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]
