使用 FastAPI 在生产环境中部署 AI
Matt Eckerle
Software and Data Engineering Leader
from fastapi import FastAPI
sentiment_model = None
def load_model(): global sentiment_model sentiment_model = SentimentAnalyzer("trained_model.joblib") print("Model loaded successfully")
load_model()
Model loaded successfully
from contextlib import asynccontextmanager
@asynccontextmanager async def lifespan(app: FastAPI): # Startup: Load the ML model load_model() yield
app = FastAPI(lifespan=lifespan)
@app.get("/health") def health_check(): if sentiment_model is not None: return {"status": "healthy", "model_loaded": True} return {"status": "unhealthy", "model_loaded": False}
Curl 命令:
curl -X GET \ "http://localhost:8080/health" \ -H "accept: application/json"
输出：
{ "status": "healthy", "model_loaded": true }
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