Add samples for the Cloud ML Engine

ml_engine/online_prediction/predict.py

@@ -0,0 +1,189 @@
+# 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('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.
+        instances: [dict], dictionaries from string keys defined by the model
+        to data.
+        version: [optional] str, version of the model to target.
+    Returns:
+        A dictionary of prediction results defined by the model.
+    """
+    service = get_ml_engine_service()
+    name = 'projects/{}/models/{}'.format(project, model)
+    if version is not None:
+        name += '/versions/{}'.format(version)
+
+    response = service.projects().predict(
+        name=name,
+        body={'instances': instances}
+    ).execute()
+
+    if 'error' in response:
+        raise RuntimeError(response['error'])
+
+    return response['predictions']
+# [END predict_json]
+
+
+# [START predict_tf_records]
+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.
+        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.
+    """
+    import base64
+    service = get_ml_engine_service()
+    name = 'projects/{}/models/{}'.format(project, model)
+    if version is not None:
+        name += '/versions/{}'.format(version)
+
+    response = service.projects().predict(
+        name=name,
+        body={'instances': [
+            {key: {'b64': base64.b64encode(example_bytes)}}
+            for example_bytes in example_bytes_list
+        ]}
+    ).execute()
+    if 'error' in response:
+        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():
+        if isinstance(data, str) or isinstance(data, unicode):
+            feature_dict[key] = tf.train.Feature(
+                bytes_list=tf.train.BytesList(value=[str(data)]))
+        elif isinstance(data, int) or isinstance(data, float):
+            feature_dict[key] = tf.train.Feature(
+                float_list=tf.train.FloatList(value=[data]))
+    return tf.train.Example(
+        features=tf.train.Features(
+            feature=feature_dict
+        )
+    ).SerializeToString()
+# [END census_to_example_bytes]
+
+
+# [START predict_from_files]
+def predict_from_files(project,
+                       model,
+                       files,
+                       version=None,
+                       force_tfrecord=False):
+    import json
+    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__)

ml_engine/online_prediction/resources/test.json

@@ -0,0 +1 @@
+{"age": 25, "workclass": " Private", "education": " 11th", "education_num": 7, "marital_status": " Never-married", "occupation": " Machine-op-inspct", "relationship": " Own-child", "race": " Black", "gender": " Male", "capital_gain": 0, "capital_loss": 0, "hours_per_week": 40, "native_country": " United-States"}