# Jeremylongshore Claude Code Plugins Plus Skills Vastai Sdk Patterns

> Vast.ai SDK Patterns

- Skill: `tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-sdk-p` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-sdk-p`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-sdk-p/raw
- Safety review: pending (external: skill-scanner WARNING, 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-sdk-p

---

# 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

```python
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

```python
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

```python
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

```python
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

```python
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
| 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 |

## Resources
- [REST API Reference](https://vast.ai/developers/api)
- [Search Filtering](https://docs.vast.ai/search-and-filter-gpu-offers)
- [vast-cli GitHub](https://github.com/vast-ai/vast-cli)

## 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.

---
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