AWS Elastic Beanstalk中Flask应用报HTTP 503且ELB健康异常求助

ELB health is failing or not available for all instances.

部署仅含index.html的示例程序可正常运行，推测问题出在代码或镜像配置层面，相关代码及配置如下：

Flask应用代码：app.py #

from flask import Flask, jsonify, request
from util import prediction

application = Flask(__name__)

@application.route('/predict', methods=['POST'])
def predict():
   data = request.get_json()
   try:
       sample = data['text']     
   except KeyError:
       return jsonify({'error':'No text sent'})
   pred = prediction(sample)
   try:
       result = jsonify(pred)
   except TypeError as e:
       result = jsonify({'error': str(e)})
   return result
   
if __name__ == '__main__':
   application.run(host='0.0.0.0', debug= True)

工具模块：util.py #

import nltk
import pandas as pd
from nltk import TweetTokenizer
import numpy as np
import nltk
from nltk.stem.wordnet import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer
import csv
import pandas as pd
import time
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from nltk.tokenize import TweetTokenizer
from nltk.tag import pos_tag
import re
import string
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import stopwords
import joblib
import warnings
warnings.filterwarnings("ignore")


# nltk.download('averaged_perceptron_tagger')
# nltk.download('wordnet')
# nltk.download('omw-1.4')
# nltk.download('stopwords')

token = TweetTokenizer()


def lemmatize_sentence(tokens):
    lemmatizer = WordNetLemmatizer()
    lemmatize_sentence = []
    for word, tag in pos_tag(tokens):
        if tag.startswith('NN'):
            pos = 'n'
        elif tag.startswith('VB'):
            pos = 'v'
        else:
            pos = 'a'
        lemmatize_sentence.append(lemmatizer.lemmatize(word, pos))
    return lemmatize_sentence
# print(' '.join(lemmatize_sentence(data[0][0])))


# Data cleaning, getting rid of words not needed for analysis.

stop_words = stopwords.words('english')


def cleaned(token):
    if token == 'u':
        return 'you'
    if token == 'r':
        return 'are'
    if token == 'some1':
        return 'someone'
    if token == 'yrs':
        return 'years'
    if token == 'hrs':
        return 'hours'
    if token == 'mins':
        return 'minutes'
    if token == 'secs':
        return 'seconds'
    if token == 'pls' or token == 'plz':
        return 'please'
    if token == '2morow':
        return 'tomorrow'
    if token == '2day':
        return 'today'
    if token == '4got' or token == '4gotten':
        return 'forget'
    if token == 'amp' or token == 'quot' or token == 'lt' or token == 'gt':
        return ''
    return token


# Noise removal from data, removing links, mentions and words with less than 3 length.


def remove_noise(tokens):
    cleaned_tokens = []
    for token, tag in pos_tag(tokens):
        # using non capturing groups ?:)// and eleminating the token if its a link.
        token = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|(?:%[0-9a-fA-F]))+', '', token)
        token = re.sub('[^a-zA-Z]', ' ', token)
        # eliminating token if its a mention
        token = re.sub("(@[A-Za-z0-9_]+)", "", token)
        if tag.startswith("NN"):
            pos = 'n'
        elif tag.startswith("VB"):
            pos = 'v'
        else:
            pos = 'a'

        lemmatizer = WordNetLemmatizer()
        token = lemmatizer.lemmatize(token, pos)

        cleaned_token = cleaned(token.lower())
        # Eliminating if the length of the token is less than 3, if its a punctuation or if it is a stopword.
        if cleaned_token not in string.punctuation and len(cleaned_token) > 2 and cleaned_token not in stop_words:
            cleaned_tokens.append(cleaned_token)
    return cleaned_tokens

with open ('Models/Sentimenttfpipe', 'rb') as f:
    loaded_pipeline = joblib.load(f)


def prediction(body):    
    # loaded_pipeline = joblib.load('Api/Models/Sentimenttfpipe')
    text= []
    test = token.tokenize(body)
    test = remove_noise(test)
    text.append(" ".join(test))
    test = pd.DataFrame(text, columns=['text'])
    a = loaded_pipeline.predict(test['text'].values.astype('U'))
    final = []
    if a[0] == 0:
        final.append({'Label' : 'Relaxed'})
        return {'Label' : 'Relaxed'}
        
