Question

我正在尝试使用Flask的蓝图通过API为机器学习模型提供服务，这是我的烧瓶__init__.py文件

from flask import Flask

def create_app(test_config=None):
    app = Flask(__name__)

    @app.route("/healthcheck")
    def healthcheck() -> str:
        return "OK"

    # Registers the machine learning blueprint
    from . import ml
    app.register_blueprint(ml.bp)

    return app

包含ml.py端点蓝图的/ml文件

import numpy as np
from . import configuration as cfg
import tensorflow as tf

from flask import (
    Blueprint, flash, request, url_for
)


bp = Blueprint("ml", __name__, url_prefix="/ml")
keras_model = None
graph = None

@bp.before_app_first_request
def load_model():
    print("Loading keras model")
    global keras_model
    global graph
    with open(cfg.config["model"]["path"], 'r') as model_file:
        yaml_model = model_file.read()
        keras_model = tf.keras.models.model_from_yaml(yaml_model)
        graph = tf.get_default_graph()
        keras_model.load_weights(cfg.config["model"]["weights"])

@bp.route('/predict', methods=['POST'])
def predict() -> str:
    global graph
    features = np.array([request.get_json()['features']])
    print(features, len(features), features.shape)
    with graph.as_default():
        prediction = keras_model.predict(features)
    print(prediction)
    return "%.2f" % prediction

我使用命令行脚本运行服务器

#!/bin/bash 

export FLASK_APP=src
export FLASK_ENV=development
flask run

如果我转到localhost:5000/healthcheck，则在运行以下curl时，我应该得到OK响应

curl -X POST \
  http://localhost:5000/ml/predict \
  -H 'Cache-Control: no-cache' \
  -H 'Content-Type: application/json' \
  -d '{
 "features" : [17.0, 0, 0, 12.0, 1, 0, 0]
}'

我第一次收到响应[[1.00]]，如果再次运行它，则会出现以下错误

tensorflow.python.framework.errors_impl.FailedPreconditionError: 
Error while reading resource variable dense/kernel from
Container: localhost. This could mean that the variable was uninitialized. 
Not found: Container localhost does not exist. (Could not find resource: localhost/dense/kernel)
         [[{{node dense/MatMul/ReadVariableOp}}]]

如果我修改了蓝图文件，服务器将检测到更改并刷新它，我可以再次调用API，它将为第一次调用返回正确的结果，而我又回到了错误。为什么会这样？为什么只在第一个电话之后拨打电话？

Answer 1

您可以尝试创建对用于加载模型的会话的引用，然后将其设置为在每个请求中由keras使用。即执行以下操作：

from tensorflow.python.keras.backend import set_session
from tensorflow.python.keras.models import load_model

tf_config = some_custom_config
sess = tf.Session(config=tf_config)
graph = tf.get_default_graph()

# IMPORTANT: models have to be loaded AFTER SETTING THE SESSION for keras! 
# Otherwise, their weights will be unavailable in the threads after the session there has been set
set_session(sess)
model = load_model(...)

，然后在每个请求中：

global sess
global graph
with graph.as_default():
    set_session(sess)
    model.predict(...)

使用Keras + Flask蓝图时容器本地主机不存在错误

1 个答案: