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OPERATIONS.md

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Operations Guide

Version: 1.4.0-alpha
Last Updated: January 2026
Purpose: Master index for production operations documentation

Overview

This operations guide provides comprehensive documentation for deploying, managing, and troubleshooting ThemisDB in production environments with GPU acceleration.

Target Audience

  • DevOps Engineers: Deployment and infrastructure management
  • Site Reliability Engineers: Monitoring, incident response, and reliability
  • Security Engineers: Security hardening and compliance
  • System Administrators: Day-to-day operations and maintenance
  • ML Engineers: Training and inference workload optimization

Quick Links

Getting Started

Auto-Scaling & High Availability

Day-to-Day Operations

Operational Excellence

Security & Compliance

Performance

Disaster Recovery

Documentation Structure

docs/
├── OPERATIONS.md (this file)
└── production/
    ├── DEPLOYMENT.md                  # Installation and configuration
    ├── DISASTER_RECOVERY_PLAN.md      # Complete DR plan with RTO/RPO
    ├── LOAD_BALANCER_INTEGRATION.md   # Load balancer configuration
    ├── PERFORMANCE_TUNING.md          # Optimization guide
    ├── MONITORING.md                  # Observability setup
    ├── TROUBLESHOOTING.md             # Problem resolution
    ├── RUNBOOKS.md                    # Operational procedures
    ├── SECURITY.md                    # Security hardening
    ├── RUNBOOKS/
    │   ├── UPGRADE_RUNBOOK.md         # Upgrade procedures
    │   ├── RESTORE_RUNBOOK.md         # Backup restoration
    │   ├── FAILOVER_RUNBOOK.md        # Failover & recovery
    │   └── SCALING_RUNBOOK.md         # Scaling operations
    ├── CHECKLISTS/
    │   ├── pre_deployment.md          # Pre-deployment validation
    │   ├── post_deployment.md         # Post-deployment validation
    │   ├── incident_response.md       # Incident handling
    │   └── operational_compliance.md  # Monthly compliance check
    └── examples/
        ├── single_gpu_setup.yaml      # Single GPU configuration
        ├── multi_gpu_setup.yaml       # Multi-GPU configuration
        ├── distributed_training.yaml  # Distributed training
        └── raid_configuration.yaml    # High-availability storage

grafana/
└── dashboards/
    └── sla-monitoring.json            # SLA monitoring dashboard

prometheus/
└── alerts/
    └── sla-rules.yml                  # SLA alerting rules

helm/themisdb/
└── templates/
    ├── hpa.yaml                       # Horizontal Pod Autoscaler
    └── servicemonitor.yaml            # Prometheus ServiceMonitor

Deployment Scenarios

Single GPU Development

Use Case: Development, testing, small-scale inference

Documentation:

Hardware:

  • 1x RTX 3090/4090 or A100
  • 64GB RAM
  • 500GB NVMe SSD

Multi-GPU Production

Use Case: Training workloads, high-throughput inference

Documentation:

Hardware:

  • 4-8x A100 or H100 GPUs
  • 256GB+ RAM
  • 2TB+ NVMe RAID

Distributed Multi-Node

Use Case: Large-scale distributed training, enterprise deployments

Documentation:

Hardware:

  • Multiple nodes with 4-8 GPUs each
  • InfiniBand or 100 GbE networking
  • Shared storage (Lustre, BeeGFS)

Common Tasks

Initial Deployment

  1. Pre-Deployment

    • Complete Pre-Deployment Checklist
    • Verify hardware compatibility
    • Install GPU drivers and CUDA
    • Configure networking and storage

  2. Deployment

  3. Post-Deployment

Job Submission

Training Job:

# See detailed procedure in Runbooks
themisdb-cli job submit --config training-job.yaml

Inference Deployment:

# See detailed procedure in Runbooks
themisdb-cli inference deploy --model llama-2-7b

Checkpoint Management

# Save checkpoint
themisdb-cli checkpoint save --job-id <job-id>

# Restore from checkpoint
themisdb-cli job restore --checkpoint <checkpoint-id>

LoRA Adapter Deployment

# Deploy LoRA adapter
themisdb-cli lora deploy --adapter custom-adapter

Monitoring

View GPU Metrics:

