Deployments

Understanding deployments in Pylee and how to deploy your MCP servers

Deployments

Deployments are the mechanism by which your MCP servers are made available and accessible in Pylee. They represent running instances of your server versions that can be connected to and used by AI models.

What are Deployments?

Deployments in Pylee are live, running instances of your MCP servers that:

• Execute Server Code: Run your server version’s code and configuration
• Handle Connections: Accept connections from AI models and clients
• Manage Resources: Allocate and manage computational resources
• Provide Endpoints: Expose your server’s tools and resources
• Monitor Health: Track server performance and availability

Deployment Types

Development Deployments

Used for testing and development:

• Local Testing: Run servers locally for development
• Staging Environment: Test in production-like conditions
• Feature Testing: Validate new features before release
• Integration Testing: Test server integrations

Production Deployments

Used for live, production workloads:

• High Availability: Ensure server uptime and reliability
• Load Balancing: Distribute requests across instances
• Auto Scaling: Automatically scale based on demand
• Performance Monitoring: Track production metrics

Temporary Deployments

Short-lived deployments for specific purposes:

• Demo Deployments: Showcase server capabilities
• Testing Deployments: Validate specific scenarios
• Experiment Deployments: Test new configurations
• Backup Deployments: Emergency fallback instances

Creating Your First Deployment

Prerequisites

Before creating a deployment, ensure you have:

1. Server Version: A valid server version to deploy
2. Configuration: Proper server configuration including:
   • Remotes (if using HTTP endpoints)
   • Packages (if using external dependencies)
   • Variables (environment configuration)
   • Secrets (sensitive data)
3. Permissions: Deployment creation permissions in your organization

Step-by-Step Guide

1. Navigate to Server: Go to your server’s page in Pylee
2. Select Version: Choose the version you want to deploy
3. Create Deployment: Click “Create New Deployment”
4. Configure Environment: Set deployment-specific configuration
5. Review Settings: Verify all configuration is correct
6. Deploy: Start the deployment process
7. Monitor: Watch deployment status and health

Deployment Process

When you create a deployment, Pylee:

1. Validates Configuration: Checks all settings are correct
2. Provisions Resources: Allocates computational resources
3. Installs Dependencies: Downloads and installs required packages
4. Starts Server: Launches your server with the specified configuration
5. Performs Health Checks: Verifies server is running correctly
6. Exposes Endpoints: Makes server available for connections

Deployment Configuration

Environment Variables

Configure your deployment with environment-specific settings:

{
  "variables": {
    "API_BASE_URL": "https://api.production.com",
    "LOG_LEVEL": "warn",
    "MAX_CONNECTIONS": "100",
    "TIMEOUT": "30000"
  }
}

Resource Allocation

Specify computational resources for your deployment:

{
  "resources": {
    "cpu": "1000m",
    "memory": "512Mi",
    "storage": "1Gi"
  }
}

Network Configuration

Configure network settings:

{
  "network": {
    "ports": [8080, 8443],
    "protocols": ["http", "https"],
    "loadBalancer": true
  }
}

Scaling Configuration

Set up auto-scaling parameters:

{
  "scaling": {
    "minReplicas": 1,
    "maxReplicas": 10,
    "targetCPU": 80,
    "targetMemory": 70
  }
}

Deployment Lifecycle

1. Provisioning Phase

• Resource Allocation: Computational resources are allocated
• Environment Setup: Runtime environment is prepared
• Dependency Installation: Required packages are installed

2. Startup Phase

• Server Launch: Your server process is started
• Configuration Loading: Environment variables and secrets are loaded
• Service Registration: Server registers with Pylee infrastructure

3. Running Phase

• Request Handling: Server processes incoming requests
• Health Monitoring: Continuous health checks are performed
• Performance Tracking: Metrics are collected and analyzed

4. Scaling Phase

• Load Monitoring: Request load is continuously monitored
• Auto Scaling: Additional instances are created as needed
• Load Distribution: Traffic is distributed across instances

5. Termination Phase

• Graceful Shutdown: Server is gracefully stopped
• Resource Cleanup: Allocated resources are released
• Data Persistence: Any persistent data is saved

Deployment Management

Monitoring Deployments

Track deployment health and performance:

• Health Status: Monitor server health and availability
• Performance Metrics: Track response times and throughput
• Resource Usage: Monitor CPU, memory, and storage usage
• Error Rates: Track and analyze error patterns

Scaling Deployments

Manage deployment scaling:

• Manual Scaling: Manually increase or decrease instances
• Auto Scaling: Automatically scale based on metrics
• Scheduled Scaling: Scale based on time patterns
• Predictive Scaling: Scale based on predicted demand

Updating Deployments

Update running deployments:

• Rolling Updates: Gradually update instances
• Blue-Green Deployment: Switch between environments
• Canary Deployment: Test updates with subset of traffic
• Rollback: Revert to previous version if needed

