Vastai Deploy Integration

Vast.ai Deploy Integration

Overview

Deploy ML training jobs and inference services on Vast.ai GPU cloud. Covers instance provisioning via the REST API, Docker image configuration, data transfer strategies, and automated deployment scripts for GPU workloads.

Prerequisites

• Vast.ai account with API key stored in VASTAI_API_KEY environment variable
• Vast.ai CLI installed (pip install vastai)
• Docker image for your workload published to a registry
• SSH key configured for instance access

Instructions

Step 1: Search and Provision GPU

set -euo pipefail
# Search for available GPUs
vastai search offers 'gpu_name=RTX_4090 reliability2>0.95 disk_space>50' \
  -o 'dph_total' --limit 5

# Create instance from best offer
vastai create instance $OFFER_ID \
  --image pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime \
  --disk 100 \
  --onstart-cmd "cd /workspace && git clone https://github.com/myorg/project.git && pip install -r project/requirements.txt"

Step 2: Deploy Custom Docker Image

# Dockerfile.gpu
FROM pytorch/pytorch:2.1.0-cuda12.1-cudnn8-runtime

WORKDIR /workspace
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

CMD ["python", "train.py", "--config", "config.yaml"]

set -euo pipefail
# Build and push
docker build -f Dockerfile.gpu -t myregistry/ml-trainer:latest .
docker push myregistry/ml-trainer:latest

# Deploy on Vast.ai
vastai create instance $OFFER_ID \
  --image myregistry/ml-trainer:latest \
  --disk 100 \
  --env "WANDB_API_KEY=$WANDB_API_KEY HF_TOKEN=$HF_TOKEN"

Step 3: Automated Deployment Script

import requests
import json
import os

VASTAI_API = "https://cloud.vast.ai/api/v0"
API_KEY = os.environ["VASTAI_API_KEY"]

def deploy_training_job(gpu_type="RTX_4090", disk_gb=100):
    # Find cheapest matching offer
    response = requests.get(f"{VASTAI_API}/bundles", params={
        "api_key": API_KEY,
        "q": json.dumps({
            "gpu_name": {"eq": gpu_type},
            "rentable": {"eq": True},
            "disk_space": {"gte": disk_gb},
            "reliability2": {"gte": 0.95},
        }),
        "order": "dph_total",
        "limit": 1,
    })

    offers = response.json()["offers"]
    if not offers:
        raise ValueError(f"No {gpu_type} available")

    # Provision instance
    result = requests.put(
        f"{VASTAI_API}/asks/{offers[0]['id']}/",
        params={"api_key": API_KEY},
        json={
            "image": "myregistry/ml-trainer:latest",
            "disk": disk_gb,
            "env": {"WANDB_API_KEY": os.environ.get("WANDB_API_KEY", "")},
        },
    )

    instance = result.json()
    print(f"Instance {instance['new_contract']} created at ${offers[0]['dph_total']}/hr")
    return instance

Step 4: Monitor and Cleanup

# List running instances
vastai show instances

# Check instance status
vastai show instance $INSTANCE_ID

# Download results
vastai scp $INSTANCE_ID:/workspace/output ./results/

# Destroy instance when done
vastai destroy instance $INSTANCE_ID

Error Handling

Examples

Quick Training Deploy

vastai search offers 'gpu_name=A100_SXM4 num_gpus=1' -o 'dph_total' --limit 3
vastai create instance $BEST_OFFER --image myregistry/trainer:latest --disk 200  # HTTP 200 OK

Resources

• Vast.ai Documentation
• Vast.ai CLI Reference
• GPU Pricing

Next Steps

For multi-environment setup, see vastai-multi-env-setup.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale