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Monitoring.md

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Monitoring System

Table of Contents

Overview

LlamaHome's monitoring system provides comprehensive tracking of system performance, resource usage, and model behavior. This document outlines the monitoring configuration, metrics collection, and visualization options.

Configuration

Core Monitoring Settings

monitoring:
  enabled: true
  update_frequency: 10  # seconds
  metrics_path: ".metrics"
  export_format: "prometheus"
  
  thresholds:
    memory:
      warning: 0.80  # 80% usage
      critical: 0.90 # 90% usage
    
    gpu:
      utilization_min: 0.70  # 70% minimum utilization
      memory_max: 0.95      # 95% maximum memory usage
    
    training:
      loss_plateau_patience: 3
      validation_loss_max: 2.0
      learning_rate_min: 1e-6
    
    inference:
      latency_max: 1000  # ms
      timeout: 30        # seconds
      batch_queue_max: 100

Alert Configuration

alerts:
  channels:
    slack:
      webhook_url: ${SLACK_WEBHOOK_URL}
      channel: "#llamahome-alerts"
    
    email:
      smtp_server: ${SMTP_SERVER}
      from_address: "[email protected]"
      to_addresses: ["[email protected]"]

Metrics Collection

System Metrics

class SystemMetricsCollector:
    """Collects system-level metrics."""
    
    async def collect(self) -> Dict[str, float]:
        return {
            "cpu_usage": self.get_cpu_usage(),
            "memory_usage": self.get_memory_usage(),
            "gpu_utilization": self.get_gpu_utilization(),
            "disk_usage": self.get_disk_usage(),
            "network_throughput": self.get_network_throughput()
        }

Model Metrics

class ModelMetricsCollector:
    """Collects model-specific metrics."""
    
    async def collect(self) -> Dict[str, float]:
        return {
            "inference_latency": self.get_inference_latency(),
            "batch_throughput": self.get_batch_throughput(),
            "cache_hit_rate": self.get_cache_hit_rate(),
            "token_throughput": self.get_token_throughput()
        }

Visualization

Dashboards

  1. System Dashboard

    • Resource utilization
    • Performance metrics
    • Cache statistics
    • Network usage

  2. Training Dashboard

    • Loss curves
    • Learning rates
    • Validation metrics
    • Resource usage

  3. Inference Dashboard

    • Latency metrics
    • Throughput graphs
    • Error rates
    • Cache performance

Alert System

Configuration

alerts:
  channels:
    slack:
      webhook_url: ${SLACK_WEBHOOK_URL}
      channel: "#llamahome-alerts"
    
    email:
      smtp_server: ${SMTP_SERVER}
      from_address: "[email protected]"
      to_addresses: ["[email protected]"]
      
  rules:
    memory_usage:
      condition: "memory_usage > 0.90"
      severity: "critical"
      channels: ["slack", "email"]
      
    gpu_utilization:
      condition: "gpu_utilization < 0.50"
      severity: "warning"
      channels: ["slack"]

Best Practices

  1. Resource Monitoring

    • Regular metric collection
    • Appropriate threshold settings
    • Proactive alert configuration
    • Historical data retention

  2. Performance Tracking

    • Monitor key metrics
    • Track trends over time
    • Set up alerting
    • Regular review cycles

  3. Alert Management

    • Configure appropriate thresholds
    • Use multiple notification channels
    • Document alert responses
    • Regular alert review

Integration Examples

Integrating with Training

from llamahome.training import TrainingPipeline
from llamahome.monitoring import TrainingMonitor

monitor = TrainingMonitor(config)
pipeline = TrainingPipeline(model, monitor=monitor)

async with monitor:
    await pipeline.train()
    metrics = await monitor.get_training_metrics()

Integrating with Inference

from llamahome.inference import InferencePipeline
from llamahome.monitoring import InferenceMonitor

monitor = InferenceMonitor(config)
pipeline = InferencePipeline(model, monitor=monitor)

async with monitor:
    response = await pipeline.generate("prompt")
    latency = await monitor.get_latency_metrics()

Custom Metric Collection

from llamahome.monitoring import MetricsCollector

class CustomMetrics(MetricsCollector):
    async def collect(self):
        return {
            "custom_metric": self.calculate(),
            "business_metric": self.get_business_value()
        }

Related Documentation