Azure ML model deployment fail: Module not found error - azure

Question

I'm trying to deploy a model locally using Azure ML before deploying to AKS. I have a custom script that I want to import into my entry script (scoring script), but it's saying it is not found.
Here is the error:
Here's my entry script with the custom script import on line 1:
import rake_refactored as rake
from operator import itemgetter
import pandas as pd
import datetime
import re
import operator
import numpy as np
import json
# Called when the deployed service starts
def init():
global stopword_path
# AZUREML_MODEL_DIR is an environment variable created during deployment.
# It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
# For multiple models, it points to the folder containing all deployed models (./azureml-models)
stopword_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'models/SmartStoplist.txt')
# load models
def preprocess(df):
df = rake.prepare_data(df)
text = rake.process_response(df, "RESPNS")
return text
# Use model to make predictions
def predict(df):
text = preprocess(df)
return rake.extract_keywords(stopword_path, text)
def run(data):
try:
# Find the data property of the JSON request
df = pd.read_json(json.loads(data))
prediction = predict(df)
return json.dump(prediction)
except Exception as e:
return str(e)
And here is my model artifact directory in Azure ML showing that it is in the same directory as the entry script (rake_score.py).
What am I doing wrong? I had a similar issue before with a sklearn package that I was able to add to the pip-package list when I built the environment, but my custom script isn't a pip package.

Answer 1

Not able to find rake_refactored in documentation and on the internet.
You can try below steps for importing rake.
Using pip
pip install rake-nltk
Directly from the repository
git clone https://github.com/csurfer/rake-nltk.git
python rake-nltk/setup.py install
Sample Code:
from rake_nltk import Rake
# Uses stopwords for english from NLTK, and all puntuation characters by
# default
r = Rake()
# Extraction given the text.
r.extract_keywords_from_text(<text to process>)
# Extraction given the list of strings where each string is a sentence.
r.extract_keywords_from_sentences(<list of sentences>)
# To get keyword phrases ranked highest to lowest.
r.get_ranked_phrases()
# To get keyword phrases ranked highest to lowest with scores.
r.get_ranked_phrases_with_scores()
Refer - https://github.com/csurfer/rake-nltk

Answer 2

In order to access my custom script in my scoring script I needed to explicitly define the source directory in my inference configuration:
from azureml.core.model import InferenceConfig
inference_config = InferenceConfig(
environment = env,
entry_script = "rake_score.py",
source_directory='./models'
)