From vastai-pack
Applies Vast.ai SDK patterns for Python and REST API: typed GPU queries, managed instance lifecycles, offer scoring, retries. For GPU cloud integrations and refactoring.
How this skill is triggered — by the user, by Claude, or both
Slash command
/vastai-pack:vastai-sdk-patternsThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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.
vastai-install-auth setuprequestsfrom 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
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")
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)
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
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
GPUQuery builder for search filters| Error | Cause | Solution |
|---|---|---|
| Offer unavailable | Already rented | Re-search and pick next best |
| SSH key rejected | Key not uploaded | Upload at cloud.vast.ai > SSH Keys |
| Instance destroyed unexpectedly | Spot preemption | Use managed_instance with checkpoints |
| API timeout | Network or server issue | Apply retry decorator |
See vastai-core-workflow-a for the complete provisioning workflow.
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.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin vastai-packRents first GPU instance on Vast.ai via CLI, REST API, or Python; runs PyTorch workload and manages full lifecycle for beginners.
Creates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.