Skip to content

Deployment

Jump to bottom
AutoBotSolutions edited this page Apr 27, 2026 · 1 revision

Deployment Guide

This guide covers deploying the Cognitive Engine in various environments.

Table of Contents

Deployment Overview

The Cognitive Engine can be deployed in various configurations:

  • Local/Development: Single instance for development and testing
  • Docker: Containerized deployment for consistency
  • Cloud: Cloud-native deployment on AWS, GCP, or Azure
  • Distributed: Multi-instance deployment for high availability

System Requirements

Minimum Requirements

  • CPU: 2 cores
  • RAM: 4 GB
  • Storage: 10 GB
  • Python: 3.9+
  • OS: Linux, macOS, or Windows

Recommended Requirements

  • CPU: 4+ cores
  • RAM: 8+ GB
  • Storage: 20+ GB SSD
  • Python: 3.10+
  • OS: Linux (Ubuntu 20.04+ recommended)

Network Requirements

  • Outbound: HTTPS access to LLM APIs (OpenAI, Anthropic)
  • Inbound: Port 8000 (if dashboard enabled)
  • Bandwidth: 1+ Mbps for API calls

Local Deployment

Development Setup

# Clone repository
git clone <repository-url>
cd cognitive_engine

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your settings

# Run tests
python run.py test

# Start engine
python run.py

Production-Ready Local Setup

# Install system dependencies
sudo apt-get update
sudo apt-get install python3.10 python3.10-venv sqlite3

# Create dedicated user
sudo useradd -r -s /bin/false cognitive_engine

# Set up directory structure
sudo mkdir -p /opt/cognitive_engine
sudo chown cognitive_engine:cognitive_engine /opt/cognitive_engine

# Install application
cd /opt/cognitive_engine
sudo -u cognitive_engine python3.10 -m venv venv
sudo -u cognitive_engine venv/bin/pip install -r requirements.txt

# Configure systemd service
sudo tee /etc/systemd/system/cognitive-engine.service > /dev/null <<EOF
[Unit]
Description=Cognitive Engine Service
After=network.target

[Service]
Type=simple
User=cognitive_engine
WorkingDirectory=/opt/cognitive_engine
Environment="PATH=/opt/cognitive_engine/venv/bin"
ExecStart=/opt/cognitive_engine/venv/bin/python run.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

# Enable and start service
sudo systemctl enable cognitive-engine
sudo systemctl start cognitive-engine

Docker Deployment

Dockerfile

Create a Dockerfile in the project root:

FROM python:3.10-slim

# Set working directory
WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    gcc \
    && rm -rf /var/lib/apt/lists/*

# Copy requirements first for caching
COPY requirements.txt .

# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Create non-root user
RUN useradd -m -u 1000 cognitive_engine && \
    chown -R cognitive_engine:cognitive_engine /app
USER cognitive_engine

# Expose dashboard port
EXPOSE 8000

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD python -c "import requests; requests.get('http://localhost:8000/health')" || exit 1

# Run application
CMD ["python", "run.py"]

docker-compose.yml

Create a docker-compose.yml for easy deployment:

version: '3.8'

services:
  cognitive-engine:
    build: .
    container_name: cognitive-engine
    restart: unless-stopped
    ports:
      - "8000:8000"
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
      - DEFAULT_LLM_PROVIDER=${DEFAULT_LLM_PROVIDER:-openai}
      - MIN_ITERATIONS=${MIN_ITERATIONS:-3}
      - MAX_ITERATIONS=${MAX_ITERATIONS:-50}
      - ENABLE_DASHBOARD=${ENABLE_DASHBOARD:-true}
      - DASHBOARD_PORT=${DASHBOARD_PORT:-8000}
      - LOG_LEVEL=${LOG_LEVEL:-INFO}
    volumes:
      - ./data:/app/data
      - ./cognitive_engine.db:/app/cognitive_engine.db
      - ./cognitive_engine.log:/app/cognitive_engine.log
    networks:
      - cognitive-network
    healthcheck:
      test: ["CMD", "python", "-c", "import requests; requests.get('http://localhost:8000/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 40s

networks:
  cognitive-network:
    driver: bridge

Building and Running

# Build image
docker build -t cognitive-engine:latest .

# Run with docker-compose
docker-compose up -d

# Run standalone
docker run -d \
  --name cognitive-engine \
  -p 8000:8000 \
  -e OPENAI_API_KEY=your_key \
  -e ANTHROPIC_API_KEY=your_key \
  -v $(pwd)/data:/app/data \
  cognitive-engine:latest

# View logs
docker logs -f cognitive-engine

# Stop container
docker stop cognitive-engine
docker rm cognitive-engine

Cloud Deployment

AWS Deployment

Using EC2

  1. Launch EC2 Instance

    • AMI: Ubuntu 20.04 LTS
    • Instance Type: t3.medium (2 vCPU, 4 GB RAM)
    • Storage: 20 GB GP2

  2. Security Groups

    • Inbound: SSH (22), HTTP (80), HTTPS (443), Custom (8000)
    • Outbound: All traffic

