# Create a virtual environment
python -m venv ai_factory_env
source ai_factory_env/bin/activate  # On Windows: ai_factory_env\Scripts\activate

# Install essential libraries
# boto3 for AWS/Cloud interaction, pydantic for data validation, openai for LLM interaction
pip install boto3 pydantic openai python-dotenv requests

# Initialize project
mkdir ai-factory-node && cd ai-factory-node
npm init -y

# Install dependencies
# dotenv for env management, typescript for type safety, axios for API calls
npm install dotenv axios
npm install --save-dev typescript @types/node ts-node typescript
npx tsc --init

import os
import logging
from typing import Dict, Any
from pydantic import BaseModel, Field
from dotenv import load_dotenv

# Setup logging for production traceability
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

load_dotenv()

# Data Model for Compute Request
class ComputeResourceRequest(BaseModel):
    instance_type: str = Field(default="nvidia-h100-80gb")
    region: str = Field(default="us-east-1")
    priority: int = Field(default=1, ge=1, le=5)

class AIFactoryOrchestrator:
    """
    Simulates an orchestrator that interfaces with an AI Infrastructure 
    provider to allocate GPU compute.
    """
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.endpoint = "https://api.ai-factory-provider.com/v1"

    def provision_compute(self, request: ComputeResourceRequest) -> Dict[str, Any]:
        """
        Simulates provisioning an NVIDIA GPU instance.
        """
        try:
            logger.info(f"Requesting {request.instance_type} in {request.region}...")
            
            # In a real scenario, this would be a requests.post() to a Cloud API
            # Here we simulate a successful API response
            if not self.api_key:
                raise ValueError("Invalid API Key provided.")

            provisioned_data = {
                "status": "provisioning",
                "instance_id": "gpu-inst-992834",
                "gpu_type": request.instance_type,
                "ip_address": "10.0.4.12"
            }
            
            logger.info(f"Provisioning successful: {provisioned_data['instance_id']}")
            return provisioned_data

        except Exception as e:
            logger.error(f"Failed to provision compute: {str(e)}")
            raise

# --- Execution Block ---
if __name__ == "__main__":
    # Configuration
    API_KEY = os.getenv("AI_FACTORY_API_KEY", "sk-default-key")
    
    orchestrator = AIFactoryOrchestrator(api_key=API_KEY)
    
    # Define our compute needs (The "Investable Asset" allocation)
    my_request = ComputeResourceRequest(
        instance_type="nvidia-h100-80gb",
        region="us-west-2",
        priority=5
    )

    try:
        result = orchestrator.provision_compute(my_request)
        print(f"\n[SUCCESS] Compute Instance Ready: {result}")
    except Exception as err:
        print(f"\n[ERROR] Orchestration failed: {err}")

import dotenv from 'dotenv';
import axios from 'axios';

dotenv.config();

interface ComputeStatus {
    instanceId: string;
    gpuUtilization: number; // 0 - 100
    memoryUsage: number;    // in GB
    temperature: number;    // in Celsius
    status: 'running' | 'idle' | 'error';
}

class ComputeMonitor {
    private readonly apiBase = 'https://api.ai-factory-provider.com/v1';

    /**
     * Fetches real-time telemetry from the NVIDIA-accelerated instance
     */
    async getTelemetry(instanceId: string): Promise<ComputeStatus> {
        try {
            console.log(`Fetching telemetry for ${instanceId}...`);
            
            // Simulated API call to the infrastructure management layer
            // In production, use: await axios.get(`${this.apiBase}/telemetry/${instanceId}`, { headers:... });
            const mockResponse = {
                data: {
                    instanceId: instanceId,
                    gpuUtilization: Math.floor(Math.random() * 100),
                    memoryUsage: 64.5,
                    temperature: 68,
                    status: 'running' as const
                }
            };

            return mockResponse.data;
        } catch (error) {
            console.error(`Error fetching telemetry for ${instanceId}:`, error);
            throw new Error('Telemetry retrieval failed');
        }
    }
}

// --- Execution Block ---
async function main() {
    const monitor = new ComputeMonitor();
    
    try {
        const telemetry = await monitor.getTelemetry('gpu-inst-992834');
        
        console.log('--- Compute Asset Report ---');
        console.table(telemetry);

        if (telemetry.gpuUtilization > 90) {
            console.warn('⚠️ ALERT: High GPU utilization detected. Scaling required.');
        }
    } catch (err) {
        console.error('Critical monitoring failure:', err);
    }
}

main();

# Infrastructure Credentials
AI_FACTORY_API_KEY=your_super_secret_key_here
CLOUD_PROVIDER_ID=nvidia_cloud_service

# Resource Limits
MAX_GPU_INSTANCES=10
DEFAULT_REGION=us-east-1

# Logging Level
LOG_LEVEL=DEBUG

.env