Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/vastai-webhooks-events/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill vastai-webhooks-events. Copyright stays with the author (MIT).

Vast.ai Webhooks & Events

Overview

Build event-driven workflows around Vast.ai GPU instance lifecycle. Vast.ai does not provide traditional webhooks, so event detection relies on polling the REST API at cloud.vast.ai/api/v0 and reacting to instance status transitions (loading, running, exited, error, offline).

Prerequisites

• Vast.ai CLI authenticated
• Understanding of instance lifecycle states
• Python 3.8+ for event loop implementation

Instructions

Step 1: Instance Status Poller

import time, json, subprocess
from typing import Callable, Dict, List

class InstanceEventPoller:
    """Poll Vast.ai API and emit events on status transitions."""

    def __init__(self, api_key: str, poll_interval: int = 30):
        self.api_key = api_key
        self.poll_interval = poll_interval
        self.previous_states: Dict[int, str] = {}
        self.handlers: Dict[str, List[Callable]] = {}

    def on(self, event: str, handler: Callable):
        self.handlers.setdefault(event, []).append(handler)

    def poll_once(self):
        result = subprocess.run(
            ["vastai", "show", "instances", "--raw"],
            capture_output=True, text=True)
        instances = json.loads(result.stdout)

        for inst in instances:
            inst_id = inst["id"]
            status = inst.get("actual_status", "unknown")
            prev = self.previous_states.get(inst_id)

            if prev and prev != status:
                event = f"{prev}_to_{status}"
                for handler in self.handlers.get(event, []):
                    handler(inst)
                for handler in self.handlers.get("any_change", []):
                    handler(inst, prev, status)

            self.previous_states[inst_id] = status

    def run(self):
        print(f"Polling every {self.poll_interval}s...")
        while True:
            self.poll_once()
            time.sleep(self.poll_interval)

Step 2: Event Handlers

def on_instance_running(instance):
    print(f"Instance {instance['id']} is RUNNING")
    print(f"  SSH: ssh -p {instance['ssh_port']} root@{instance['ssh_host']}")
    # Trigger: start training job, send notification, etc.

def on_instance_exited(instance):
    print(f"Instance {instance['id']} EXITED")
    # Trigger: collect results, check for errors, notify team

def on_spot_preemption(instance, old_status, new_status):
    if old_status == "running" and new_status in ("exited", "offline"):
        print(f"ALERT: Instance {instance['id']} may have been preempted")
        # Trigger: auto-recovery, provision replacement

# Wire up handlers
poller = InstanceEventPoller(api_key)
poller.on("loading_to_running", on_instance_running)
poller.on("running_to_exited", on_instance_exited)
poller.on("any_change", on_spot_preemption)
poller.run()

Step 3: Auto-Recovery on Preemption

def auto_recover(instance, old_status, new_status):
    """Automatically replace preempted instances."""
    if old_status != "running" or new_status not in ("exited", "offline", "error"):
        return

    gpu_name = instance.get("gpu_name", "RTX_4090")
    image = instance.get("image_uuid", "pytorch/pytorch:latest")

    print(f"Auto-recovering {instance['id']} ({gpu_name})...")

    # Search for replacement
    offers = json.loads(subprocess.run(
        ["vastai", "search", "offers",
         f"gpu_name={gpu_name} reliability>0.98 rentable=true",
         "--order", "dph_total", "--raw", "--limit", "3"],
        capture_output=True, text=True, check=True).stdout)

    if offers:
        new_id = json.loads(subprocess.run(
            ["vastai", "create", "instance", str(offers[0]["id"]),
             "--image", image, "--disk", "50", "--raw"],
            capture_output=True, text=True, check=True).stdout)["new_contract"]
        print(f"Replacement instance: {new_id}")

Step 4: Cost Event Tracking

def track_costs(instance, old_status, new_status):
    """Log cost events for billing tracking."""
    if new_status == "running":
        print(f"BILLING START: Instance {instance['id']} "
              f"at ${instance.get('dph_total', 0):.3f}/hr")
    elif old_status == "running":
        print(f"BILLING STOP: Instance {instance['id']}")

Output

• Polling-based event detection for instance status changes
• Event handlers for running, exited, preempted states
• Auto-recovery on spot preemption
• Cost tracking event logger

Error Handling

Resources

• Vast.ai REST API
• Instance Management

Next Steps

For performance optimization, see vastai-performance-tuning.

Examples

Slack notifications: Wire on_instance_running to send a Slack message with SSH connection details. Wire on_spot_preemption to alert the team.

Training monitor: Track running_to_exited events. If exit was expected (job complete), collect results. If unexpected, trigger auto-recovery with checkpoint resume.