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import time
import psutil
import GPUtil
from typing import List, Dict, Any, Optional
import numpy as np
import logging
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
logger = logging.getLogger(__name__)
class SystemEvaluator:
def __init__(self):
self.monitoring = False
self.metrics = []
self.monitor_thread = None
def start_monitoring(self):
"""Start system monitoring"""
self.monitoring = True
self.metrics = []
self.monitor_thread = threading.Thread(target=self._monitor_system)
self.monitor_thread.start()
logger.info("Started system monitoring")
def stop_monitoring(self):
"""Stop system monitoring"""
self.monitoring = False
if self.monitor_thread:
self.monitor_thread.join()
logger.info("Stopped system monitoring")
def _monitor_system(self):
"""Monitor system resources"""
while self.monitoring:
try:
# CPU usage
cpu_percent = psutil.cpu_percent(interval=1)
# Memory usage
memory = psutil.virtual_memory()
memory_percent = memory.percent
memory_used_gb = memory.used / (1024**3)
# GPU usage (if available)
gpu_metrics = self._get_gpu_metrics()
# Disk usage
disk = psutil.disk_usage('/')
disk_percent = disk.percent
metric = {
'timestamp': time.time(),
'cpu_percent': cpu_percent,
'memory_percent': memory_percent,
'memory_used_gb': memory_used_gb,
'disk_percent': disk_percent,
**gpu_metrics
}
self.metrics.append(metric)
except Exception as e:
logger.error(f"Error monitoring system: {e}")
time.sleep(1) # Monitor every second
def _get_gpu_metrics(self) -> Dict[str, Any]:
"""Get GPU metrics"""
try:
gpus = GPUtil.getGPUs()
if gpus:
gpu = gpus[0] # Use first GPU
return {
'gpu_utilization': gpu.load * 100,
'gpu_memory_used': gpu.memoryUsed,
'gpu_memory_total': gpu.memoryTotal,
'gpu_memory_percent': (gpu.memoryUsed / gpu.memoryTotal) * 100,
'gpu_temperature': gpu.temperature
}
except:
pass
return {
'gpu_utilization': 0,
'gpu_memory_used': 0,
'gpu_memory_total': 0,
'gpu_memory_percent': 0,
'gpu_temperature': 0
}
def measure_throughput(self, func, args_list: List[tuple],
max_workers: int = 4) -> Dict[str, Any]:
"""Measure throughput of a function"""
start_time = time.time()
# Execute function with different concurrency levels
results = []
with ThreadPoolExecutor(max_workers=max_workers) as executor:
futures = [executor.submit(func, *args) for args in args_list]
for future in as_completed(futures):
try:
result = future.result()
results.append(result)
except Exception as e:
logger.error(f"Error in throughput measurement: {e}")
end_time = time.time()
total_time = end_time - start_time
throughput = len(results) / total_time # queries per second
return {
'total_queries': len(args_list),
'successful_queries': len(results),
'total_time': total_time,
'throughput_qps': throughput,
'avg_time_per_query': total_time / len(args_list) if args_list else 0
}
def measure_latency(self, func, args: tuple, num_runs: int = 10) -> Dict[str, Any]:
"""Measure latency of a function"""
latencies = []
for _ in range(num_runs):
start_time = time.time()
try:
result = func(*args)
end_time = time.time()
latency = end_time - start_time
latencies.append(latency)
except Exception as e:
logger.error(f"Error in latency measurement: {e}")
latencies.append(float('inf'))
# Remove infinite latencies
latencies = [l for l in latencies if l != float('inf')]
if not latencies:
return {
'avg_latency': 0,
'p50_latency': 0,
'p95_latency': 0,
'p99_latency': 0,
'min_latency': 0,
'max_latency': 0,
'std_latency': 0
}
latencies = np.array(latencies)
return {
'avg_latency': np.mean(latencies),
'p50_latency': np.percentile(latencies, 50),
'p95_latency': np.percentile(latencies, 95),
'p99_latency': np.percentile(latencies, 99),
'min_latency': np.min(latencies),
'max_latency': np.max(latencies),
'std_latency': np.std(latencies)
}
def measure_batch_latency(self, func, args_list: List[tuple],
batch_sizes: List[int] = [1, 4, 8, 16]) -> Dict[str, Any]:
"""Measure latency for different batch sizes"""
results = {}
for batch_size in batch_sizes:
batch_latencies = []
# Process in batches
for i in range(0, len(args_list), batch_size):
batch_args = args_list[i:i + batch_size]
start_time = time.time()
try:
batch_results = [func(*args) for args in batch_args]
end_time = time.time()
batch_latency = end_time - start_time
batch_latencies.append(batch_latency)
except Exception as e:
logger.error(f"Error in batch latency measurement: {e}")
if batch_latencies:
results[f'batch_size_{batch_size}'] = {
'avg_latency': np.mean(batch_latencies),
'p95_latency': np.percentile(batch_latencies, 95),
'throughput': batch_size / np.mean(batch_latencies)
}
return results
def get_system_stats(self) -> Dict[str, Any]:
"""Get current system statistics"""
if not self.metrics:
return {}
# Calculate statistics from monitoring data
cpu_values = [m['cpu_percent'] for m in self.metrics]
memory_values = [m['memory_percent'] for m in self.metrics]
gpu_values = [m.get('gpu_utilization', 0) for m in self.metrics]
return {
'monitoring_duration': len(self.metrics),
'cpu': {
'avg': np.mean(cpu_values),
'max': np.max(cpu_values),
'min': np.min(cpu_values),
'std': np.std(cpu_values)
},
'memory': {
'avg': np.mean(memory_values),
'max': np.max(memory_values),
'min': np.min(memory_values),
'std': np.std(memory_values)
},
'gpu': {
'avg': np.mean(gpu_values),
'max': np.max(gpu_values),
'min': np.min(gpu_values),
'std': np.std(gpu_values)
}
}
def evaluate_retrieval_performance(self, retriever, queries: List[str],
k: int = 10) -> Dict[str, Any]:
"""Evaluate retrieval performance"""
# Measure latency
latency_stats = self.measure_latency(
retriever.retrieve_single,
(queries[0], k),
num_runs=5
)
# Measure throughput
throughput_stats = self.measure_throughput(
retriever.retrieve_single,
[(query, k) for query in queries[:10]], # Limit for throughput test
max_workers=4
)
return {
'latency': latency_stats,
'throughput': throughput_stats
}
def evaluate_generation_performance(self, generator, questions: List[str],
passages_list: List[List[Dict[str, Any]]]) -> Dict[str, Any]:
"""Evaluate generation performance"""
# Measure latency
latency_stats = self.measure_latency(
generator.generate_with_strategy,
(questions[0], passages_list[0]),
num_runs=5
)
# Measure throughput
throughput_stats = self.measure_throughput(
generator.generate_with_strategy,
list(zip(questions[:5], passages_list[:5])), # Limit for throughput test
max_workers=2
)
return {
'latency': latency_stats,
'throughput': throughput_stats
}
def evaluate_end_to_end_performance(self, rag_system, queries: List[str]) -> Dict[str, Any]:
"""Evaluate end-to-end RAG performance"""
# Measure latency
latency_stats = self.measure_latency(
rag_system.query,
(queries[0],),
num_runs=5
)
# Measure throughput
throughput_stats = self.measure_throughput(
rag_system.query,
[(query,) for query in queries[:10]], # Limit for throughput test
max_workers=2
)
return {
'latency': latency_stats,
'throughput': throughput_stats
}
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