  3. Deploy Application

# SSH into instance
ssh -i your-key.pem ubuntu@your-instance-ip

# Update system
sudo apt-get update

# Install Docker
sudo apt-get install docker.io docker-compose

# Clone repository
git clone <repository-url>
cd cognitive_engine

# Configure environment
cp .env.example .env
nano .env  # Add your API keys

# Deploy
docker-compose up -d

Using ECS

  1. Create ECR Repository
aws ecr create-repository --repository-name cognitive-engine
  1. Build and Push Image
# Login to ECR
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <account-id>.dkr.ecr.us-east-1.amazonaws.com

# Build and tag
docker build -t cognitive-engine .
docker tag cognitive-engine:latest <account-id>.dkr.ecr.us-east-1.amazonaws.com/cognitive-engine:latest

# Push
docker push <account-id>.dkr.ecr.us-east-1.amazonaws.com/cognitive-engine:latest
  1. Create Task Definition
{
  "family": "cognitive-engine",
  "networkMode": "awsvpc",
  "requiresCompatibilities": ["FARGATE"],
  "cpu": "512",
  "memory": "1024",
  "containerDefinitions": [
    {
      "name": "cognitive-engine",
      "image": "<account-id>.dkr.ecr.us-east-1.amazonaws.com/cognitive-engine:latest",
      "portMappings": [
        {
          "containerPort": 8000,
          "protocol": "tcp"
        }
      ],
      "environment": [
        {
          "name": "OPENAI_API_KEY",
          "value": "your-key"
        },
        {
          "name": "ANTHROPIC_API_KEY",
          "value": "your-key"
        }
      ],
      "logConfiguration": {
        "logDriver": "awslogs",
        "options": {
          "awslogs-group": "/ecs/cognitive-engine",
          "awslogs-region": "us-east-1",
          "awslogs-stream-prefix": "ecs"
        }
      }
    }
  ]
}

Google Cloud Platform Deployment

Using Cloud Run

# Build and deploy
gcloud builds submit --tag gcr.io/PROJECT_ID/cognitive-engine

# Deploy to Cloud Run
gcloud run deploy cognitive-engine \
  --image gcr.io/PROJECT_ID/cognitive-engine \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated \
  --set-env-vars OPENAI_API_KEY=your_key,ANTHROPIC_API_KEY=your_key

Using GKE

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: cognitive-engine
spec:
  replicas: 2
  selector:
    matchLabels:
      app: cognitive-engine
  template:
    metadata:
      labels:
        app: cognitive-engine
    spec:
      containers:
      - name: cognitive-engine
        image: gcr.io/PROJECT_ID/cognitive-engine:latest
        ports:
        - containerPort: 8000
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-keys
              key: openai-key
        - name: ANTHROPIC_API_KEY
          valueFrom:
            secretKeyRef:
              name: api-keys
              key: anthropic-key
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
# Apply deployment
kubectl apply -f k8s/deployment.yaml

# Create service
kubectl expose deployment cognitive-engine --type=LoadBalancer --port 8000

Azure Deployment

Using Azure Container Instances

# Create resource group
az group create --name cognitive-engine-rg --location eastus

# Create container instance
az container create \
  --resource-group cognitive-engine-rg \
  --name cognitive-engine \
  --image cognitive-engine:latest \
  --cpu 2 \
  --memory 4 \
  --ports 8000 \
  --environment-variables \
    OPENAI_API_KEY=your_key \
    ANTHROPIC_API_KEY=your_key

Production Checklist

Pre-Deployment

  • All tests passing
  • Code reviewed and approved
  • Documentation updated
  • Security audit completed
  • Performance benchmarks met
  • Backup strategy in place
  • Monitoring configured
  • Alerting configured
  • Rollback plan documented

Configuration

  • Environment variables set correctly
  • API keys secured (use secrets manager)
  • Log level set to INFO or WARNING
  • Debug features disabled
  • Dashboard disabled or secured
  • Memory limits configured
  • Iteration limits appropriate
  • Database backup configured

Security

  • HTTPS enabled
  • Firewall rules configured
  • API keys not in code
  • Secrets manager configured
  • Access controls in place
  • Authentication configured (if needed)
  • Rate limiting configured
  • Input validation enabled

Performance

  • Load testing completed
  • Caching configured
  • Database indexed
  • Connection pooling configured
  • CDN configured (if applicable)
  • Autoscaling configured (if applicable)
  • Monitoring dashboards set up
  • Performance baselines established

Reliability

  • Health checks configured
  • Auto-restart configured
  • Load balancing configured
  • High availability setup
  • Disaster recovery plan
  • Backup schedule configured
  • Monitoring alerts configured
  • On-call rotation established

Monitoring and Maintenance

Health Checks

# Add to dashboard/server.py
@app.get("/health")
async def health_check():
    """Health check endpoint."""
    return {
        "status": "healthy",
        "timestamp": datetime.now().isoformat(),
        "version": "1.0.0"
    }

