# Jeremylongshore Claude Code Plugins Plus Skills Vastai Incident Runbook

> Vast.ai Incident Runbook

- Skill: `tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-incid` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-incid`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-incid/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-incid

---

# Vast.ai Incident Runbook

## Overview
Rapid incident response procedures for Vast.ai GPU instance failures. Covers triage, mitigation, recovery, and postmortem for common incident types: spot preemption, instance crashes, GPU failures, and billing issues.

## Prerequisites
- Vast.ai CLI access
- SSH access to instances (if still running)
- Checkpoint storage accessible (S3/GCS)

## Instructions

### Triage: Assess Impact (< 2 minutes)

```bash
#!/bin/bash
set -euo pipefail
echo "=== INCIDENT TRIAGE ==="
echo "Time: $(date -u)"

# 1. Check all instances
echo -e "\n--- Instance Status ---"
vastai show instances --raw | python3 -c "
import sys, json
for inst in json.load(sys.stdin):
    status = inst.get('actual_status', '?')
    flag = 'ALERT' if status in ('error', 'exited', 'offline') else 'OK'
    print(f'  [{flag}] ID:{inst[\"id\"]} Status:{status} '
          f'GPU:{inst.get(\"gpu_name\",\"?\")} \${inst.get(\"dph_total\",0):.3f}/hr')
"

# 2. Check if affected instance has recent logs
echo -e "\n--- Recent Logs (last 20 lines) ---"
vastai logs ${INSTANCE_ID:-0} --tail 20 2>/dev/null || echo "No logs available"

# 3. Check account balance
echo -e "\n--- Account ---"
vastai show user --raw | python3 -c "import sys,json; u=json.load(sys.stdin); print(f'Balance: \${u.get(\"balance\",0):.2f}')"
```

### Incident Type 1: Spot Preemption

**Symptoms**: Instance status changes from `running` to `exited` or `offline` without user action.

```bash
# 1. Verify preemption (not user error)
vastai show instance $ID --raw | python3 -c "
import sys, json; i=json.load(sys.stdin)
print(f'Status: {i.get(\"actual_status\")}')
print(f'Status msg: {i.get(\"status_msg\", \"none\")}')
"

# 2. Check if checkpoint was saved
# (depends on your checkpoint storage — S3, GCS, etc.)
aws s3 ls s3://bucket/checkpoints/ --recursive | tail -5

# 3. Provision replacement instance
vastai search offers "gpu_name=${GPU_NAME} reliability>0.98 rentable=true" \
  --order dph_total --limit 3

# 4. Create replacement and resume from checkpoint
vastai create instance $NEW_OFFER_ID --image $IMAGE --disk 50
```

### Incident Type 2: Training Job Crash

**Symptoms**: Instance running but training process exited with error.

```bash
# 1. SSH in and check logs
ssh -p $PORT root@$HOST "tail -100 /workspace/train.log 2>/dev/null || echo 'No log file'"

# 2. Common causes
ssh -p $PORT root@$HOST << 'CHECK'
# GPU memory issue?
nvidia-smi | grep -i "out of memory" && echo "OOM detected"
# Disk full?
df -h /workspace | tail -1
# Process still running?
ps aux | grep python | grep -v grep
CHECK

# 3. Restart training from checkpoint
ssh -p $PORT root@$HOST "cd /workspace && python train.py --resume-from latest"
```

### Incident Type 3: GPU Hardware Failure

**Symptoms**: `nvidia-smi` fails, CUDA errors, or ECC memory errors.

```bash
# 1. Check GPU health
ssh -p $PORT root@$HOST "nvidia-smi" || echo "GPU not responding"

# 2. This is a host-level failure — you cannot fix it
# Destroy the instance and provision on a different host
vastai destroy instance $ID

# 3. Report the host to Vast.ai support
echo "Report host ID to Vast.ai support for investigation"
```

### Incident Type 4: Billing Emergency

```bash
# Stop all billing immediately
echo "EMERGENCY: Destroying all instances"
vastai show instances --raw | python3 -c "
import sys, json, subprocess
for inst in json.load(sys.stdin):
    if inst.get('actual_status') in ('running', 'loading'):
        subprocess.run(['vastai', 'destroy', 'instance', str(inst['id'])])
        print(f'Destroyed instance {inst[\"id\"]}')
"
```

### Postmortem Template

```markdown
## Incident Report
- **Date**: YYYY-MM-DD
- **Duration**: X hours
- **Impact**: N instances affected, $X cost
- **Root cause**: [spot preemption / OOM / disk full / GPU failure]
- **Resolution**: [replaced instance / increased VRAM / expanded disk]
- **Prevention**: [higher reliability filter / checkpoints / auto-recovery]
```

## Output
- Triage script with instant status assessment
- Recovery procedures for 4 incident types
- Emergency billing stop command
- Postmortem template

## Error Handling
| Incident | MTTR Target | Recovery |
|----------|-------------|----------|
| Spot preemption | < 10 min | Auto-provision replacement, resume from checkpoint |
| Training crash | < 5 min | SSH in, diagnose, restart from checkpoint |
| GPU failure | < 15 min | Destroy instance, provision on different host |
| Billing emergency | < 1 min | Destroy all instances immediately |

## Resources
- [Vast.ai Status](https://status.vast.ai)
- [Vast.ai CLI](https://docs.vast.ai/cli/get-started)

## Next Steps
For data handling and security, see `vastai-data-handling`.

## Examples

**Auto-recovery script**: Run the event poller from `vastai-webhooks-events` with an auto-recovery handler that provisions a replacement within 5 minutes of preemption.

**Kill switch**: Keep `vastai show instances && vastai destroy instance ALL` aliased for emergency billing stops.

---
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<!-- tomevault:4.0:skill_md:2026-04-11 -->