poolApply.py

import joblib
import multiprocessing as mp
from joblib import load

#add the path of the file you wish to load
filename= './new_model.sav'
#your models weights are now stored in the variable loaded model
loaded_model = load("new_model.sav")

#define a function and pass your parameters 
def predict(A):
  y_test=A
  y_test=np.asarray(y_test)
  X_pred_prob = loaded_model.predict_proba(y_test)
  prediction= loaded_binarizer.inverse_transform(X_pred_new)
  print(prediction)
  #make sure you return the vaule, this would help you create the API
  return(A,prediction)

def mainPredict(Arr):
  # if Arr =[1,2,3,4,5,6] and each value is an input parameter to predict 
  #initialize pool 
  pool = mp.Pool(mp.cpu_count())
  
  #map array elements to the predict function
  result = [pool.apply(predict, args=(A))for A in Arr]
  
  #close the pool
  pool.close()
  
  return(result)