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Add samples for the Cloud ML Engine (GoogleCloudPlatform#824)
Samples for using online prediction with JSON and TFRecord inputs.
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‎ml_engine/online_prediction/README.md

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https://cloud.google.com/ml-engine/docs/concepts/prediction-overview

‎ml_engine/online_prediction/predict.py

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#!/bin/python
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# Copyright 2017 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Examples of using the Cloud ML Engine's online prediction service."""
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import argparse
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import base64
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import json
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# [START import_libraries]
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import googleapiclient.discovery
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# [END import_libraries]
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import six
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# [START predict_json]
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def predict_json(project, model, instances, version=None):
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"""Send json data to a deployed model for prediction.
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Args:
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project (str): project where the Cloud ML Engine Model is deployed.
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model (str): model name.
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instances ([Mapping[str: Any]]): Keys should be the names of Tensors
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your deployed model expects as inputs. Values should be datatypes
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convertible to Tensors, or (potentially nested) lists of datatypes
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convertible to tensors.
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version: str, version of the model to target.
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Returns:
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Mapping[str: any]: dictionary of prediction results defined by the
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model.
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"""
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# Create the ML Engine service object.
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# To authenticate set the environment variable
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# GOOGLE_APPLICATION_CREDENTIALS=<path_to_service_account_file>
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service = googleapiclient.discovery.build('ml', 'v1beta1')
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name = 'projects/{}/models/{}'.format(project, model)
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if version is not None:
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name += '/versions/{}'.format(version)
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response = service.projects().predict(
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name=name,
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body={'instances': instances}
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).execute()
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if 'error' in response:
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raise RuntimeError(response['error'])
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return response['predictions']
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# [END predict_json]
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# [START predict_tf_records]
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def predict_tf_records(project,
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model,
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example_bytes_list,
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version=None):
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"""Send protocol buffer data to a deployed model for prediction.
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Args:
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project (str): project where the Cloud ML Engine Model is deployed.
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model (str): model name.
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example_bytes_list ([str]): A list of bytestrings representing
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serialized tf.train.Example protocol buffers. The contents of this
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protocol buffer will change depending on the signature of your
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deployed model.
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version: str, version of the model to target.
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Returns:
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Mapping[str: any]: dictionary of prediction results defined by the
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model.
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"""
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service = googleapiclient.discovery.build('ml', 'v1beta1')
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name = 'projects/{}/models/{}'.format(project, model)
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if version is not None:
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name += '/versions/{}'.format(version)
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response = service.projects().predict(
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name=name,
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body={'instances': [
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{'b64': base64.b64encode(example_bytes)}
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for example_bytes in example_bytes_list
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]}
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).execute()
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if 'error' in response:
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raise RuntimeError(response['error'])
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return response['predictions']
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# [END predict_tf_records]
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# [START census_to_example_bytes]
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def census_to_example_bytes(json_instance):
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"""Serialize a JSON example to the bytes of a tf.train.Example.
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This method is specific to the signature of the Census example.
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See: https://cloud.google.com/ml-engine/docs/concepts/prediction-overview
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for details.
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Args:
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json_instance (Mapping[str: Any]): Keys should be the names of Tensors
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your deployed model expects to parse using it's tf.FeatureSpec.
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Values should be datatypes convertible to Tensors, or (potentially
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nested) lists of datatypes convertible to tensors.
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Returns:
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str: A string as a container for the serialized bytes of
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tf.train.Example protocol buffer.
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"""
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import tensorflow as tf
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feature_dict = {}
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for key, data in json_instance.iteritems():
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if isinstance(data, six.string_types):
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feature_dict[key] = tf.train.Feature(
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bytes_list=tf.train.BytesList(value=[str(data)]))
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elif isinstance(data, float):
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feature_dict[key] = tf.train.Feature(
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float_list=tf.train.FloatList(value=[data]))
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elif isinstance(data, int):
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feature_dict[key] = tf.train.Feature(
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int64_list=tf.train.Int64List(value=[data]))
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return tf.train.Example(
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features=tf.train.Features(
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feature=feature_dict
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)
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).SerializeToString()
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# [END census_to_example_bytes]
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def main(project, model, version=None, force_tfrecord=False):
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"""Send user input to the prediction service."""
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while True:
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try:
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user_input = json.loads(raw_input("Valid JSON >>>"))
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except KeyboardInterrupt:
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return
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if not isinstance(user_input, list):
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user_input = [user_input]
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try:
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if force_tfrecord:
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example_bytes_list = [
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census_to_example_bytes(e)
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for e in user_input
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]
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result = predict_tf_records(
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project, model, example_bytes_list, version=version)
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else:
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result = predict_json(
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project, model, user_input, version=version)
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except RuntimeError as err:
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print(str(err))
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else:
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print(result)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--project',
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help='Project in which the model is deployed',
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type=str,
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required=True
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)
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parser.add_argument(
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'--model',
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help='Model name',
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type=str,
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required=True
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)
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parser.add_argument(
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'--version',
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help='Name of the version.',
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type=str
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)
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parser.add_argument(
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'--force-tfrecord',
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help='Send predictions as TFRecords rather than raw JSON',
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action='store_true',
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default=False
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)
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args = parser.parse_args()
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main(
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args.project,
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args.model,
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version=args.version,
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force_tfrecord=args.force_tfrecord
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)

‎ml_engine/online_prediction/predict_test.py

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# Copyright 2017 Google Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for predict.py ."""
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import base64
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import pytest
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import predict
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MODEL = 'census'
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VERSION = 'v1'
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TF_RECORDS_VERSION = 'v1tfrecord'
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PROJECT = 'python-docs-samples-tests'
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JSON = {
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'age': 25,
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'workclass': ' Private',
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'education': ' 11th',
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'education_num': 7,
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'marital_status': ' Never-married',
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'occupation': ' Machine-op-inspct',
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'relationship': ' Own-child',
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'race': ' Black',
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'gender': ' Male',
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'capital_gain': 0,
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'capital_loss': 0,
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'hours_per_week': 40,
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'native_country': ' United-States'
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}
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EXPECTED_OUTPUT = {
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u'probabilities': [0.9942260384559631, 0.005774002522230148],
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u'logits': [-5.148599147796631],
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u'classes': 0,
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u'logistic': [0.005774001590907574]
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}
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def test_predict_json():
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result = predict.predict_json(
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PROJECT, MODEL, [JSON, JSON], version=VERSION)
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assert [EXPECTED_OUTPUT, EXPECTED_OUTPUT] == result
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def test_predict_json_error():
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with pytest.raises(RuntimeError):
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predict.predict_json(PROJECT, MODEL, [{"foo": "bar"}], version=VERSION)
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@pytest.mark.slow
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def test_census_example_to_bytes():
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b = predict.census_to_example_bytes(JSON)
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assert base64.b64encode(b) is not None
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@pytest.mark.slow
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@pytest.mark.xfail('Single placeholder inputs broken in service b/35778449')
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def test_predict_tfrecords():
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b = predict.census_to_example_bytes(JSON)
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result = predict.predict_tfrecords(
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PROJECT, MODEL, [b, b], version=TF_RECORDS_VERSION)
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assert [EXPECTED_OUTPUT, EXPECTED_OUTPUT] == result

‎ml_engine/online_prediction/requirements.txt

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tensorflow==1.0.0

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