Python REST API 在 Docker 容器中无法正常工作

Question

我使用 python 和 Flask 创建了一个简单的 ML API。它从sample.csv获取数据并基于该数据训练逻辑回归模型。我还有一个“/predict”端点，我可以在其中输入参数以供模型预测。

示例 localhost:80/predict?weight1=1.2&weight2=0.00123&&weight3=0.45 将输出 { "predicted": 1}

ma​​in.py:

from sklearn.linear_model import LogisticRegression
from flask import Flask, request
import numpy as np

# Create Flask object to run
app = Flask(__name__)

@app.route('/')
def home():
    return "Predicting status from other features"


@app.route('/predict')
def predict():
    # Get values from the server
    weight1 = request.args['weight1']
    weight2 = request.args['weight2']
    weight3 = request.args['weight3']

    testData = np.array([weight1, weight2, weight3]).reshape(1, -1)

    class_predicted = logisticRegr.predict(testData.astype(float))

    output = "{ \"predicted\": " + "\"" + class_predicted[0] + "\"" +  "}"

    return output


# Train and load the model based on the MetroPCS_Sample
def load_model():
    global logisticRegr
    label_y = []
    label_x = []

    with open('sample.csv') as f:
        lines = f.readlines()
        for line in lines[1:]:
            # Adding labels to label_y
            label_y.append(int(line[0]))
            line = line.strip().split(",")
            x_data = []
            for e in line[1:]:
            # Adding other features to label_x
                x_data.append(float(e))
            label_x.append(x_data)


    train_x = label_x[:700]
    train_y = label_y[:700]

    test_x = label_x[700:1000]
    test_y = label_y[700:1000]

    logisticRegr = LogisticRegression()


    logisticRegr.fit(train_x, train_y)

    predictions = logisticRegr.predict(test_x)

    score = logisticRegr.score(test_x, test_y)
    # print score

if __name__ == "__main__":
    print("Starting Server...")

    # Call function that loads Model
    load_model()

    # Run Server
    app.run(host="127.0.0.1", debug=True, port=80) 

当我在没有容器的情况下运行此脚本时，一切正常。

但是，当我将其放入容器并运行时，出现以下错误:

NameError:全局名称“logisticRegr”未定义

Dockerfile

FROM tiangolo/uwsgi-nginx-flask:python2.7

# copy over our requirements.txt file
COPY requirements.txt /tmp/

# upgrade pip and install required python packages
RUN pip install -U pip
RUN pip install -r /tmp/requirements.txt

COPY ./app /app

ENV MESSAGE "hello"

需求.txt

Flask
numpy
sklearn
scipy

您知道当脚本位于容器内时什么会导致 NameError 吗？

Answer 1

在您的def load_model()中。你有global LogisticsRegr。仅使用 global 关键字不会使您的变量成为全局变量。

当您想要访问和更改函数内的全局变量时，可以使用全局变量。由于您的 logisticRegr 不是全局的，

当您尝试在 def Predict() 中访问它时，

class_predicted = LogisticRegr.predict(testData.astype(float)) 你会得到NameError: global name 'logisticRegr' is not Defined。

现在来解决您的问题。在初始化 app 变量后初始化/声明您的 logisticRegr 模型变量，如下所示:

# Create Flask object to run
app = Flask(__name__)
logisticRegr = LogisticRegression()

然后在 test_y = label_y[700:1000]

之后从 load_model 中删除变量初始化

PS:建议将全局变量大写。以便在代码中轻松识别它们。