为什么函数调用耗时 0.12 秒改成网络服务，耗时增加到 2s ，差距太大了，具体什么原因以及怎么优化呢？

我用了 flask 和 fastapi 部署都这中情况。为了 gpt 回答的不太行

server 端:

# -*- coding: utf-8 -*- import time import requests import numpy as np from loguru import logger from fastapi import FastAPI, Request from transformers import AutoTokenizer, AutoModel import torch import uvicorn app = FastAPI() # Load model and tokenizer # model_name = "Alibaba-NLP/gte-Qwen2-7B-instruct" model_name = 'DMetaSoul/Dmeta-embedding-zh' tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModel.from_pretrained(model_name, trust_remote_code=True).half().cuda() model.eval() # Model warm-up dummy_input = ["预热"] inputs = tokenizer(dummy_input, return_tensors="pt", padding=True, truncation=True).to("cuda") with torch.no_grad(): _ = model(**inputs) @app.post("/vectorize") async def vectorize(request: Request): data = await request.json() texts = data.get("texts", []) if not texts: return {"error": "No texts provided"} try: start = time.time() inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True).to("cuda") with torch.no_grad(): outputs = model(**inputs) vectors = outputs.last_hidden_state.mean(dim=1).cpu().numpy().tolist() logger.info(f'转化耗时: {time.time() - start:.2f} seconds') return {"vectors": vectors} except Exception as e: logger.error(f"Error occurred: {str(e)}") return {"error": str(e)} # Run FastAPI server if __name__ == "__main__": uvicorn.run(app, host="0.0.0.0", port=5000) 

客户端：

# -*- coding: utf-8 -*- import time import requests import numpy as np from loguru import logger class TextVectorizationClient: def __init__(self, api_url="http://localhost:5000"): self.api_url = api_url def vectorize(self, text): start_time = time.time() respOnse= requests.post(f"{self.api_url}/vectorize", json={"texts": [text]}) end_time = time.time() print('接口响应时间：', end_time - start_time) if response.status_code == 200: return np.array(response.json()["vectors"][0]) else: raise Exception(f"API request failed: {response.json().get('error', 'Unknown error')}") if __name__ == "__main__": client = TextVectorizationClient() text = """ 天津历史上名医辈出，中医和中西医结合的发展成就位居全国前列。和平区作为天津市中心城区的核心区域，是近代津沽名中医的汇聚地，也是中医药事业的奠基之地。天津市和平区中医医院开展了“寻访近代津沽名中医和平印迹活动”，旨在挖掘中医药文化的传承脉络，践行天津市中医药强市行动计划的“文化”要求。 """ start_time = time.time() vector = client.vectorize(text) logger.info(f"Vectorization completed in {time.time() - start_time:.2f} seconds.") 

#服务器 log: 2024-11-13 17:49:26.580 | INFO | __main__:vectorize:40 - 转化耗时: 0.12 seconds INFO: 127.0.0.1:52711 - "POST /vectorize HTTP/1.1" 200 OK INFO: 127.0.0.1:52739 - "POST /vectorize HTTP/1.1" 200 OK 2024-11-13 17:49:28.740 | INFO | __main__:vectorize:40 - 转化耗时: 0.12 seconds # 客户端 log： 接口响应时间：2.15596079826355 接口响应时间：2.1456186771392822