optimization
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Alireza
214
vosk/test_files/monitor_performance.py
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214
vosk/test_files/monitor_performance.py
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#!/usr/bin/env python3
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"""
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Performance monitoring script for tracking CPU utilization during processing.
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Run this in a separate terminal while your main processing script is running.
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"""
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import psutil
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import time
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import matplotlib.pyplot as plt
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import numpy as np
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from datetime import datetime
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import threading
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import json
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import os
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class PerformanceMonitor:
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def __init__(self, log_file="performance_log.json"):
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self.log_file = log_file
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self.monitoring = False
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self.data = {
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'timestamps': [],
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'cpu_percent': [],
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'memory_percent': [],
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'cpu_count': [],
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'load_average': [],
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'network_io': [],
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'disk_io': []
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}
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def start_monitoring(self):
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"""Start monitoring in a separate thread"""
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self.monitoring = True
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self.monitor_thread = threading.Thread(target=self._monitor_loop)
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self.monitor_thread.daemon = True
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self.monitor_thread.start()
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print("🚀 Performance monitoring started...")
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def stop_monitoring(self):
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"""Stop monitoring"""
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self.monitoring = False
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if hasattr(self, 'monitor_thread'):
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self.monitor_thread.join()
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print("⏹️ Performance monitoring stopped.")
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def _monitor_loop(self):
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"""Main monitoring loop"""
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while self.monitoring:
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try:
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# CPU usage
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cpu_percent = psutil.cpu_percent(interval=1, percpu=True)
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cpu_avg = np.mean(cpu_percent)
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# Memory usage
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memory = psutil.virtual_memory()
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# Load average
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load_avg = psutil.getloadavg()
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# Network I/O
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net_io = psutil.net_io_counters()
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# Disk I/O
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disk_io = psutil.disk_io_counters()
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# Store data
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timestamp = datetime.now().isoformat()
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self.data['timestamps'].append(timestamp)
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self.data['cpu_percent'].append(cpu_percent)
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self.data['memory_percent'].append(memory.percent)
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self.data['cpu_count'].append(len(cpu_percent))
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self.data['load_average'].append(load_avg)
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self.data['network_io'].append({
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'bytes_sent': net_io.bytes_sent,
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'bytes_recv': net_io.bytes_recv
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})
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self.data['disk_io'].append({
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'read_bytes': disk_io.read_bytes,
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'write_bytes': disk_io.write_bytes
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})
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# Print current stats
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print(f"\r📊 CPU: {cpu_avg:.1f}% | Memory: {memory.percent:.1f}% | Load: {load_avg[0]:.2f}", end='')
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except Exception as e:
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print(f"\n❌ Monitoring error: {e}")
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def save_data(self):
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"""Save monitoring data to file"""
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with open(self.log_file, 'w') as f:
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json.dump(self.data, f, indent=2)
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print(f"\n💾 Performance data saved to {self.log_file}")
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def plot_performance(self):
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"""Create performance plots"""
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if not self.data['timestamps']:
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print("❌ No data to plot")
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return
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# Convert timestamps to relative time
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start_time = datetime.fromisoformat(self.data['timestamps'][0])
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relative_times = [(datetime.fromisoformat(ts) - start_time).total_seconds()
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for ts in self.data['timestamps']]
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# Create subplots
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fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 10))
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# CPU usage
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cpu_data = np.array(self.data['cpu_percent'])
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ax1.plot(relative_times, np.mean(cpu_data, axis=1), label='Average CPU %')
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ax1.fill_between(relative_times, np.min(cpu_data, axis=1), np.max(cpu_data, axis=1), alpha=0.3)
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ax1.set_title('CPU Utilization')
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ax1.set_ylabel('CPU %')
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ax1.grid(True)
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ax1.legend()
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# Memory usage
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ax2.plot(relative_times, self.data['memory_percent'], label='Memory %')
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ax2.set_title('Memory Utilization')
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ax2.set_ylabel('Memory %')
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ax2.grid(True)
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ax2.legend()
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# Load average
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load_data = np.array(self.data['load_average'])
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ax3.plot(relative_times, load_data[:, 0], label='1min')
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ax3.plot(relative_times, load_data[:, 1], label='5min')
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ax3.plot(relative_times, load_data[:, 2], label='15min')
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ax3.set_title('System Load Average')
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ax3.set_ylabel('Load')
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ax3.grid(True)
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ax3.legend()
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# Network I/O
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net_data = self.data['network_io']
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bytes_sent = [d['bytes_sent'] for d in net_data]
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bytes_recv = [d['bytes_recv'] for d in net_data]
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ax4.plot(relative_times, bytes_sent, label='Bytes Sent')
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ax4.plot(relative_times, bytes_recv, label='Bytes Received')
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ax4.set_title('Network I/O')
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ax4.set_ylabel('Bytes')
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ax4.grid(True)
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ax4.legend()
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plt.tight_layout()
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plt.savefig('performance_plot.png', dpi=300, bbox_inches='tight')
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print("📈 Performance plot saved as 'performance_plot.png'")
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def print_summary(self):
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"""Print performance summary"""
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if not self.data['timestamps']:
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print("❌ No data available")
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return
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cpu_data = np.array(self.data['cpu_percent'])
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memory_data = np.array(self.data['memory_percent'])
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print("\n" + "="*50)
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print("📊 PERFORMANCE SUMMARY")
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print("="*50)
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print(f"📈 Monitoring duration: {len(self.data['timestamps'])} samples")
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print(f"🖥️ CPU cores: {self.data['cpu_count'][0]}")
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print(f"⚡ Average CPU usage: {np.mean(cpu_data):.1f}%")
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print(f"🔥 Peak CPU usage: {np.max(cpu_data):.1f}%")
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print(f"💾 Average memory usage: {np.mean(memory_data):.1f}%")
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print(f"📊 Peak memory usage: {np.max(memory_data):.1f}%")
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# Calculate CPU utilization per core
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core_utilization = np.mean(cpu_data, axis=0)
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print(f"\n🔧 Per-core CPU utilization:")
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for i, util in enumerate(core_utilization):
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print(f" Core {i+1:2d}: {util:5.1f}%")
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# Calculate efficiency
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total_cpu_potential = len(core_utilization) * 100
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actual_cpu_usage = np.sum(core_utilization)
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efficiency = (actual_cpu_usage / total_cpu_potential) * 100
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print(f"\n🎯 CPU Efficiency: {efficiency:.1f}%")
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if efficiency < 50:
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print("⚠️ Low CPU utilization detected!")
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print("💡 Consider:")
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print(" - Increasing batch sizes")
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print(" - Using more concurrent processes")
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print(" - Optimizing I/O operations")
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elif efficiency > 90:
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print("✅ Excellent CPU utilization!")
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else:
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print("👍 Good CPU utilization")
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def main():
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"""Main function"""
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print("🔍 Performance Monitor for 192-core system")
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print("Press Ctrl+C to stop monitoring and generate report")
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monitor = PerformanceMonitor()
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try:
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monitor.start_monitoring()
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# Keep running until interrupted
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while True:
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time.sleep(1)
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except KeyboardInterrupt:
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print("\n\n⏹️ Stopping monitoring...")
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monitor.stop_monitoring()
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# Generate report
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monitor.save_data()
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monitor.plot_performance()
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monitor.print_summary()
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if __name__ == "__main__":
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main()
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