Add Local Mode Batch Inference support.

CHANGELOG.rst

@@ -2,15 +2,18 @@
 CHANGELOG
 =========
 
-======
+1.11.3dev
+=========
+
+* feature: Local Mode: Add support for Batch Inference
+
 1.11.2
 ======
 
 * enhancement: Enable setting VPC config when creating/deploying models
 * enhancement: Local Mode: accept short lived credentials with a warning message
 * bug-fix: Local Mode: pass in job name as parameter for training environment variable
 
-=======
 1.11.1
 ======
 

README.rst

@@ -226,6 +226,27 @@ Here is an end-to-end example:
     predictor.delete_endpoint()
 
 
+If you don't want to deploy your model locally, you can also choose to perform a Local Batch Transform Job. This is
+useful if you want to test your container before creating a Sagemaker Batch Transform Job. Note that the performance
+will not match Batch Transform Jobs hosted on SageMaker but it is still a useful tool to ensure you have everything
+right or if you are not dealing with huge amounts of data.
+
+Here is an end-to-end example:
+
+.. code:: python
+
+    from sagemaker.mxnet import MXNet
+
+    mxnet_estimator = MXNet('train.py',
+                            train_instance_type='local',
+                            train_instance_count=1)
+
+    mxnet_estimator.fit('file:///tmp/my_training_data')
+    transformer = mxnet_estimator.transformer(1, 'local', assemble_with='Line', max_payload=1)
+    transformer.transform('s3://my/transform/data, content_type='text/csv', split_type='Line')
+    transformer.wait()
+
+
 For detailed examples of running Docker in local mode, see:
 
 - `TensorFlow local mode example notebook <https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_local_mode_mnist.ipynb>`__.

src/sagemaker/amazon/common.py

@@ -153,7 +153,7 @@ def write_spmatrix_to_sparse_tensor(file, array, labels=None):
 def read_records(file):
     """Eagerly read a collection of amazon Record protobuf objects from file."""
     records = []
-    for record_data in _read_recordio(file):
+    for record_data in read_recordio(file):
         record = Record()
         record.ParseFromString(record_data)
         records.append(record)
@@ -183,7 +183,7 @@ def _write_recordio(f, data):
     f.write(padding[pad])
 
 
-def _read_recordio(f):
+def read_recordio(f):
     while(True):
         try:
             read_kmagic, = struct.unpack('I', f.read(4))