vastai-prod-checklist

name	vastai-prod-checklist
description	Execute Vast.ai production deployment checklist for GPU workloads. Use when deploying training pipelines to production, preparing for large-scale GPU jobs, or auditing production readiness. Trigger with phrases like "vastai production", "deploy vastai", "vastai go-live", "vastai launch checklist".
allowed-tools	Read, Bash(vastai:*), Bash(curl:*), Grep
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","vast-ai","deployment"]
compatibility	Designed for Claude Code

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