System Requirements

This document outlines the hardware, software, and infrastructure requirements for deploying and running IRIS OCR in various environments.

Hardware Requirements

Minimum Requirements (Development)

Component	Specification	Notes
CPU	4 cores @ 2.0GHz	Intel i5/AMD Ryzen 5 equivalent
RAM	8GB	Minimum for basic development
Storage	20GB free space	Including models and datasets
Network	Broadband internet	For model downloads

Recommended Requirements (Production)

Component	Specification	Notes
CPU	8+ cores @ 2.4GHz	Intel i7/AMD Ryzen 7 or better
RAM	16GB+	32GB recommended for high throughput
Storage	50GB+ SSD	Fast storage for model loading
Network	Gigabit ethernet	For high-volume processing
GPU	NVIDIA GTX 1060+	Optional but significantly improves performance

Enterprise Requirements (High-Volume Production)

Component	Specification	Notes
CPU	16+ cores @ 2.8GHz	Server-grade processors recommended
RAM	64GB+	For concurrent processing
Storage	100GB+ NVMe SSD	Ultra-fast storage for ML models
Network	10Gbps+	For distributed deployments
GPU	NVIDIA RTX 3080+ or Tesla V100+	Dramatically improves ML processing

Software Requirements

Operating System Support

Linux (Recommended)

• Ubuntu 20.04 LTS or newer
• CentOS 8+ / RHEL 8+
• Debian 11+
• Amazon Linux 2

Windows

• Windows 10 Pro/Enterprise (with WSL2 for development)
• Windows Server 2019+

macOS

• macOS Big Sur (11.0)+ (Development only)
• Apple Silicon (M1/M2) supported with Docker

Container Runtime

Docker

• Docker Engine 20.10+
• Docker Compose 2.0+
• Docker Desktop (for Windows/macOS development)

Kubernetes (Production)

• Kubernetes 1.21+
• Helm 3.7+
• Ingress controller (NGINX, Traefik, etc.)

Python Runtime

Core Python

• Python 3.8 - 3.11 (3.9 recommended)
• pip 21.0+
• virtualenv or conda

Key Dependencies

# Core ML/AI libraries
torch>=1.12.0
torchvision>=0.13.0
paddlepaddle>=2.4.0
paddleocr>=2.6.0
opencv-python>=4.6.0
pillow>=9.0.0
numpy>=1.21.0
scikit-learn>=1.1.0

# API framework
fastapi>=0.85.0
uvicorn>=0.18.0
pydantic>=1.10.0

# Utilities
requests>=2.28.0
aiofiles>=0.8.0
python-multipart>=0.0.5

GPU Requirements (Optional but Recommended)

NVIDIA GPU Support

Minimum GPU Specifications

• Memory: 4GB VRAM minimum (8GB+ recommended)
• CUDA Compute Capability: 6.0+ (Pascal architecture or newer)
• Driver Version: 470+ (Linux), 472+ (Windows)

Supported GPU Models

GPU Family	Recommended Models	VRAM	Performance Gain
GTX 16 Series	GTX 1660, 1660 Ti	6GB	2-3x faster
RTX 20 Series	RTX 2060, 2070, 2080	6-8GB	3-4x faster
RTX 30 Series	RTX 3060, 3070, 3080	8-12GB	4-6x faster
RTX 40 Series	RTX 4060, 4070, 4080	8-16GB	5-8x faster
Tesla/A Series	V100, A100, A10	16-80GB	Enterprise-grade

CUDA Toolkit Requirements

# CUDA 11.2 - 11.8 recommended
nvidia-smi  # Check driver version
nvcc --version  # Check CUDA toolkit

Performance Comparison

Configuration	Processing Time per Document	Throughput (docs/hour)
CPU Only (8 cores)	8-12 seconds	300-450
GTX 1660 Ti	3-5 seconds	720-1200
RTX 3070	2-3 seconds	1200-1800
RTX 4080	1-2 seconds	1800-3600

Network Requirements

Bandwidth Requirements

Development Environment

• Download: 25 Mbps minimum (for model downloads)
• Upload: 5 Mbps (for testing with sample images)

Production Environment

• Internal Network: 1 Gbps+ between services
• External Access: 100 Mbps+ per concurrent user
• Content Delivery: CDN recommended for global deployments

Port Requirements

Service	Port	Protocol	Access
API Gateway	8000	HTTP/HTTPS	External
Image Processor	8001	HTTP	Internal
ML Embeddings	8002	HTTP	Internal
ML Classifier	8003	HTTP	Internal
OCR Extractor	8004	HTTP	Internal
Health Monitoring	9090	HTTP	Internal
Metrics (Prometheus)	9091	HTTP	Internal

Firewall Configuration

# Allow inbound traffic
sudo ufw allow 8000/tcp    # API Gateway
sudo ufw allow 22/tcp      # SSH (for management)
sudo ufw allow 443/tcp     # HTTPS (production)

# Block direct access to internal services
sudo ufw deny 8001:8004/tcp  # Internal services

# Allow internal network access (adjust CIDR as needed)
sudo ufw allow from 10.0.0.0/8 to any port 8001:8004

Storage Requirements

Disk Space Breakdown

Component	Development	Production	Notes
Base System	5GB	10GB	OS and core utilities
Docker Images	8GB	15GB	All service containers
ML Models	3GB	5GB	PaddleOCR and classification models
Training Data	2GB	10GB+	Sample images and datasets
Logs & Monitoring	1GB	5GB+	Application logs and metrics
User Data	1GB	Variable	Processed documents (if stored)
**Total Minimum	20GB	45GB+

