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, also compatible with Codex and OpenClaw

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