监控与日志

使用 FastAPI 在生产环境中部署 AI

Matt Eckerle

Software and Data Engineering Leader

为何进行监控与日志？

生产环境无法调试
进程管理器需要简单的健康检查
随时间记录关键指标

监控与日志软件

设置自定义日志

加载 uvicorn 错误记录器

在应用启动时添加自定义日志

在端点添加自定义日志

from fastapi import FastAPI
import logging

logger = logging.getLogger(
    'uvicorn.error'
)

app = FastAPI()
logger.info("App is running!")


@app.get('/')
async def main():
    logger.debug('GET /')
    return 'ok'

加载模型时记录日志

from fastapi import FastAPI
import logging
import joblib 

logger = logging.getLogger('uvicorn.error')


model = joblib.load('penguin_classifier.pkl')
logger.info("Penguin classifier loaded successfully.")

app = FastAPI()

用中间件记录处理时长

from fastapi import FastAPI, Request
import logging
import time
logger = logging.getLogger('uvicorn.error')
app = FastAPI()

@app.middleware("http")
async def log_process_time(request: Request, call_next):
    start_time = time.perf_counter()
    response = await call_next(request)
    process_time = time.perf_counter() - start_time
    logger.info(f"Process time was {process_time} seconds.")
    return response

¹ https://fastapi.tiangolo.com/tutorial/middleware/

设置日志级别

Python 日志级别

uvicorn main:app --log-level debug

监控

from fastapi import FastAPI

app = FastAPI()

@app.get("/health")
async def get_health():
    return {"status": "OK"}

"我没事！"

通过监控共享模型参数

from fastapi import FastAPI
import joblib

model = joblib.load(
    'penguin_classifier.pkl'
)
app = FastAPI()

@app.get("/health")
async def get_health():
    params = model.get_params()
    return {"status": "OK",
            "params": params}

"我没事！"
"这里有一些关于我的信息！"

开始练习！

使用 FastAPI 在生产环境中部署 AI