gabrielmoreira 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
| Error | Cause | Solution |
|---|---|---|
| Wrong environment selected | VASTAI_ENV not set |
Default to development for safety |
| Instance limit exceeded | Too many concurrent instances | Destroy idle instances or increase limit |
| Daily budget exceeded | Expensive GPUs running too long | Implement auto-destroy timeout |
| Dev key used in prod | Environment variable misconfigured | Validate key matches expected account |
Resources
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.