Common Issues and Solutions

This guide covers frequently encountered issues in the Local AI Cyber Lab environment and their solutions.

AI Service Issues

Model Loading Failures

Symptoms

• Models fail to load
• Slow model initialization
• Out of memory errors

Solutions

1. Check GPU Memory Usage:

   nvidia-smi
   

2. Ensure sufficient GPU memory is available

3. Consider using smaller models or enabling model offloading

4. Verify Model Files:

   ls -l models/
   sha256sum models/your-model.bin
   

5. Confirm model files are complete and uncorrupted

6. Compare checksums with original sources

7. Check File Permissions:

   chmod 644 models/*
   chown -R user:group models/
   

API Connection Issues

Symptoms

• API timeouts
• Authentication failures
• Connection refused errors

Solutions

1. Verify Service Status:

   docker-compose ps
   docker logs ai-guardian
   

2. Check API Keys:

3. Verify key format and expiration
4. Ensure proper environment variable setup

5. Check rate limits

6. Network Connectivity:

   curl -v http://localhost:8000/health
   docker network ls
   

Security Component Issues

AI Guardian Service

Symptoms

• Failed security checks
• Blocked legitimate requests
• High latency in security validation

Solutions

1. Review Security Logs:

   tail -f logs/ai-guardian.log
   

2. Adjust Security Rules:

3. Review and update validation rules
4. Check for false positives

5. Tune rate limiting settings

6. Monitor Resource Usage:

   docker stats ai-guardian
   

Database Connection Issues

Symptoms

• Failed database operations
• Connection timeouts
• Data consistency errors

Solutions

1. Check Database Status:

   docker-compose ps supabase-db
   docker logs supabase-db
   

2. Verify Connection Settings:

3. Check database URL and credentials
4. Verify network connectivity
5. Review connection pool settings

Performance Issues

Slow Response Times

Symptoms

• High latency in API responses
• Slow model inference
• System resource exhaustion

Solutions

1. Monitor System Resources:

   htop
   nvidia-smi -l 1
   

2. Optimize Configuration:

3. Adjust worker counts
4. Enable caching

5. Configure model optimization settings

6. Check Logging Levels:

7. Reduce debug logging in production
8. Configure log rotation
9. Monitor log file sizes

Memory Management

Symptoms

• Out of memory errors
• System slowdown
• Container restarts

Solutions

1. Monitor Memory Usage:

   docker stats
   free -h
   

2. Adjust Resource Limits:

3. Update container memory limits
4. Configure swap space
5. Implement memory optimization strategies

Integration Issues

Service Communication

Symptoms

• Inter-service timeouts
• Failed service discovery
• Network connectivity issues

Solutions

1. Check Docker Network:

   docker network inspect local-ai-cyber-lab_default
   

2. Verify Service Discovery:

3. Check DNS resolution
4. Verify service names and ports

5. Review network policies

6. Test Connectivity:

   docker-compose exec service-name ping other-service
   

Recovery Procedures

System Recovery

1. Backup Current State:

   ./scripts/backup.sh
   

2. Stop Services:

   docker-compose down
   

3. Clear Problematic State:

   docker system prune
   

4. Restore from Backup:

   ./scripts/restore.sh backup_file
   

Emergency Procedures

1. Quick Service Restart:

   docker-compose restart service-name
   

2. Force Clean Restart:

   docker-compose down -v
   docker-compose up -d
   

3. Reset to Known Good State:

   git checkout main
   docker-compose pull
   docker-compose up -d
   

Getting Help

If you continue to experience issues:

1. Check the GitHub Issues
2. Join our Discord Community
3. Review the Documentation
4. Contact Support