vastai-deploy-integration

name	vastai-deploy-integration
description	Deploy ML training jobs and inference services on Vast.ai GPU cloud. Use when deploying GPU workloads, configuring Docker images, or setting up automated deployment scripts. Trigger with phrases like "deploy vastai", "vastai deployment", "vastai docker", "vastai production deploy".
allowed-tools	Read, Write, Edit, Bash(vastai:*), Bash(docker:*), Bash(ssh:*)
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","vast-ai","deployment"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

Vast.ai Deploy Integration

Overview

Deploy ML training jobs and inference services on Vast.ai GPU cloud. Covers Docker image optimization, automated provisioning scripts, data transfer strategies, and deployment automation.

Prerequisites

• Vast.ai CLI authenticated
• Docker image published to a registry
• Training/inference code tested locally

Instructions

Step 1: Optimized Docker Image

# Dockerfile.vastai — optimized for fast pulls on Vast.ai
FROM pytorch/pytorch:2.2.0-cuda12.1-cudnn8-runtime

# Install dependencies in a single layer
COPY requirements.txt /tmp/
RUN pip install --no-cache-dir -r /tmp/requirements.txt && rm /tmp/requirements.txt

# Copy application code
COPY src/ /workspace/src/
COPY scripts/ /workspace/scripts/

WORKDIR /workspace
CMD ["python", "src/train.py"]

# Build and push
docker build -t ghcr.io/yourorg/training:v1 -f Dockerfile.vastai .
docker push ghcr.io/yourorg/training:v1

Step 2: Automated Deployment Script

#!/usr/bin/env python3
"""deploy.py — Automated Vast.ai deployment with monitoring."""
import subprocess, json, time, argparse, sys

def deploy(args):
    # Search for matching offer
    query = (f"num_gpus={args.gpus} gpu_name={args.gpu} "
             f"reliability>{args.reliability} dph_total<={args.max_price} "
             f"disk_space>={args.disk} rentable=true")

    offers = json.loads(subprocess.run(
        ["vastai", "search", "offers", query, "--order", "dph_total",
         "--raw", "--limit", "5"],
        capture_output=True, text=True, check=True).stdout)

      offers:
        (, file=sys.stderr)
        sys.exit()

    offer = offers[]
    (
          )

    
    cmd = [, , , (offer[]),
           , args.image, , (args.disk)]
     args.onstart:
        cmd.extend([, args.onstart])

    result = json.loads(subprocess.run(
        cmd, capture_output=, text=, check=).stdout)
    instance_id = result[]
    ()

    
     _  ():
        info = json.loads(subprocess.run(
            [, , , (instance_id), ],
            capture_output=, text=).stdout)
         info.get() == :
            ()
             instance_id, info
        time.sleep()

     TimeoutError()

 __name__ == :
    parser = argparse.ArgumentParser()
    parser.add_argument(, default=)
    parser.add_argument(, =, default=)
    parser.add_argument(, required=)
    parser.add_argument(, =, default=)
    parser.add_argument(, =, default=)
    parser.add_argument(, =, default=)
    parser.add_argument(, default=)
    deploy(parser.parse_args())