Change 'distribution' to 'distributions' in documentation

src/sagemaker/mxnet/README.rst

@@ -207,15 +207,15 @@ If you were previously relying on the default save method, you can now import on
 
         save(args.model_dir, model)
 
-Lastly, if you were relying on the container launching a parameter server for use with distributed training, you must now set ``distribution`` to the following dictionary when creating an MXNet estimator:
+Lastly, if you were relying on the container launching a parameter server for use with distributed training, you must now set ``distributions`` to the following dictionary when creating an MXNet estimator:
 
 .. code:: python
 
     from sagemaker.mxnet import MXNet
 
     estimator = MXNet('path-to-distributed-training-script.py',
                       ...,
-                      distribution={'parameter_server': {'enabled': True}})
+                      distributions={'parameter_server': {'enabled': True}})
 
 
 Using third-party libraries
@@ -321,7 +321,7 @@ The following are optional arguments. When you create an ``MXNet`` object, you c
    framework_version and py_version. Refer to: `SageMaker MXNet Docker Containers
    <#sagemaker-mxnet-docker-containers>`_ for details on what the Official images support
    and where to find the source code to build your custom image.
--  ``distribution`` For versions 1.3 and above only.
+-  ``distributions`` For versions 1.3 and above only.
    Specifies information for how to run distributed training.
    To launch a parameter server during training, set this argument to:
 

src/sagemaker/mxnet/estimator.py

@@ -67,7 +67,7 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, py_versio
                     Examples:
                         123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
                         custom-image:latest.
-             distribution (dict): A dictionary with information on how to run distributed training
+             distributions (dict): A dictionary with information on how to run distributed training
                 (default: None).
             **kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Framework` constructor.
         """

src/sagemaker/tensorflow/estimator.py

@@ -199,7 +199,7 @@ def __init__(self, training_steps=None, evaluation_steps=None, checkpoint_path=N
                         custom-image:latest.
             script_mode (bool): If set to True will the estimator will use the Script Mode containers (default: False).
                 This will be ignored if py_version is set to 'py3'.
-            distribution (dict): A dictionary with information on how to run distributed training
+            distributions (dict): A dictionary with information on how to run distributed training
                 (default: None). Currently we only support distributed training with parameter servers. To enable it
                 use the following setup:
                     {