Instance cleanup script removes data before destruction
• No sensitive data (API keys, PII) embedded in Docker images

Spot Instance Protection

• Spot preemption handler implemented (save checkpoint on SIGTERM)
• Auto-recovery: detect destroyed instance, provision replacement, resume
• On-demand fallback configured for critical final training stages
• Checkpoint integrity verification after recovery

Monitoring & Alerting

• GPU utilization monitoring (alert if < 50% for > 10 min)
• Instance health polling every 60 seconds
• Cost accumulation tracking with budget threshold alerts
• Training loss/metrics logged to external service (W&B, MLflow)
• Dead instance detection (auto-destroy stuck instances)

Cost Controls

• Maximum

  dph_total

  set in search queries
• Auto-destroy timeout for all instances (e.g., 24h max)
• Daily spending limit configured
• Cost-per-job tracking for budget reporting

Verification Script

#!/bin/bash
set -euo pipefail
echo "Vast.ai Production Readiness Check"

# 1. Auth
vastai show user --raw | python3 -c "
import sys, json; u=json.load(sys.stdin)
balance = u.get('balance', 0)
print(f'  Auth: OK | Balance: \${balance:.2f}')
assert balance >= 10, f'Balance too low: \${balance:.2f}'
" && echo "  Balance: PASS" || echo "  Balance: FAIL"

# 2. Offer availability
COUNT=$(vastai search offers 'reliability>0.98 num_gpus=1 rentable=true' --raw --limit 1 | python3 -c "import sys,json; print(len(json.load(sys.stdin)))")
echo "  Offers available: $COUNT+ | PASS"

# 3. Docker image pullable
docker pull pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime > /dev/null 2>&1 && echo "  Docker image: PASS" || echo "  Docker image: FAIL"

echo "Pre-flight checks complete."

Output

• Production readiness checklist verified
• Verification script passes all checks
• Cost controls and monitoring configured
• Data safety measures in place

Error Handling

Error	Cause	Solution
Insufficient balance	Credits depleted mid-job	Set up auto-top-up or balance alerts
Instance preempted during final epoch	Spot instance reclaimed	Use on-demand for final training stage
Checkpoint corrupted	Interrupted mid-save	Implement atomic checkpoint writes (save to temp, rename)
GPU utilization drops to 0%	Data pipeline bottleneck	Profile data loading; increase disk I/O

Resources

• Vast.ai Documentation
• Instance Types

Next Steps

For version upgrades, see

vastai-upgrade-migration

.

Examples

Pre-launch audit: Run the verification script, check all boxes, confirm Docker image pulls successfully, and verify at least 3 matching offers are available before starting a production training run.

Budget-safe launch: Set

max_dph=2.00

, auto-destroy timeout of 12 hours, and daily spend alert at $50 to prevent cost overruns.