Storage Performance

Recommended Storage Types

• Development: Standard SSD (500+ MB/s)
• Production: NVMe SSD (2000+ MB/s)
• Enterprise: NVMe RAID or distributed storage

I/O Requirements

• Random Read: 1000+ IOPS
• Sequential Read: 500+ MB/s
• Random Write: 500+ IOPS (for logging)

Database Requirements (Optional)

Metadata Storage

If using persistent storage for processing history and analytics:

SQLite (Development)

• File Size: 100MB - 1GB
• Concurrent Users: 1-5
• Performance: Basic analytics only

PostgreSQL (Recommended)

• Version: PostgreSQL 12+
• Memory: 2GB+ dedicated
• Storage: 10GB+ with regular backups
• Concurrent Connections: 100+

MongoDB (Document Storage)

• Version: MongoDB 5.0+
• Memory: 4GB+ dedicated
• Storage: 20GB+ for document metadata
• Replica Set: Recommended for production

Cloud Platform Requirements

Amazon Web Services (AWS)

EC2 Instance Types

Use Case	Instance Type	vCPUs	RAM	Storage	Cost/Month
Development	t3.large	2	8GB	30GB EBS	~$60
Production	c5.2xlarge	8	16GB	100GB EBS	~$300
GPU-Enabled	g4dn.xlarge	4	16GB	125GB SSD	~$400
High-Volume	c5.4xlarge	16	32GB	200GB EBS	~$600

Additional AWS Services

• ECS/EKS: Container orchestration
• ALB/NLB: Load balancing
• S3: Model and data storage
• CloudWatch: Monitoring and logging
• VPC: Network isolation

Google Cloud Platform (GCP)

Compute Engine Instance Types

Use Case	Machine Type	vCPUs	RAM	Storage	Cost/Month
Development	e2-standard-2	2	8GB	30GB SSD	~$50
Production	c2-standard-8	8	32GB	100GB SSD	~$350
GPU-Enabled	n1-standard-4 + T4	4	15GB	100GB SSD	~$450

Additional GCP Services

• GKE: Kubernetes management
• Cloud Load Balancing: Traffic distribution
• Cloud Storage: Object storage
• Cloud Monitoring: Observability
• VPC: Network management

Microsoft Azure

Virtual Machine Sizes

Use Case	VM Size	vCPUs	RAM	Storage	Cost/Month
Development	Standard_D2s_v3	2	8GB	30GB SSD	~$70
Production	Standard_D8s_v3	8	32GB	100GB SSD	~$400
GPU-Enabled	Standard_NC6	6	56GB	340GB SSD	~$900

Security Requirements

SSL/TLS Requirements

• TLS Version: 1.2 minimum (1.3 recommended)
• Certificate: Valid SSL certificate for production domains
• Cipher Suites: Modern cipher suites only
• HSTS: HTTP Strict Transport Security enabled

Authentication & Authorization

• API Keys: Secure API key management
• Rate Limiting: Request rate limiting per client
• Input Validation: Strict file type and size validation
• Network Security: Firewall and VPC configuration

Compliance (if applicable)

• GDPR: Data protection compliance for EU users
• HIPAA: Healthcare data compliance (if processing medical documents)
• SOC 2: Security controls for enterprise deployments

Monitoring & Observability Requirements

Metrics Collection

• Prometheus: Metrics aggregation
• Grafana: Visualization dashboards
• AlertManager: Alert routing and management

Logging

• Centralized Logging: ELK stack or cloud logging
• Log Retention: 30+ days minimum
• Log Analysis: Search and alerting capabilities

Health Checks

• Service Health: Individual service monitoring
• Dependency Checks: External service monitoring
• Performance Metrics: Response time and throughput tracking

Installation Verification

System Check Script

#!/bin/bash
# IRIS System Requirements Check

echo "=== IRIS OCR System Requirements Check ==="

# Check Python version
python_version=$(python3 --version 2>&1 | cut -d' ' -f2)
echo "Python version: $python_version"

# Check available memory
total_mem=$(free -h | awk '/^Mem:/ {print $2}')
echo "Total memory: $total_mem"

# Check available disk space
disk_space=$(df -h . | awk 'NR==2 {print $4}')
echo "Available disk space: $disk_space"

# Check Docker
if command -v docker &> /dev/null; then
    docker_version=$(docker --version | cut -d' ' -f3 | tr -d ',')
    echo "Docker version: $docker_version"
else
    echo "Docker: Not installed"
fi

# Check GPU (if available)
if command -v nvidia-smi &> /dev/null; then
    gpu_info=$(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader,nounits | head -1)
    echo "GPU: $gpu_info"
else
    echo "GPU: Not available or NVIDIA drivers not installed"
fi

# Check network connectivity
if ping -c 1 google.com &> /dev/null; then
    echo "Network: Connected"
else
    echo "Network: No internet connection"
fi

echo "=== End System Check ==="

Performance Benchmark

# Run performance test
python scripts/benchmark/system_performance.py

# Expected output for minimum requirements:
# CPU Performance: 1000+ operations/second
# Memory Performance: 5GB/s+ bandwidth
# Disk Performance: 100MB/s+ sequential read
# Network Performance: 25Mbps+ download

Meeting these requirements ensures optimal performance and reliability for IRIS OCR in your target environment. Adjust specifications based on your expected document processing volume and performance requirements.