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Troubleshooting.md

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Troubleshooting Guide

Table of Contents

Overview

This document provides a comprehensive overview of LlamaHome's troubleshooting guide, including common issues, diagnostic tools, and integration troubleshooting.

Common Issues

1. Out of Memory Errors

problem: "CUDA out of memory"
solutions:
  - "Reduce batch size in config.yaml"
  - "Enable gradient checkpointing"
  - "Use model quantization"
  - "Implement memory efficient attention"

2. Training Issues

problem: "Loss not converging"
solutions:
  - "Check learning rate settings"
  - "Verify data preprocessing"
  - "Monitor gradient norms"
  - "Adjust model architecture"

3. Performance Issues

problem: "Slow inference speed"
solutions:
  - "Enable batch processing"
  - "Optimize model loading"
  - "Use appropriate hardware"
  - "Configure proper cache sizes"

4. Integration Issues

problem: "API connection failures"
solutions:
  - "Verify network settings"
  - "Check authentication"
  - "Monitor request timeouts"
  - "Validate API endpoints"

Diagnostic Tools

System Checks

# Check CUDA availability
python -c "import torch; print(torch.cuda.is_available())"

# Monitor GPU usage
nvidia-smi -l 1

# Check memory usage
free -h

Log Analysis

# Check training logs
tail -f logs/training.log

# Monitor error logs
grep ERROR logs/error.log

Integration Troubleshooting

API Integration Issues

from llamahome.api import APIClient
from llamahome.monitoring import APIMonitor

async def diagnose_api():
    monitor = APIMonitor()
    client = APIClient(monitor=monitor)
    
    try:
        await client.health_check()
    except Exception as e:
        diagnostics = await monitor.get_diagnostics()
        print(f"API Issue: {e}")
        print(f"Diagnostics: {diagnostics}")

Cache Integration Issues

from llamahome.cache import CacheManager
from llamahome.monitoring import CacheMonitor

async def diagnose_cache():
    monitor = CacheMonitor()
    cache = CacheManager(monitor=monitor)
    
    metrics = await monitor.get_metrics()
    if metrics["hit_rate"] < 0.5:
        print("Low cache hit rate detected")
        print(await monitor.get_cache_recommendations())

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