8000 Initial commit of prediction examples · lauro-cesar/python-docs-samples@42645c7 · GitHub
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Initial commit of prediction examples
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‎ml_engine/online_prediction/predict.py

Lines changed: 116 additions & 27 deletions
Original file line numberDiff line numberDiff line change
@@ -1,21 +1,32 @@
1-
import argparse
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import json
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# Copyright 2016 Google Inc. All Rights Reserved. Licensed under the Apache
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# License, Version 2.0 (the "License"); you may not use this file except in
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# compliance with the License. You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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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, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations under
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# the License.
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"""Examples of using the Cloud ML Engine's online prediction service."""
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313
# [START import_libraries]
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import googleapiclient.discovery
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# [END import_libraries]
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718
# [START authenticating]
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def get_ml_engine_service():
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return googleapiclient.discovery.build_from_document(
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json.load(open('staging_ml.json')))
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return googleapiclient.discovery.build('ml', 'v1beta1')
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# [END authenticating]
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# [START predict_json]
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def predict_json(project, model, instances, version=None):
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"""Send data instances 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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project: str, project where the Cloud ML Engine Model is deployed.
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model: str, model name.
1930
instances: [dict], dictionaries from string keys defined by the model
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to data.
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version: [optional] str, version of the model to target.
@@ -26,10 +37,10 @@ def predict_json(project, model, instances, version=None):
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name = 'projects/{}/models/{}'.format(project, model)
2738
if version is not None:
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name += '/versions/{}'.format(version)
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3041
response = service.projects().predict(
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name=name,
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body={"instances": instances}
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body={'instances': instances}
3344
).execute()
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3546
if 'error' in response:
@@ -38,15 +49,19 @@ def predict_json(project, model, instances, version=None):
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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, model, example_bytes_list, key='tfrecord', version=None):
54+
def predict_tf_records(project,
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model,
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example_bytes_list,
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key='tfrecord',
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version=None):
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"""Send data instances 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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feature_dict_list: [dict], dictionaries from string keys to
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tf.train.Feature protos.
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version: [optional] str, version of the model to target.
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model: str, model name.
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example_bytes_list: [str], Serialized tf.train.Example protos.
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version: str, version of the model to target.
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Returns:
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A dictionary of prediction results defined by the model.
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"""
@@ -58,7 +73,7 @@ def predict_tf_records(project, model, example_bytes_list, key='tfrecord', versi
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response = service.projects().predict(
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name=name,
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body={"instances": [
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body={'instances': [
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{key: {'b64': base64.b64encode(example_bytes)}}
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for example_bytes in example_bytes_list
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]}
@@ -67,8 +82,18 @@ def predict_tf_records(project, model, example_bytes_list, key='tfrecord', versi
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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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7087

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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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Args:
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json_instance: dict, representing data to be serialized.
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Returns:
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A string (as a container for bytes).
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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():
@@ -83,18 +108,82 @@ def census_to_example_bytes(json_instance):
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feature=feature_dict
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)
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).SerializeToString()
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# [END predict_tf_records]
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# [END census_to_example_bytes]
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if __name__=='__main__':
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import sys
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import base64
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# [START predict_from_files]
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def predict_from_files(project,
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model,
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files,
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version=None,
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force_tfrecord=False):
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import json
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with open(sys.argv[1]) as f:
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instances = [json.loads(line) for line in f.readlines()]
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95-
with open(sys.argv[2], 'w') as f:
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for instance in instances:
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f.write(json.dumps(
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{'tfrecord': {'b64': base64.b64encode(
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census_to_example_string(instance)
100-
)}}))
121+
import itertools
122+
instances = (json.loads(line)
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for f in files
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for line in f.readlines())
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126+
# Requests to online prediction
127+
# can have at most 100 instances
128+
args = [instances] * 100
129+
instance_batches = itertools.izip(*args)
130+
131+
results = []
132+
for batch in instance_batches:
133+
if force_tfrecord:
134+
example_bytes_list = [
135+
census_to_example_bytes(instance)
136+
for instance in batch
137+
]
138+
results.append(predict_tf_records(
139+
project,
140+
model,
141+
example_bytes_list,
142+
version=version
143+
))
144+
else:
145+
results.append(predict_json(
146+
project,
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model,
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batch,
149+
version=version
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))
151+
return results
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# [END predict_from_files]
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154+
155+
if __name__ == '__main__':
156+
import argparse
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import os
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parser = argparse.ArgumentParser()
159+
parser.add_argument(
160+
'input_files',
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help='File paths with examples to predict',
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nargs='+',
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type=os.path.abspath
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)
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parser.add_argument(
166+
'--project',
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help='Project in which the model is deployed',
168+
type=str,
169+
required=True
170+
)
171+
parser.add_argument(
172+
'--model',
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help='Model name',
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type=str,
175+
required=True
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)
177+
parser.add_argument(
178+
'--version',
179+
help='Name of the version.',
180+
type=str
181+
)
182+
parser.add_argument(
183+
'--force-tfrecord',
184+
help='Send predictions as TFRecords rather than raw JSON',
185+
action='store_true',
186+
default=False
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)
188+
args = parser.parse_args()
189+
predict_from_files(**args.__dict__)

‎ml_engine/online_prediction/test.json renamed to ‎ml_engine/online_prediction/resources/test.json

File renamed without changes.

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