Update changelog and README.

Author: Ignacio Quintero

CHANGELOG.rst

@@ -2,6 +2,10 @@
 CHANGELOG
 =========
 
+1.2.5-dev
+=========
+* feature: Local Mode: add support for local training data using file://
+
 1.2.4
 =====
 

README.rst

@@ -148,7 +148,7 @@ Local Mode
 ~~~~~~~~~~
 
 The SageMaker Python SDK now supports local mode, which allows you to create TensorFlow, MXNet and BYO estimators and
-deploy to your local environment.  This is a great way to test your deep learning script before running in
+deploy to your local environment. This is a great way to test your deep learning script before running in
 SageMaker's managed training or hosting environments.
 
 We can take the example in  `Estimator Usage <#estimator-usage>`__ , and use either ``local`` or ``local_gpu`` as the
@@ -166,6 +166,9 @@ instance type.
     # In Local Mode, fit will pull the MXNet container docker image and run it locally
     mxnet_estimator.fit('s3://my_bucket/my_training_data/')
 
+    # Alternatively, you can train using data in your local file system. This is only supported in Local mode.
+    mxnet_estimator.fit('file:///tmp/my_training_data')
+
     # Deploys the model that was generated by fit() to local endpoint in a container
     mxnet_predictor = mxnet_estimator.deploy(initial_instance_count=1, instance_type='local')
 
@@ -184,7 +187,7 @@ For detailed examples of running docker in local mode, see:
 A few important notes:
 
 - Only one local mode endpoint can be running at a time
-- Since the data are pulled from S3 to your local environment, please ensure you have sufficient space.
+- Since the data is pulled from S3 to your local environment, please ensure you have sufficient space.
 - If you run into problems, this is often due to different docker containers conflicting.  Killing these containers and re-running often solves your problems.
 - Local Mode requires docker-compose and `nvidia-docker2 <https://github.com/NVIDIA/nvidia-docker>`__ for ``local_gpu``.
 - Distributed training is not yet supported for ``local_gpu``.