Execute Vast.ai production deployment checklist and rollback procedures.
Use when deploying Vast.ai integrations to production, preparing for launch,
or implementing go-live procedures.
Trigger with phrases like "vastai production", "deploy vastai",
"vastai go-live", "vastai launch checklist".
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)
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.
