vertex ai 中的自定义容器部署

我正在尝试在 vertex ai 端点中部署自定义容器以进行预测。申请内容如下。

1. 烧瓶-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')

2. 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

3. 预处理.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

4. 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]

4. 要求.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

5. model

6. 分词器.pkl

我正在关注这个博客顶点人工智能部署 https://medium.com/mlearning-ai/serverless-prediction-at-scale-part-2-custom-container-deployment-on-vertex-ai-103a43d0a290用于 gcloud 控制台命令将模型容器化并将其部署到端点。但是该模型需要很长时间才能部署，最终无法部署。

在本地主机中运行容器后，它按预期运行，但没有部署到 vertex ai 端点。我不明白问题是否出在 Flask app.py 或 Dockerfile 中，或者问题是否出在其他地方。

我能够通过向 http 服务器添加健康路由来解决此问题。我在我的烧瓶应用程序中添加了以下代码。

@app.route('/healthz')
def healthz():
    return "OK"