Improved documentation and tests about input and output functions

README.rst

@@ -1365,11 +1365,11 @@ An example of ``input_fn`` for the content-type "application/python-pickle" can
 
     import numpy as np
 
-    def input_fn(data, content_type):
-        """An input_fn that loads a pickled numpy array"""
+    def input_fn(serialized_input, content_type):
+        """An input_fn that loads a pickled object"""
         if request_content_type == "application/python-pickle":
-            array = np.load(StringIO(request_body))
-            return array.reshape(model.data_shpaes[0])
+            deserialized_input = pickle.loads(serialized_input)
+            return deserialized_input
         else:
             # Handle other content-types here or raise an Exception
             # if the content type is not supported.
@@ -1384,15 +1384,18 @@ An example of ``output_fn`` for the accept type "application/python-pickle" can
 
     import numpy as np
 
-    def output_fn(data, accepts):
-        """An output_fn that dumps a pickled numpy as response"""
+    def output_fn(prediction_result, accepts):
+        """An output_fn that dumps a pickled object as response"""
         if request_content_type == "application/python-pickle":
-            return np.dumps(data)
+            return np.dumps(prediction_result)
         else:
             # Handle other content-types here or raise an Exception
             # if the content type is not supported.
             pass  
 
+A example with ``input_fn`` and ``output_fn`` above can be found in
+`here <https://github.com/aws/sagemaker-python-sdk/blob/master/tests/data/cifar_10/source/resnet_cifar_10.py#L143>`_.
+
 SageMaker TensorFlow Docker containers
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

tests/data/cifar_10/source/resnet_cifar_10.py

@@ -2,6 +2,8 @@
 from __future__ import division
 from __future__ import print_function
 
+import pickle
+
 import resnet_model
 import tensorflow as tf
 
@@ -106,21 +108,19 @@ def model_fn(features, labels, mode, params):
 
 
 def serving_input_fn(hyperpameters):
-    feature_spec = {INPUT_TENSOR_NAME: tf.FixedLenFeature(dtype=tf.float32, shape=(32, 32, 3))}
-    return tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)()
+    inputs = {INPUT_TENSOR_NAME: tf.placeholder(tf.float32, [None, 32, 32, 3])}
+    return tf.estimator.export.ServingInputReceiver(inputs, inputs)
 
 
-def train_input_fn(training_dir, hyperpameters):
-    return input_fn(tf.estimator.ModeKeys.TRAIN,
-                    batch_size=BATCH_SIZE, data_dir=training_dir)
+def train_input_fn(training_dir, hyperparameters):
+    return _generate_synthetic_data(tf.estimator.ModeKeys.TRAIN, batch_size=BATCH_SIZE)
 
 
-def eval_input_fn(training_dir, hyperpameters):
-    return input_fn(tf.estimator.ModeKeys.EVAL,
-                    batch_size=BATCH_SIZE, data_dir=training_dir)
+def eval_input_fn(training_dir, hyperparameters):
+    return _generate_synthetic_data(tf.estimator.ModeKeys.EVAL, batch_size=BATCH_SIZE)
 
 
-def input_fn(mode, batch_size, data_dir):
+def _generate_synthetic_data(mode, batch_size):
     input_shape = [batch_size, HEIGHT, WIDTH, DEPTH]
     images = tf.truncated_normal(
         input_shape,
@@ -138,3 +138,7 @@ def input_fn(mode, batch_size, data_dir):
     labels = tf.contrib.framework.local_variable(labels, name='labels')
 
     return {INPUT_TENSOR_NAME: images}, labels
+
+
+def input_fn(serialized_data, content_type):
+    return pickle.loads(serialized_data)

tests/integ/test_tf_cifar.py

@@ -10,7 +10,10 @@
 # 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.
+import pickle
+
 import boto3
+import numpy as np
 import os
 import pytest
 
@@ -19,12 +22,22 @@
 from tests.integ import DATA_DIR, REGION
 from tests.integ.timeout import timeout_and_delete_endpoint, timeout
 
+PICKLE_CONTENT_TYPE = 'application/python-pickle'
+
 
 @pytest.fixture(scope='module')
 def sagemaker_session():
     return Session(boto_session=boto3.Session(region_name=REGION))
 
 
+class PickleSerializer(object):
+    def __init__(self):
+        self.content_type = PICKLE_CONTENT_TYPE
+
+    def __call__(self, data):
+        return pickle.dumps(data, protocol=2)
+
+
 def test_cifar(sagemaker_session):
     with timeout(minutes=15):
         script_path = os.path.join(DATA_DIR, 'cifar_10', 'source')
@@ -42,4 +55,10 @@ def test_cifar(sagemaker_session):
         print('job succeeded: {}'.format(estimator.latest_training_job.name))
 
     with timeout_and_delete_endpoint(estimator=estimator, minutes=20):
-        estimator.deploy(initial_instance_count=1, instance_type='ml.c4.xlarge')
+        predictor = estimator.deploy(initial_instance_count=1, instance_type='ml.p2.xlarge')
+        predictor.serializer = PickleSerializer()
+        predictor.content_type = PICKLE_CONTENT_TYPE
+
+        data = np.random.randn(32, 32, 3)
+        predict_response = predictor.predict(data)
+        assert len(predict_response['outputs']['probabilities']['floatVal']) == 10