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

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 = []
    for i, offer in enumerate(offers[:num_nodes]):
        inst_id = provision_single(offer["id"], image, rank=i)
        instances.append({"id": inst_id, "rank": i, "offer": offer})

    # Wait for all to be running
    for inst in instances:
        info = wait_for_running(inst["id"])
        inst.update({"ssh_host": info["ssh_host"], "ssh_port": info["ssh_port"]})

    return instances

Step 2: Spot Interruption Recovery

class SpotRecoveryManager:
    """Monitor instances and replace preempted spot instances."""

    def __init__(self, client, checkpoint_dir="/workspace/checkpoints"):
        self.client = client
        self.checkpoint_dir = checkpoint_dir

    def monitor_and_recover(self, instances, image, poll_interval=60):
        """Poll instance status; replace any that are destroyed/error."""
        while True:
            for inst in instances:
                result = subprocess.run(
                    ["vastai", "show", "instance", str(inst["id"]), "--raw"],
                    capture_output=True, text=True,
                )
                info = json.loads(result.stdout)
                status = info.get("actual_status", "unknown")

                if status in ("exited", "error", "offline"):
                    print(f"Instance {inst['id']} lost (status={status}). Replacing...")
                    new_inst = self.replace_instance(inst, image)
                    inst.update(new_inst)

            time.sleep(poll_interval)

    def replace_instance(self, old_inst, image):
        """Provision replacement and resume from last checkpoint."""
        # Search for a new offer
        offers = search_offers(gpu_name=old_inst["offer"]["gpu_name"])
        new_id = provision_single(offers[0]["id"], image, rank=old_inst["rank"])
        info = wait_for_running(new_id)

        # Upload last checkpoint to new instance
        subprocess.run([
            "scp", "-P", str(info["ssh_port"]), "-r",
            f"{self.checkpoint_dir}/",
            f"root@{info['ssh_host']}:/workspace/checkpoints/",
        ], check=True)

        return {"id": new_id, "ssh_host": info["ssh_host"],
                "ssh_port": info["ssh_port"]}

Step 3: Cost Analysis

def analyze_spending():
    """Pull billing history and compute cost-per-GPU-hour by GPU type."""
    result = subprocess.run(
        ["vastai", "show", "invoices", "--raw"],
        capture_output=True, text=True,
    )
    invoices = json.loads(result.stdout)

    # Aggregate by GPU type
    by_gpu = {}
    for inv in invoices:
        gpu = inv.get("gpu_name", "unknown")
        cost = inv.get("total_cost", 0)
        hours = inv.get("duration_hours", 0)
        if gpu not in by_gpu:
            by_gpu[gpu] = {"total_cost": 0, "total_hours": 0}
        by_gpu[gpu]["total_cost"] += cost
        by_gpu[gpu]["total_hours"] += hours

    print("GPU Cost Summary:")
    for gpu, data in sorted(by_gpu.items(), key=lambda x: x[1]["total_cost"], reverse=True):
        avg = data["total_cost"] / max(data["total_hours"], 1)
        print(f"  {gpu}: ${data['total_cost']:.2f} total, "
              f"{data['total_hours']:.1f}hrs, ${avg:.3f}/hr avg")

Step 4: Destroy Cluster

def destroy_cluster(instances):
    """Destroy all instances in a cluster to stop billing."""
    for inst in instances:
        subprocess.run(
            ["vastai", "destroy", "instance", str(inst["id"])],
            check=True,
        )
        print(f"Destroyed instance {inst['id']} (rank {inst['rank']})")
    print(f"All {len(instances)} instances destroyed — billing stopped")

Output

• Multi-node GPU cluster provisioned from marketplace offers
• Automatic spot interruption detection and recovery with checkpoint resume
• Cost analysis report comparing GPU types and actual spend
• Clean cluster teardown stopping all billing

Error Handling

Resources

• Vast.ai Instance Types
• Search Filtering
• CLI Reference

Next Steps

For common errors, see vastai-common-errors.

Examples

Distributed fine-tuning: Provision 4x A100 instances, configure PyTorch DDP with torchrun --nproc_per_node=1 --nnodes=4, save checkpoints every 500 steps, and implement spot recovery to auto-resume from the latest checkpoint.

Cost comparison: Run the same workload on RTX 4090 ($0.20/hr) vs A100 ($1.50/hr) and compare wall-clock time vs total cost to find the optimal GPU type for your specific model.