Apply production-ready Vast.ai SDK patterns for TypeScript and Python.
Use when implementing Vast.ai integrations, refactoring SDK usage,
or establishing team coding standards for Vast.ai.
Trigger with phrases like "vastai SDK patterns", "vastai best practices",
"vastai code patterns", "idiomatic vastai".
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")
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.
