LLM服务监控指标采集

LLM服务监控指标采集：从理论到实践

在大模型微服务化改造过程中，监控指标采集是保障系统稳定运行的关键环节。本文将围绕LLM服务的核心监控指标进行详细阐述，并提供可复现的采集方案。

核心监控指标

LLM服务主要关注以下几类指标：

性能指标：响应时间、吞吐量、错误率
资源指标：CPU使用率、内存占用、GPU利用率
业务指标：请求成功率、平均响应时间、并发请求数

采集方案实现

以Prometheus + Grafana为例，配置如下监控规则：

# prometheus.yml
scrape_configs:
  - job_name: 'llm-service'
    static_configs:
      - targets: ['localhost:8080']
    metrics_path: '/metrics'
    scrape_interval: 15s

# 监控采集代码示例
from prometheus_client import Gauge, Histogram
from flask import Flask, request
import time

app = Flask(__name__)
request_count = Gauge('llm_requests_total', 'Total requests')
request_duration = Histogram('llm_request_duration_seconds', 'Request duration')

@app.route('/predict', methods=['POST'])
def predict():
    start_time = time.time()
    request_count.inc()
    try:
        # 处理逻辑
        result = process_request(request.json)
        duration = time.time() - start_time
        request_duration.observe(duration)
        return {'result': result}
    except Exception as e:
        duration = time.time() - start_time
        request_duration.observe(duration)
        return {'error': str(e)}, 500