Deployment Strategies

Rolling Deployment

Gradually replace old instances with new ones:

1. Start New Instance: Launch new version instance
2. Health Check: Verify new instance is healthy
3. Route Traffic: Gradually shift traffic to new instance
4. Terminate Old: Remove old instance when safe
5. Repeat: Continue until all instances are updated

Blue-Green Deployment

Maintain two identical environments:

1. Blue Environment: Current production deployment
2. Green Environment: New version deployment
3. Testing: Validate green environment thoroughly
4. Switch: Route traffic from blue to green
5. Rollback: Keep blue environment for quick rollback

Canary Deployment

Test new version with small subset of traffic:

1. Deploy Canary: Deploy new version to small subset
2. Monitor: Watch canary performance and errors
3. Validate: Ensure canary performs well
4. Gradual Rollout: Gradually increase canary traffic
5. Full Deployment: Complete rollout if successful

Deployment Security

Access Control

Control who can manage deployments:

• Deploy Permissions: Who can create deployments
• Manage Permissions: Who can update deployments
• View Permissions: Who can view deployment details

Network Security

Secure deployment network access:

• Firewall Rules: Control network access
• TLS/SSL: Encrypt data in transit
• VPN Access: Secure remote access
• IP Whitelisting: Restrict access by IP address

Data Security

Protect sensitive data in deployments:

• Secrets Management: Secure storage of sensitive data
• Encryption: Encrypt data at rest and in transit
• Access Logging: Log all data access
• Audit Trail: Track all deployment changes

Deployment Monitoring

Health Checks

Implement comprehensive health monitoring:

{
  "healthChecks": {
    "startup": {
      "path": "/health/startup",
      "timeout": "30s",
      "interval": "5s"
    },
    "liveness": {
      "path": "/health/live",
      "timeout": "5s",
      "interval": "30s"
    },
    "readiness": {
      "path": "/health/ready",
      "timeout": "5s",
      "interval": "10s"
    }
  }
}

Performance Metrics

Track key performance indicators:

• Response Time: Average and percentile response times
• Throughput: Requests per second
• Error Rate: Percentage of failed requests
• Availability: Uptime percentage
• Resource Utilization: CPU, memory, and storage usage

Alerting

Set up alerts for deployment issues:

• High Error Rate: Alert when error rate exceeds threshold
• Slow Response: Alert when response time is too high
• Resource Exhaustion: Alert when resources are running low
• Health Check Failure: Alert when health checks fail

Deployment Troubleshooting

Common Issues

Deployment Fails to Start

• Check server configuration
• Verify dependencies are available
• Review startup logs
• Validate environment variables

High Error Rate

• Review error logs
• Check external dependencies
• Validate configuration
• Monitor resource usage

Poor Performance

• Analyze performance metrics
• Check resource allocation
• Review code efficiency
• Optimize configuration

Connection Issues

• Verify network configuration
• Check firewall rules
• Validate endpoints
• Test connectivity

Debugging Steps

1. Check Logs: Review deployment and server logs
2. Monitor Metrics: Analyze performance metrics
3. Test Connectivity: Verify network connections
4. Validate Configuration: Check all settings
5. Resource Analysis: Monitor resource usage
6. External Dependencies: Test external services

Best Practices

Deployment Planning

• Environment Parity: Keep environments similar
• Configuration Management: Use consistent configuration
• Dependency Management: Pin dependency versions
• Resource Planning: Allocate appropriate resources

Monitoring Strategy

• Comprehensive Monitoring: Monitor all aspects
• Proactive Alerting: Set up preventive alerts
• Performance Baselines: Establish performance baselines
• Regular Reviews: Regularly review metrics

Security Practices

• Principle of Least Privilege: Minimal required permissions
• Regular Updates: Keep dependencies updated
• Security Scanning: Regularly scan for vulnerabilities
• Incident Response: Have incident response plan

Performance Optimization

• Resource Optimization: Right-size resources
• Caching Strategy: Implement appropriate caching
• Connection Pooling: Use connection pooling
• Load Testing: Regular load testing

Integration with CI/CD

Automated Deployment

Integrate deployment with CI/CD:

name: Deploy to Production
on:
  push:
    tags:
      - 'v*'

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - name: Deploy to Pylee
        run: |
          pylee server deploy \
            --server=${{ env.SERVER_NAME }} \
            --version=${{ github.ref_name }} \
            --environment=production

Deployment Pipeline

Create comprehensive deployment pipeline:

1. Build: Build server artifacts
2. Test: Run automated tests
3. Deploy Staging: Deploy to staging environment
4. Integration Test: Run integration tests
5. Deploy Production: Deploy to production
6. Monitor: Monitor deployment health

Next Steps

• Learn about Server Versions to understand what gets deployed
• Explore Variables for environment configuration
• Check Secrets for secure data management
• Review Servers for server management basics
• Visit Troubleshooting for common issues and solutions