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Performance Optimization Guide

Table of Contents

Overview

This document provides a comprehensive guide to optimizing LlamaHome's performance, covering memory management, data processing, training optimizations, resource utilization, configuration, best practices, monitoring, and troubleshooting.

Memory Management

Training Optimizations

  1. Streaming Data Pipeline

    class StreamingDataset:
    """Memory-efficient dataset implementation."""
    
    def __init__(self, buffer_size: int = 1000):
        self.buffer_size = buffer_size
        self._buffer = []

    Key features:

    • Dynamic buffer management
    • Memory-aware streaming
    • Efficient disk I/O
    • Automatic cleanup

  2. Batch Processing

    class BatchProcessor:
    """Optimized batch processing."""
    
    async def process_batch(self, batch: Dict[str, torch.Tensor]):
        if self.config.dynamic_batch_size:
            batch = await self._adjust_batch_size(batch)

    Optimizations:

    • Dynamic batch sizing
    • Gradient accumulation
    • Memory monitoring
    • Cache optimization

Resource Management

  1. Memory Tracking

    class MemoryTracker:
    """Track and optimize memory usage."""
    
    def update(self):
        if torch.cuda.is_available():
            self.peak_memory = max(
                self.peak_memory,
                torch.cuda.memory_allocated()
            )

    Features:

    • Real-time monitoring
    • Peak usage tracking
    • Automatic optimization
    • Resource alerts

  2. Device Management

    class DeviceManager:
    """Manage compute devices."""
    
    def optimize_device_usage(self):
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            torch.cuda.memory.set_per_process_memory_fraction(0.95)

    Capabilities:

    • GPU memory optimization
    • CPU offloading
    • Mixed precision
    • Device synchronization

Data Processing

Loading Optimizations

  1. Efficient Loading

    async def load_data(self, path: Path) -> Dataset:
    return StreamingDataset(
        path,
        buffer_size=self.config.stream_buffer_size,
        memory_limit=self.config.memory_limit
    )

    Features:

    • Async loading
    • Memory limits
    • Streaming support
    • Format detection

  2. Preprocessing Pipeline

    class PreprocessingPipeline:
    """Efficient data preprocessing."""
    
    def preprocess_batch(self, batch: Dict):
        return self._apply_transforms(
            batch,
            num_workers=self.config.num_workers
        )

    Optimizations:

    • Parallel processing
    • Memory mapping
    • Caching
    • Format optimization

Cache Management

  1. Tiered Caching

    class CacheManager:
    """Multi-level cache system."""
    
    def __init__(self):
        self.memory_cache = MemoryCache()
        self.disk_cache = DiskCache()
        self.network_cache = NetworkCache()

    Levels:

    • Memory (fast, limited)
    • Disk (medium, local)
    • Network (slow, distributed)

  2. Cache Policies

    class CachePolicy:
    """Cache management policies."""
    
    def apply_policy(self, cache: Cache):
        if cache.memory_pressure > 0.8:
            cache.evict_least_used()

    Features:

    • LRU eviction
    • Size limits
    • TTL management
    • Priority levels

Training Optimization

Memory Efficiency

  1. Gradient Management

    class GradientOptimizer:
    """Optimize gradient handling."""
    
    def optimize_gradients(self):
        if self.config.gradient_checkpointing:
            self.model.gradient_checkpointing_enable()

    Features:

    • Checkpointing
    • Accumulation
    • Clipping
    • Scaling

  2. Model Optimization

    class ModelOptimizer:
    """Model memory optimization."""
    
    def optimize_model(self):
        if self.config.memory_efficient_attention:
            self.model.enable_memory_efficient_attention()

    Techniques:

    • Attention optimization
    • Parameter sharing
    • Quantization
    • Pruning

Resource Utilization

  1. Compute Optimization

    class ComputeOptimizer:
    """Optimize compute resources."""
    