Metrics to Monitor

  • System Metrics

    • CPU usage
    • Memory usage
    • Disk usage
    • Network I/O

  • Application Metrics

    • Request rate
    • Response time
    • Error rate
    • Active connections

  • Cognitive Metrics

    • Average iterations per query
    • Average confidence scores
    • Memory growth rate
    • Learning effectiveness

Logging

Configure centralized logging:

# Use structured logging
import json
import logging

class JsonFormatter(logging.Formatter):
    def format(self, record):
        log_obj = {
            "timestamp": self.formatTime(record),
            "level": record.levelname,
            "message": record.getMessage(),
            "module": record.name,
            "line": record.lineno
        }
        if hasattr(record, 'extra'):
            log_obj.update(record.extra)
        return json.dumps(log_obj)

Alerting

Set up alerts for:

  • High error rate (> 5%)
  • High response time (> 10s)
  • High memory usage (> 80%)
  • High CPU usage (> 90%)
  • Disk space low (< 10%)
  • API key quota exceeded
  • Database connection failures

Scaling Strategies

Horizontal Scaling

Run multiple instances behind a load balancer:

# docker-compose.yml for scaling
version: '3.8'

services:
  cognitive-engine:
    image: cognitive-engine:latest
    deploy:
      replicas: 3
    environment:
      - ENABLE_DASHBOARD=false  # Disable dashboard on workers

Vertical Scaling

Increase resources for single instance:

services:
  cognitive-engine:
    deploy:
      resources:
        limits:
          cpus: '4'
          memory: 8G
        reservations:
          cpus: '2'
          memory: 4G

Database Scaling

For high-volume deployments:

  • Use PostgreSQL instead of SQLite
  • Implement read replicas
  • Use connection pooling
  • Cache frequently accessed data

Caching Strategy

Implement caching for:

  • LLM API responses
  • Thought graph queries
  • Memory lookups
  • Pattern extraction results
# Example Redis caching
import redis

redis_client = redis.Redis(host='localhost', port=6379, db=0)

def cached_llm_call(prompt):
    cache_key = f"llm:{hash(prompt)}"
    cached = redis_client.get(cache_key)
    if cached:
        return cached.decode('utf-8')
    
    result = llm_client.generate(prompt)
    redis_client.setex(cache_key, 3600, result)
    return result

Backup and Recovery

Database Backup

# Automated backup script
#!/bin/bash
BACKUP_DIR="/backups/cognitive-engine"
DATE=$(date +%Y%m%d_%H%M%S)
DB_PATH="/app/cognitive_engine.db"

mkdir -p $BACKUP_DIR

# Backup database
cp $DB_PATH $BACKUP_DIR/cognitive_engine_$DATE.db

# Compress
gzip $BACKUP_DIR/cognitive_engine_$DATE.db

# Keep last 7 days
find $BACKUP_DIR -name "*.db.gz" -mtime +7 -delete

Configuration Backup

# Backup environment configuration
cp .env $BACKUP_DIR/.env_$DATE.backup

Recovery Procedure

  1. Stop the service
  2. Restore database from backup
  3. Restore configuration
  4. Verify integrity
  5. Start the service
  6. Run health checks

Disaster Recovery

  • Store backups in multiple locations
  • Test recovery procedures regularly
  • Document recovery steps
  • Maintain recovery time objectives (RTO)
  • Maintain recovery point objectives (RPO)

Security Considerations

API Key Management

  • Never commit API keys to version control
  • Use environment variables or secrets manager
  • Rotate keys regularly
  • Monitor key usage
  • Revoke compromised keys immediately

Network Security

  • Use HTTPS for all communications
  • Implement rate limiting
  • Use firewall rules
  • Configure VPC/network segmentation
  • Use private networks where possible

Application Security

  • Validate all inputs
  • Sanitize outputs
  • Implement authentication (if needed)
  • Use parameterized queries
  • Keep dependencies updated
  • Run security scans

Data Security

  • Encrypt sensitive data at rest
  • Encrypt data in transit
  • Implement access controls
  • Audit data access
  • Comply with data regulations

Troubleshooting Deployment Issues

Container Won't Start

Symptoms: Container exits immediately or won't start

Solutions:

  • Check logs: docker logs <container-name>
  • Verify environment variables
  • Check resource limits
  • Verify API keys are valid
  • Check network connectivity

High Memory Usage

Symptoms: Memory usage grows continuously

Solutions:

  • Check memory limits in configuration
  • Implement memory cleanup
  • Check for memory leaks
  • Reduce MAX_MEMORY_SIZE
  • Restart service periodically

Slow Performance

Symptoms: Slow response times

Solutions:

  • Check system resources
  • Implement caching
  • Optimize database queries
  • Reduce iteration limits
  • Use faster LLM provider

API Rate Limits

Symptoms: API calls failing with rate limit errors

Solutions:

  • Implement exponential backoff
  • Cache responses
  • Use multiple API keys
  • Reduce request frequency
  • Upgrade API plan

Support

For deployment issues:

  • Email: autobotsolution@gmail.com
  • Address: Flushing MI
  • Check logs: cognitive_engine.log
  • Review this documentation
  • Check GitHub issues

Clone this wiki locally