vastai-core-workflow-b

name	vastai-core-workflow-b
description	Execute Vast.ai secondary workflow: multi-instance orchestration, spot recovery, and cost optimization. Use when running distributed training, handling spot preemption, or optimizing GPU spend across multiple instances. Trigger with phrases like "vastai distributed training", "vastai spot recovery", "vastai multi-gpu", "vastai cost optimization".
allowed-tools	Read, Write, Edit, Bash(vastai:*), Bash(curl:*), Grep
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","vast-ai","workflow"]
compatibility	Designed for Claude Code

Vast.ai Core Workflow B: Multi-Instance & Cost Optimization

Overview

Secondary workflow for Vast.ai: orchestrate multiple GPU instances for distributed training, implement automatic spot interruption recovery with checkpoint-based resume, and analyze spending to reduce per-job cost.

Prerequisites

• Completed vastai-core-workflow-a
• Understanding of distributed training (PyTorch DDP, DeepSpeed)
• Checkpoint-based training pipeline

Instructions

Step 1: Multi-Instance Provisioning

import subprocess, json, time
from concurrent.futures import ThreadPoolExecutor

def provision_cluster(num_nodes, gpu_name="A100", min_vram=80, image=""):
    """Provision multiple GPU instances for distributed training."""
    # Search for matching offers
    query = (f"num_gpus=1 gpu_name={gpu_name} gpu_ram>={min_vram} "
             f"reliability>0.98 inet_down>500 rentable=true")
    result = subprocess.run(
        ["vastai", "search", "offers", query, "--order", "dph_total",
         "--raw", "--limit", str(num_nodes * 3)],
        capture_output=True, text=True, check=True,
    )
    offers = json.loads(result.stdout)
    if len(offers) < num_nodes:
        raise RuntimeError(f"Only {len(offers)} offers, need {num_nodes}")

    # Provision nodes in parallel
    instances = []
     i, offer  (offers[:num_nodes]):
        inst_id = provision_single(offer[], image, rank=i)
        instances.append({: inst_id, : i, : offer})

    
     inst  instances:
        info = wait_for_running(inst[])
        inst.update({: info[], : info[]})

     instances