Model deployment fails when using pydantic

elsammonselsammons Member Posts: 6

I'm attempting to deploy a model, but my model uses pydantic for the function argument. In this approach the model fails to deploy. The in this case is based on what would be in a FastAPI implementation.

import pickle
import json
import time
from pydantic import BaseModel
from sklearn.feature_extraction.text import TfidfVectorizer
import xgboost as xgb

clf = pickle.load(open('data/xgbmodel.pickle', 'rb'))
vect = pickle.load(open('data/tfidfvect.pickle', 'rb'))

class Data(BaseModel):
  id: str = None
  project: str
  messages: str
def predict(data: Data):
  start = time.time()
  data_l = [data.messages]
  to_predict = vect.transform(data_l)
  prediction = clf.predict(to_predict)

  end = round((time.time() - start), 3)
  return {
    "project": data.project,
    "prediction": prediction[0], 
    "execution_time": end

However, when I test the model with the following

import requests
response ="",
        'data': {
            'project': 'PROJ',
            'messages': 'This is a test message and who knows what it will return\n'

The following error is returned:

('{\'error\': {\'message\': "predict() got an unexpected keyword argument '
 '\'project\'"}, \'model_time_in_ms\': 0, \'release\': {\'harness_version\': '
 "'0.1', 'model_version': '5f248eb11e43c7000602300d', 'model_version_number': "
 "1}, 'request_id': 'MXP0ZITN2FR3DGB1', 'timing': 0.1239776611328125}\n")

Any thoughts on how to resolve this?



  • elsammonselsammons Member Posts: 6
    edited August 3

    I was able to get this working by dropping pydantic and putting all required data features into the function call as arguments.

    def predict(project, messages, id = None):
  • zach.ingrahamzach.ingraham Member, Administrator, Moderator, Domino Posts: 42 admin

    Hi @elsammons ,

    I'm not personally familiar with pydantic and I did a quick check through our internal and external docs and don't see any cases of pydantic usage or example syntax. So it appears we haven't looked into this before, meaning I don't have an immediate answer without some testing of our own. It makes sense that your code works without pydantic, just using conventional syntax, but I'll check to see if our API can be compatible with an annotation-based tool like this one.


  • melanie.vealemelanie.veale Member, Domino Posts: 14

    Hi @elsammons , am I correct in guessing that you did not change the test code for calling the model in any way when you got it to work by dropping pydantic?

    Can you try adding back the pydantic syntax to your function signature (I'm changing the naming a little for reasons you'll see below):

    def predict(inputdata):
        data = Data(**inputdata)

    And then try calling the model with an additional level of nesting in your json like so:

    response ="",
            'data': {
                'inputdata': {
                    'project': 'PROJ',
                    'messages': 'This is a test message and who knows what it will return\n'

    Hopefully my naming helps clarify what is going on: basically, the first data is part of the Domino boilerplate for calling the model (you will see it in the sample calling code) - it is required for Domino to know "this is what I pass to the model function". Then the second inputdata corresponds to the argument of your function, which you want to use pydantic to help unpack and check. You'll also notice I took out the annotation on the function signature and added an explicit instantiation - I think this is needed since inputs will come in raw json/dict format, but then the rest of the type annotations you define in your Data class should work as expected.

    Let us know how it goes!


  • elsammonselsammons Member Posts: 6

    Thank you @melanie.veale. Correct I changed no code on the actual test code side. But through some testing and additional print statements I was able to figure out what was going on.

    i.e. I determined that Model API boilerplate is essentially treating the 'data': {} in a manner similar to kwargs, each key value pair becomes input into the api function.

    Once I determined this is what was going on I was able to conclude that I can simply the expected features as arguments to my function.

    I will give your approach a shot as well, given I'm already adding the level 'data': {} ('inputdata': {} won't be an issue) for the boilerplate, something that FastAPI does not require.

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