vastai-deploy-integration

# 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 # 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"] ``` ```bash # 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 ```python #!/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", ...