merla18
diff --git a/‎ml_engine/online_prediction/predict.py‎
Lines changed: 116 additions & 27 deletions b/‎ml_engine/online_prediction/predict.py‎
Lines changed: 116 additions & 27 deletions
diff --git a/‎ml_engine/online_prediction/test.json‎ ‎…ne/online_prediction/resources/test.json‎ml_engine/online_prediction/test.json renamed to ml_engine/online_prediction/resources/test.json b/‎ml_engine/online_prediction/test.json‎ ‎…ne/online_prediction/resources/test.json‎ml_engine/online_prediction/test.json renamed to ml_engine/online_prediction/resources/test.json
@@ -1,21 +1,32 @@
-import argparse
-import json
+# Copyright 2016 Google Inc. All Rights Reserved. Licensed under the Apache
+# License, Version 2.0 (the "License"); you may not use this file except in
+# compliance with the License. You may obtain a copy of the License at
+# http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations under
+# the License.
+"""Examples of using the Cloud ML Engine's online prediction service."""
+
 # [START import_libraries]
 import googleapiclient.discovery
 # [END import_libraries]
 
+
 # [START authenticating]
 def get_ml_engine_service():
-    return googleapiclient.discovery.build_from_document(
-        json.load(open('staging_ml.json')))
+    return googleapiclient.discovery.build('ml', 'v1beta1')
 # [END authenticating]
 
+
 # [START predict_json]
 def predict_json(project, model, instances, version=None):
     """Send data instances to a deployed model for prediction
     Args:
-        project: str, project where the Cloud ML Engine Model is deployed
-        model: str, model name
+        project: str, project where the Cloud ML Engine Model is deployed.
+        model: str, model name.
         instances: [dict], dictionaries from string keys defined by the model
         to data.
         version: [optional] str, version of the model to target.
@@ -26,10 +37,10 @@ def predict_json(project, model, instances, version=None):
     name = 'projects/{}/models/{}'.format(project, model)
     if version is not None:
         name += '/versions/{}'.format(version)
-    
+
     response = service.projects().predict(
         name=name,
-        body={"instances": instances}
+        body={'instances': instances}
     ).execute()
 
     if 'error' in response:
@@ -38,15 +49,19 @@ def predict_json(project, model, instances, version=None):
     return response['predictions']
 # [END predict_json]
 
+
 # [START predict_tf_records]
-def predict_tf_records(project, model, example_bytes_list, key='tfrecord', version=None):
+def predict_tf_records(project,
+                       model,
+                       example_bytes_list,
+                       key='tfrecord',
+                       version=None):
     """Send data instances to a deployed model for prediction
     Args:
         project: str, project where the Cloud ML Engine Model is deployed
-        model: str, model name
-        feature_dict_list: [dict], dictionaries from string keys to
-          tf.train.Feature protos.
-        version: [optional] str, version of the model to target.
+        model: str, model name.
+        example_bytes_list: [str], Serialized tf.train.Example protos.
+        version: str, version of the model to target.
     Returns:
         A dictionary of prediction results defined by the model.
     """
@@ -58,7 +73,7 @@ def predict_tf_records(project, model, example_bytes_list, key='tfrecord', versi
 
     response = service.projects().predict(
         name=name,
-        body={"instances": [
+        body={'instances': [
             {key: {'b64': base64.b64encode(example_bytes)}}
             for example_bytes in example_bytes_list
         ]}
@@ -67,8 +82,18 @@ def predict_tf_records(project, model, example_bytes_list, key='tfrecord', versi
         raise RuntimeError(response['error'])
 
     return response['predictions']
+# [END predict_tf_records]
+
 
+# [START census_to_example_bytes]
 def census_to_example_bytes(json_instance):
+    """Serialize a JSON example to the bytes of a tf.train.Example.
+    This method is specific to the signature of the Census example.
+    Args:
+        json_instance: dict, representing data to be serialized.
+    Returns:
+        A string (as a container for bytes).
+    """
     import tensorflow as tf
     feature_dict = {}
     for key, data in json_instance.iteritems():
@@ -83,18 +108,82 @@ def census_to_example_bytes(json_instance):
             feature=feature_dict
         )
     ).SerializeToString()
-# [END predict_tf_records]
+# [END census_to_example_bytes]
 
-if __name__=='__main__':
-    import sys
-    import base64
+
+# [START predict_from_files]
+def predict_from_files(project,
+                       model,
+                       files,
+                       version=None,
+                       force_tfrecord=False):
     import json
-    with open(sys.argv[1]) as f:
-        instances = [json.loads(line) for line in f.readlines()]
-
-    with open(sys.argv[2], 'w') as f:
-        for instance in instances:
-            f.write(json.dumps(
-                {'tfrecord': {'b64': base64.b64encode(
-                    census_to_example_string(instance)
-                )}}))
+    import itertools
+    instances = (json.loads(line)
+                 for f in files
+                 for line in f.readlines())
+
+    # Requests to online prediction
+    # can have at most 100 instances
+    args = [instances] * 100
+    instance_batches = itertools.izip(*args)
+
+    results = []
+    for batch in instance_batches:
+        if force_tfrecord:
+            example_bytes_list = [
+                census_to_example_bytes(instance)
+                for instance in batch
+            ]
+            results.append(predict_tf_records(
+                project,
+                model,
+                example_bytes_list,
+                version=version
+            ))
+        else:
+            results.append(predict_json(
+                project,
+                model,
+                batch,
+                version=version
+            ))
+    return results
+# [END predict_from_files]
+
+
+if __name__ == '__main__':
+    import argparse
+    import os
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        'input_files',
+        help='File paths with examples to predict',
+        nargs='+',
+        type=os.path.abspath
+    )
+    parser.add_argument(
+        '--project',
+        help='Project in which the model is deployed',
+        type=str,
+        required=True
+    )
+    parser.add_argument(
+        '--model',
+        help='Model name',
+        type=str,
+        required=True
+    )
+    parser.add_argument(
+        '--version',
+        help='Name of the version.',
+        type=str
+    )
+    parser.add_argument(
+        '--force-tfrecord',
+        help='Send predictions as TFRecords rather than raw JSON',
+        action='store_true',
+        default=False
+    )
+    args = parser.parse_args()
+    predict_from_files(**args.__dict__)