SLA Monitoring:

  • SLA Dashboard - Track availability, latency, and error budgets
  • SLA Alerting Rules - Prometheus alerts for SLA breaches
  • Target: 99.9% availability, P95 < 200ms, < 0.1% error rate

Troubleshooting

GPU Issues:

Performance Issues:

Training Issues:

Incident Response

Quick Response

For production incidents:

  1. Assess Severity (P0-Critical, P1-High, P2-Medium, P3-Low)
  2. Follow Incident Response Checklist: incident_response.md
  3. Use Troubleshooting Guide: TROUBLESHOOTING.md
  4. Execute Emergency Procedures: RUNBOOKS.md#emergency-procedures

Common Incidents

Incident Response Guide
GPU Failure Troubleshooting - GPU Errors
Out of Memory Troubleshooting - OOM
Service Down Runbooks - Emergency
Security Breach Runbooks - Security Incident
Data Corruption Troubleshooting - Data Corruption

Maintenance

Regular Maintenance

Daily:

  • Monitor GPU health and utilization
  • Review logs for errors
  • Check disk space
  • Verify backups completed

Weekly:

  • Review performance metrics
  • Check for GPU driver updates
  • Review security logs
  • Update documentation

Monthly:

  • RAID scrub (if applicable)
  • Security patching
  • Performance tuning review
  • Disaster recovery test

See Runbooks - Maintenance Windows

Planned Maintenance

  1. Schedule maintenance window
  2. Follow Maintenance Procedures
  3. Complete Post-Deployment Checklist

Security

Security Checklist

  • TLS 1.3 configured
  • mTLS enabled for inter-node communication
  • Disk encryption enabled
  • Audit logging configured
  • GPU access controls configured
  • Key rotation automated
  • Security monitoring active

See Security Hardening Guide

Compliance

Supported Standards:

  • SOC 2
  • GDPR
  • HIPAA

See Security - Compliance

Performance Optimization

Quick Wins

  1. Enable Mixed Precision: 2-3x speedup

  2. Optimize Batch Size: Maximize GPU utilization

  3. Enable Gradient Checkpointing: 60-80% memory savings

  4. Use Flash Attention: 15-25% speedup, 30% memory reduction

Performance Targets

Metric Target
GPU Utilization (Training) >85%
Inference P95 Latency <100ms
Training Throughput >1000 samples/sec
GPU Memory Usage <90%
Uptime >99.9%

Support

Getting Help

  1. Check Documentation: Search this operations guide
  2. Review Logs: sudo journalctl -u themisdb -f
  3. Run Diagnostics: themisdb-cli debug dump
  4. Search Issues: https://github.com/makr-code/ThemisDB/issues
  5. Community Forum: https://github.com/makr-code/ThemisDB/discussions

Support Channels

  • GitHub Issues: Bug reports and feature requests
  • Discussions: Questions and community support
  • Documentation: This operations guide
  • Emergency: Follow on-call procedures

Creating Support Bundle

# Collect diagnostic information
themisdb-cli support-bundle --output /tmp/support-bundle.tar.gz

# Include in support request

Additional Resources

Related Documentation

External Resources

Changelog

Version 1.0 (January 2026)

  • Initial production operations documentation
  • Comprehensive deployment guides
  • Performance tuning guidelines
  • Security hardening procedures
  • Operational runbooks
  • Troubleshooting guides
  • Example configurations

Feedback

We welcome feedback on this documentation:

Document Version: 1.0
Last Updated: January 2026
Next Review: April 2026

Quick Reference:

# Health check
themisdb-cli health

# GPU status
nvidia-smi

# Submit job
themisdb-cli job submit --config job.yaml

# Monitor job
themisdb-cli job status <job-id>

# View logs
sudo journalctl -u themisdb -f

# Backup
themisdb-cli backup create --type full

# Emergency stop
sudo systemctl stop themisdb