求助： Python flask 应用内存一直在增加

2 个版本，都没有解决内存持续增加，由于内存一直增加，最后因为小鸡内存不足而被杀掉。

这是一个加载训练后的模型，通过网络传入预测参数，然后返回预测 json 结果。

可能同时发起（并发）几十个请求。

求大佬帮忙看看问题出在哪里，谢谢。

这是 chatgpt 4.0 给的版本

from flask import Flask, request, jsonify
import pickle
import os
import psutil
import pandas as pd

app = Flask(__name__)

class SingletonModel:
    _instance = None

    def __new__(cls):
        if cls._instance is None:
            print("Creating Singleton Instance")
            cls._instance = super(SingletonModel, cls).__new__(cls)
            modelName = "xgboost_model-k.pkl"
            with open(modelName, "rb") as pkl_file:
                loaded_data = pickle.load(pkl_file)
            cls._instance.model = loaded_data['model']
            cls._instance.scaler = loaded_data['scaler']
            cls._instance.label_encoder = loaded_data['label_encoder']
            cls._instance.feature_names = ['shortAvg','longAvg','volatility','diff']
        return cls._instance


resources = SingletonModel()
model = resources.model
scaler = resources.scaler
label_encoder = resources.label_encoder

@app.route('/predict', methods=['POST'])
def predict():
    global model, scaler, label_encoder

    data = request.json['input']

    df = pd.DataFrame([data], columns=resources.feature_names)

    scaled_data = scaler.transform(df)

    prediction = model.predict(scaled_data)

    label_prediction = label_encoder.inverse_transform(prediction)

    return jsonify([label_prediction[0]])

if __name__ == '__main__':
    app.run(port=6601,debug=True)

这是 Claude 给的版本

import asyncio
from flask import Flask, request, jsonify
import pickle
import pandas as pd

app = Flask(__name__)

# 模型相关全局变量
model = None
scaler = None 
label_encoder = None

async def load_model():

  global model, scaler, label_encoder,feature_names

  if not model:

    with open('xgboost_model-k.pkl', 'rb') as f:
      loaded_data = pickle.load(f)
      model = loaded_data['model']
      scaler = loaded_data['scaler'] 
      label_encoder = loaded_data['label_encoder']
      feature_names = ['shortAvg','longAvg','volatility','diff']

async def predict(data):

  await load_model()

  df = pd.DataFrame([data], columns=feature_names)

  scaled_data = scaler.transform(df)

  prediction = model.predict(scaled_data)

  label_prediction = label_encoder.inverse_transform(prediction)

  return label_prediction[0]


@app.route('/predict', methods=['POST'])
async def predict_handler():

  data = request.json['input']
  result = await asyncio.gather(predict(data))
  return jsonify(result)

if __name__ == '__main__':
    app.run(port=6601,debug=False)