Custom Container deployment in vertex ai - flask

Question

I am trying to deploy my custom container in vertex ai endpoint for predictions. The contents of the application are as follows.
Flask - app.py
import pandas as pd
from flask import Flask, jsonify,request
import tensorflow
import pre_process
import post_process
app = Flask(__name__)
#app.route('/predict',methods=['POST'])
def predict():
req = request.json.get('instances')
input_data = req[0]['email']
#preprocessing
text = pre_process.preprocess(input_data)
vector = pre_process.preprocess_tokenizing(text)
model = tensorflow.keras.models.load_model('model')
#predict
prediction = model.predict(vector)
#postprocessing
value = post_process.postprocess(list(prediction[0]))
return jsonify({'output':{'doc_class':value}})
if __name__=='__main__':
app.run(host='0.0.0.0')
Dockerfile
FROM python:3.7
WORKDIR /app
COPY . /app
RUN pip install --trusted-host pypi.python.org -r requirements.txt
CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]
EXPOSE 5050
pre_process.py
#import
import pandas as pd
import pickle
import re
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
def preprocess(text):
"""Do all the Preprocessing as shown above and
return a tuple contain preprocess_email,preprocess_subject,preprocess_text for that Text_data"""
#After you store it in the list, Replace those sentances in original text by space.
text = re.sub("(Subject:).+"," ",text,re.I)
#Delete all the sentances where sentence starts with "Write to:" or "From:".
text = re.sub("((Write to:)|(From:)).+","",text,re.I)
#Delete all the tags like "< anyword >"
text = re.sub("<[^><]+>","",text)
#Delete all the data which are present in the brackets.
text = re.sub("\([^()]+\)","",text)
#Remove all the newlines('\n'), tabs('\t'), "-", "".
text = re.sub("[\n\t\\-]+","",text)
#Remove all the words which ends with ":".
text = re.sub("(\w+:)","",text)
#Decontractions, replace words like below to full words.
lines = re.sub(r"n\'t", " not", text)
lines = re.sub(r"\'re", " are", lines)
lines = re.sub(r"\'s", " is", lines)
lines = re.sub(r"\'d", " would", lines)
lines = re.sub(r"\'ll", " will", lines)
lines = re.sub(r"\'t", " not", lines)
lines = re.sub(r"\'ve", " have", lines)
lines = re.sub(r"\'m", " am", lines)
text = lines
#replace numbers with spaces
text = re.sub("\d+"," ",text)
# remove _ from the words starting and/or ending with _
text = re.sub("(\s_)|(_\s)"," ",text)
#remove 1 or 2 letter word before _
text = re.sub("\w{1,2}_","",text)
#convert all letters to lowercase and remove the words which are greater
#than or equal to 15 or less than or equal to 2.
text = text.lower()
text =" ".join([i for i in text.split() if len(i)<15 and len(i)>2])
#replace all letters except A-Z,a-z,_ with space
preprocessed_text = re.sub("\W+"," ",text)
return preprocessed_text
def preprocess_tokenizing(text):
#from tf.keras.preprocessing.text import Tokenizer
#from tf.keras.preprocessing.sequence import pad_sequences
tokenizer = pickle.load(open('tokenizer.pkl','rb'))
max_length = 1019
tokenizer.fit_on_texts([text])
encoded_docs = tokenizer.texts_to_sequences([text])
text_padded = pad_sequences(encoded_docs, maxlen=max_length, padding='post')
return text_padded
post_process.py
def postprocess(vector):
index = vector.index(max(vector))
classes = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]
return classes[index]
requirements.txt
gunicorn
pandas==1.3.3
numpy==1.19.5
flask
flask-cors
h5py==3.1.0
scikit-learn==0.24.2
tensorflow==2.6.0
model
tokenizer.pkl
I am following this blog vertex ai deployment for gcloud console commands to containerise and deploy the model to endpoint.But the model is taking forever to get deployed and ultimately fails to get deployed.
After running the container in local host, it runs as expected but it is not getting deployed into vertex ai endpoint. I don't understand whether the problem is in flask app.py or Dockerfile or whether the problem lies somewhere else.

Answer 1

I was able to resolve this issue by adding health route to http server. I added the following piece of code in my flask app.
#app.route('/healthz')
def healthz():
return "OK"