    if a[0] == 1:
        final.append({'Label' : 'Angry'})
        return {'Label' : 'Angry'}
        
    if a[0] == 2:
        final.append({'Label' : 'Fearful'})
        return {'Label' : 'Fearful'}
        
    if a[0] == 3:
        final.append({'Label' : 'Happy'})
        return {'Label' : 'Happy'}
        
    if a[0] == 4:
        final.append({'Label' : 'Sad'})
        return {'Label' : 'Sad'}
        
    if a[0] == 5:
        final.append({'Label' : 'Surprised'})
        return {'Label' : 'Surprised'}
    
if __name__ == '__main__':
    sen = "May the force be with you"
    a = prediction(sen)
    print(a)

Dockerfile #

FROM python:3.10.8

WORKDIR /app

COPY ["requirements.txt", "./"]

RUN pip install -r requirements.txt

RUN python -c "import nltk; nltk.download('averaged_perceptron_tagger'); nltk.download('wordnet'); nltk.download('omw-1.4'); nltk.download('stopwords');"

COPY . . 

EXPOSE 5000

ENTRYPOINT [ "gunicorn", "--bind=0.0.0.0:5000", "app:application" ]

docker-compose.yml #

version: "3.7"

services:
  mlapp:
    container_name: Container
    image: mlapp
    ports: 
      - "5000:5000"
    build: 
      context: .
      dockerfile: Dockerfile

requirements.txt #

Flask>=2.2.2
joblib==1.2.0
nltk==3.7
numpy==1.21.6
pandas==1.5.1
regex==2022.10.31
requests==2.28.1
scikit-learn==1.1.3
gunicorn==20.1.0

排查方向与解决方案 #

1. 健康检查端点缺失 #

AWS Elastic Beanstalk默认会对根路径/执行健康检查，但你的Flask应用只配置了/predict接口，没有处理GET请求的根路径。当ELB尝试访问/时，会返回404错误，导致健康状态判定为严重。

解决方法：添加一个根路径的GET接口用于健康检查：

@application.route('/')
def health_check():
    return jsonify({'status': 'healthy'}), 200

2. 模型文件路径问题 #

在util.py中，加载模型的路径是Models/Sentimenttfpipe，需要确认该目录和文件是否在Docker镜像中正确复制。如果构建镜像时Models目录未被包含，启动时会抛出文件找不到的异常，导致服务无法启动。

验证方法：在本地构建镜像后，进入容器检查文件是否存在：

docker run -it --rm mlapp ls /app/Models

如果路径错误或文件缺失，修正路径，确保Models目录被正确复制到镜像中（当前Dockerfile的COPY . .应该会包含，但需确认本地目录结构是否正确）。

3. 依赖安装与NLTK数据下载顺序 #

当前Dockerfile中先安装依赖再下载NLTK数据，这没问题，但需要确认下载是否成功。可以在构建镜像时添加日志输出，或进入容器验证NLTK数据是否存在：

docker run -it --rm mlapp python -c "import nltk; print(nltk.data.find('corpora/stopwords'))"

如果下载失败，可尝试更换NLTK数据源镜像，或在下载命令中指定--timeout参数：

RUN python -c "import nltk; nltk.download('averaged_perceptron_tagger', timeout=60); nltk.download('wordnet', timeout=60); nltk.download('omw-1.4', timeout=60); nltk.download('stopwords', timeout=60);"

4. Gunicorn启动参数与端口配置 #

确认Gunicorn绑定的端口是5000，且Elastic Beanstalk的容器端口配置与之一致（默认情况下，Docker部署会自动映射EXPOSE的端口，但可以在EB配置中明确指定容器端口为5000）。

另外，可在Gunicorn启动命令中添加--workers参数，提升服务稳定性：

ENTRYPOINT [ "gunicorn", "--bind=0.0.0.0:5000", "--workers=2", "app:application" ]

5. 查看Elastic Beanstalk日志 #

直接查看EB环境的日志是最有效的排查方式，可通过EB控制台下载完整日志，重点查看：

• 容器启动日志：确认服务是否正常启动
• 应用日志：查看是否有Python异常抛出

内容的提问来源于stack exchange，提问作者M Bilal Ayaz