predict() 得到了一个意外的关键字参数 'stats'

Question

我正在尝试从 AI 平台上提供的 Tensor Flow 自定义例程中获取预测。

我已经设法通过以下设置为其提供服务：--runtime-version 2.3 --python-version 3.7 --machine-type mls1-c4-m2

但是当我尝试做出任何预测时，我总是收到这个错误。

ERROR:root:Prediction failed: predict() got an unexpected keyword argument 'stats'
ERROR:root:Prediction failed: unknown error.

例程有两个步骤：

获取输入（字符串）并使用 .pkl 格式的弓形模型将其转换为嵌入
使用嵌入来使用保存为 .h5 文件的 keras 模型获得预测

这是我的setup.py

from setuptools import setup

REQUIRED_PACKAGES = ['Keras==2.3.1', 'sklearn==0.0', 'h5py<3.0.0', 'numpy==1.16.0', 'scipy==1.4.1', 'pyyaml==5.2']

setup(
        name='my_custom_code',
        version='0.1',
        scripts=['predictor.py'],
        install_requires=REQUIRED_PACKAGES,
        packages=find_packages(),
        include_package_data=False,
        description=''
)

这是我的 predictor.py

import os
import pickle
import tensorflow as tf
import numpy as np

class MyPredictor(object):

    def __init__(self, model, bow_model):
        self._model = model
        self._bow_model = bow_model

    def predict(self, instances):

        outputs = []

        for x in instances:
            vector = self.embedding(x)
            output = self._model.predict(vector)
            outputs.append(output)

        return outputs

    def embedding(self, statement):
        vector = self._bow_model.transform(statement).toarray()
        vector = vector.to_list()
        return vector


    @classmethod
    def from_path(cls, model_dir):

        model_path = os.path.join(model_dir, 'model.h5')
        model = tf.keras.models.load_model(model_path, compile = False)

        preprocessor_path = os.path.join(model_dir, 'bow.pkl')
        with open(preprocessor_path, 'rb') as f:
            bow_model = pickle.load(f)

        return cls(model, bow_model)

我用于测试的脚本是：

import googleapiclient.discovery

instances = ['test','test']]

service = googleapiclient.discovery.build('ml', 'v1')
name = 'projects/{}/models/{}/versions/{}'.format(PROJECT_ID, MODEL_NAME, VERSION_NAME)

response = service.projects().predict(
    name=name,
    body={'instances': instances}
).execute()

if 'error' in response:
    raise RuntimeError(response['error'])
else:
  print(response['predictions'])

Answer 1

根据自定义预测例程 documentation, once creating the predictor class，predict() 方法应提供 self, instances, **kwargs 参数以正确处理预测请求。

instances: A list of prediction input instances.

**kwargs: A dictionary of keyword args provided as additional fields on the predict request body.

predict() 得到了一个意外的关键字参数 'stats'

predict() got an unexpected keyword argument 'stats'

python

google-cloud-platform

keras

google-cloud-ml

google-ai-platform