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

Vast.ai SDK Patterns

Overview

Production-ready patterns for the Vast.ai CLI, Python SDK, and REST API at cloud.vast.ai/api/v0. Covers typed search queries, instance lifecycle management, offer scoring, and error handling.

Prerequisites

• Completed vastai-install-auth setup
• Python 3.8+ with requests
• Familiarity with the Vast.ai marketplace model

Instructions

Pattern 1: Typed Search Query Builder

from dataclasses import dataclass
from typing import Optional

@dataclass
class GPUQuery:
    num_gpus: int = 1
    gpu_name: Optional[str] = None
    gpu_ram_min: Optional[float] = None
    reliability_min: float = 0.95
    max_dph: Optional[float] = None

    def to_filter(self) -> dict:
        f = {"rentable": {"eq": True}, "num_gpus": {"eq": self.num_gpus},
             "reliability2": {"gte": self.reliability_min}}
        if self.gpu_name:
            f["gpu_name"] = {"eq": self.gpu_name}
        if self.gpu_ram_min:
            f["gpu_ram"] = {"gte": self.gpu_ram_min}
        if self.max_dph:
            f["dph_total"] = {"lte": self.max_dph}
        return f

Pattern 2: Context-Managed Instance Lifecycle

from contextlib import contextmanager

@contextmanager
def managed_instance(client, offer_id, image, disk_gb=20, timeout=300):
    """Auto-destroy instance on exit or exception."""
    inst = client.create_instance(offer_id, image, disk_gb)
    instance_id = inst["new_contract"]
    try:
        info = client.poll_until_running(instance_id, timeout)
        yield info
    finally:
        client.destroy_instance(instance_id)

# Usage
with managed_instance(client, offer["id"], "pytorch/pytorch:latest") as inst:
    ssh_exec(inst["ssh_host"], inst["ssh_port"], "python train.py")

Pattern 3: Offer Scoring

def score_offer(offer, weights=None):
    w = weights or {"cost": 0.4, "reliability": 0.3, "perf": 0.3}
    return (w["cost"] * (1.0 / max(offer["dph_total"], 0.01)) +
            w["reliability"] * offer.get("reliability2", 0) * 100 +
            w["perf"] * offer.get("dlperf", 0))

best = max(offers, key=score_offer)

Pattern 4: Retry with Backoff

import time
from functools import wraps

def retry(max_attempts=3, backoff=2):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for i in range(max_attempts):
                try:
                    return func(*args, **kwargs)
                except Exception as e:
                    if i == max_attempts - 1: raise
                    time.sleep(backoff ** i)
        return wrapper
    return decorator

Pattern 5: SSH Command Executor

import subprocess

def ssh_exec(host, port, cmd, timeout=300):
    r = subprocess.run(
        ["ssh", "-p", str(port), "-o", "StrictHostKeyChecking=no",
         f"root@{host}", cmd],
        capture_output=True, text=True, timeout=timeout)
    if r.returncode != 0:
        raise RuntimeError(f"SSH failed: {r.stderr}")
    return r.stdout

Output

• Typed GPUQuery builder for search filters
• Context-managed instance lifecycle with auto-destroy
• Offer scoring algorithm (cost, reliability, performance)
• Retry decorator with exponential backoff
• SSH command executor for remote jobs

Error Handling

Resources

• REST API Reference
• Search Filtering
• vast-cli GitHub

Next Steps

See vastai-core-workflow-a for the complete provisioning workflow.

Examples

Cost-optimized scoring: Use weights {"cost": 0.7, "reliability": 0.2, "perf": 0.1} for batch jobs where price dominates. Use {"cost": 0.1, "reliability": 0.6, "perf": 0.3} for long training runs where uptime matters.

Auto-cleanup: Wrap any GPU job in managed_instance to guarantee destruction even on crash.