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

Vast.ai Multi-Environment Setup

Overview

Configure separate Vast.ai environments for development, staging, and production by using different API keys, GPU profiles, and spending limits. Vast.ai does not have built-in environment isolation, so you implement it through configuration.

Prerequisites

• Vast.ai accounts or API keys per environment
• Secrets manager for key storage
• Understanding of GPU profile requirements per tier

Instructions

Step 1: Environment Configuration

# config.py — environment-specific Vast.ai settings
import os
from dataclasses import dataclass

@dataclass
class VastEnvConfig:
    name: str
    api_key: str
    max_dph: float           # Maximum $/hr per instance
    max_instances: int       # Concurrent instance limit
    max_daily_spend: float   # Daily budget cap
    gpu_whitelist: list      # Allowed GPU types
    reliability_min: float   # Minimum reliability score
    auto_destroy_hours: int  # Auto-destroy timeout

ENVIRONMENTS = {
    "development": VastEnvConfig(
        name="development",
        api_key=os.environ.get("VASTAI_DEV_KEY", ""),
        max_dph=0.25,
        max_instances=2,
        max_daily_spend=5.00,
        gpu_whitelist=["RTX_3090", "RTX_4090"],
        reliability_min=0.90,
        auto_destroy_hours=2,
    ),
    "staging": VastEnvConfig(
        name="staging",
        api_key=os.environ.get("VASTAI_STAGING_KEY", ""),
        max_dph=2.00,
        max_instances=4,
        max_daily_spend=50.00,
        gpu_whitelist=["RTX_4090", "A100"],
        reliability_min=0.95,
        auto_destroy_hours=12,
    ),
    "production": VastEnvConfig(
        name="production",
        api_key=os.environ.get("VASTAI_PROD_KEY", ""),
        max_dph=4.00,
        max_instances=16,
        max_daily_spend=500.00,
        gpu_whitelist=["A100", "H100_SXM"],
        reliability_min=0.98,
        auto_destroy_hours=48,
    ),
}

def get_config(env=None):
    env = env or os.environ.get("VASTAI_ENV", "development")
    return ENVIRONMENTS[env]

Step 2: Environment-Aware Client

class EnvAwareVastClient:
    def __init__(self, env="development"):
        self.config = get_config(env)
        self.client = VastClient(api_key=self.config.api_key)

    def search_offers(self, **overrides):
        query = {
            "rentable": {"eq": True},
            "reliability2": {"gte": self.config.reliability_min},
            "dph_total": {"lte": overrides.get("max_dph", self.config.max_dph)},
        }
        gpu = overrides.get("gpu_name", self.config.gpu_whitelist[0])
        query["gpu_name"] = {"eq": gpu}
        return self.client.search_offers(query)

    def create_instance(self, offer_id, image, disk_gb=20):
        # Enforce instance limit
        current = len([i for i in self.client.show_instances()
                      if i.get("actual_status") == "running"])
        if current >= self.config.max_instances:
            raise RuntimeError(
                f"{self.config.name}: Instance limit reached ({current}/{self.config.max_instances})")
        return self.client.create_instance(offer_id, image, disk_gb)

Step 3: Environment Variables

# .env.development
VASTAI_ENV=development
VASTAI_DEV_KEY=dev-api-key-here

# .env.staging
VASTAI_ENV=staging
VASTAI_STAGING_KEY=staging-api-key-here

# .env.production (in secrets manager, never in files)
VASTAI_ENV=production
VASTAI_PROD_KEY=prod-api-key-here

Step 4: Docker Image Tagging by Environment

# Dev: use latest for quick iteration
docker tag training:latest ghcr.io/org/training:dev

# Staging: use specific commit hash
docker tag training:latest ghcr.io/org/training:stg-$(git rev-parse --short HEAD)

# Production: use semantic version
docker tag training:latest ghcr.io/org/training:v1.2.3

Output

• Environment-specific configuration (dev, staging, production)
• Instance limits and budget caps per environment
• GPU whitelist enforcement
• Docker image tagging strategy
• Environment-aware client wrapper

Error Handling

Resources

• Vast.ai CLI
• REST API

Next Steps

For observability and monitoring, see vastai-observability.

Examples

Dev workflow: VASTAI_ENV=development python deploy.py --gpu RTX_4090 — enforces $0.25/hr max, 2 instance limit, auto-destroy after 2 hours.

Prod deployment: VASTAI_ENV=production python deploy.py --gpu H100_SXM --gpus 4 — allows up to 16 instances at $4/hr with 48-hour timeout.