模型部署后性能监控方案设计

模型部署后性能监控方案设计

在大模型推理加速实践中，部署后的性能监控是确保系统稳定运行的关键环节。本文将介绍一套完整的性能监控方案，涵盖关键指标采集、阈值设置和告警机制。

核心监控指标

import time
import psutil
import torch
from datetime import datetime

class ModelMonitor:
    def __init__(self):
        self.metrics = {
            'cpu_utilization': [],
            'memory_usage': [],
            'inference_time': [],
            'throughput': []
        }
    
    def collect_metrics(self, model, input_data):
        # CPU利用率
        cpu_percent = psutil.cpu_percent(interval=1)
        
        # 内存使用率
        memory_info = psutil.virtual_memory()
        memory_usage = memory_info.percent
        
        # 推理时间
        start_time = time.time()
        with torch.no_grad():
            output = model(input_data)
        end_time = time.time()
        inference_time = (end_time - start_time) * 1000  # 转换为毫秒
        
        # 吞吐量计算
        throughput = 1000 / inference_time if inference_time > 0 else 0
        
        self.metrics['cpu_utilization'].append(cpu_percent)
        self.metrics['memory_usage'].append(memory_usage)
        self.metrics['inference_time'].append(inference_time)
        self.metrics['throughput'].append(throughput)
        
        return {
            'timestamp': datetime.now().isoformat(),
            'cpu_percent': cpu_percent,
            'memory_usage': memory_usage,
            'inference_time_ms': inference_time,
            'throughput_qps': throughput
        }

告警阈值设置

# 性能阈值配置
ALERT_THRESHOLDS = {
    'cpu_threshold': 85.0,      # CPU使用率超过85%告警
    'memory_threshold': 90.0,   # 内存使用率超过90%告警
    'latency_threshold': 100.0, # 平均延迟超过100ms告警
    'throughput_threshold': 5.0 # 吞吐量低于5 QPS告警
}

def check_alerts(metrics):
    alerts = []
    
    if metrics['cpu_percent'] > ALERT_THRESHOLDS['cpu_threshold']:
        alerts.append(f'CPU使用率过高: {metrics["cpu_percent"]}%')
    
    if metrics['memory_usage'] > ALERT_THRESHOLDS['memory_threshold']:
        alerts.append(f'内存使用率过高: {metrics["memory_usage"]}%')
        
    # 检查平均延迟
    avg_latency = sum([t for t in metrics['inference_time'][-10:]]) / len(metrics['inference_time'][-10:])
    if avg_latency > ALERT_THRESHOLDS['latency_threshold']:
        alerts.append(f'平均延迟过高: {avg_latency:.2f}ms')
        
    return alerts

实施建议

1. 数据采集频率：每秒采集一次性能指标
2. 历史数据存储：使用InfluxDB或Prometheus存储时间序列数据
3. 可视化展示：集成Grafana进行实时监控面板展示
4. 自动化告警：通过Webhook将告警发送到钉钉/企业微信

部署后应持续优化监控策略，结合实际业务场景调整阈值参数。