    def optimize(self):
        self._optimize_threads()
        self._optimize_memory()
        self._optimize_io()

    Areas:

    • Thread management
    • Memory allocation
    • I/O scheduling
    • Cache utilization

  2. Monitoring System

    class PerformanceMonitor:
    """Monitor system performance."""
    
    def monitor(self):
        self._track_memory()
        self._track_compute()
        self._track_io()

    Metrics:

    • Memory usage
    • GPU utilization
    • I/O throughput
    • Cache hits

Configuration

Memory Settings

memory:
  # Memory limits
  max_gpu_memory: "90%"
  max_cpu_memory: "85%"
  
  # Cache settings
  cache_size: "10GB"
  cache_ttl: 3600
  
  # Buffer settings
  stream_buffer: 1000
  prefetch_factor: 2

Processing Settings

processing:
  # Batch settings
  batch_size: "auto"
  accumulation_steps: 4
  
  # Optimization
  mixed_precision: true
  gradient_checkpointing: true
  memory_efficient_attention: true
  
  # Resources
  num_workers: "auto"
  pin_memory: true

Best Practices

  1. Memory Management

    • Monitor memory usage
    • Use streaming for large datasets
    • Enable gradient checkpointing
    • Implement proper cleanup

  2. Data Processing

    • Use appropriate batch sizes
    • Enable prefetching
    • Implement caching
    • Optimize I/O operations

  3. Resource Utilization

    • Monitor GPU usage
    • Balance CPU/GPU workload
    • Optimize cache usage
    • Handle cleanup properly

  4. Error Handling

    • Monitor OOM errors
    • Implement fallbacks
    • Log memory issues
    • Handle cleanup

Monitoring

Memory Monitoring

class MemoryMonitor:
    """Monitor memory usage."""
    
    def monitor(self):
        stats = {
            "gpu_used": self._get_gpu_memory(),
            "cpu_used": self._get_cpu_memory(),
            "cache_size": self._get_cache_size()
        }
        self._log_stats(stats)

Performance Metrics

class MetricsCollector:
    """Collect performance metrics."""
    
    def collect(self):
        return {
            "memory_usage": self._get_memory_metrics(),
            "compute_usage": self._get_compute_metrics(),
            "io_stats": self._get_io_metrics()
        }

Performance Baselines

Training Metrics

training:
  single_gpu:
    batch_size: 32
    training_speed: "X samples/second"
    memory_usage: "Y GB"
    gpu_utilization: "Z%"
  
  distributed:
    gpus: 8
    global_batch_size: 256
    training_speed: "X samples/second"
    memory_per_gpu: "Y GB"
    communication_overhead: "X ms"

Model Metrics

model:
  inference:
    batch_1_latency: "X ms"
    batch_32_latency: "X ms"
    memory_usage: "Y GB"
  
  quality:
    validation_accuracy: "X%"
    convergence_epochs: "Y"
    validation_loss: "Z"

Warning Thresholds

class PerformanceAlertSystem:
    """Monitor and alert on performance metrics."""
    
    def __init__(self):
        self.warning_thresholds = {
            "memory_usage": 0.90,  # 90%
            "gpu_utilization_min": 0.70,  # 70%
            "training_speed_min": "X samples/second",
            "validation_loss_max": ("X", "Y")  # (value, epochs)
        }
        
        self.critical_thresholds = {
            "memory_usage": 0.95,  # 95%
            "training_speed_min": "X/2 samples/second",
            "validation_loss_max": ("2X", "Y")  # (value, epochs)
        }

Troubleshooting

  1. Memory Issues

    • Check memory usage
    • Adjust batch size
    • Enable optimizations
    • Clear cache

  2. Performance Issues

    • Monitor metrics
    • Check configuration
    • Optimize resources
    • Update settings

  3. Resource Issues

    • Balance workload
    • Adjust limits
    • Enable monitoring
    • Implement cleanup