doc: clarify how to use parameter servers with distributed MXNet training

doc/using_mxnet.rst

@@ -202,7 +202,7 @@ It is good practice to save the best model after each training epoch,
 so that you can resume a training job if it gets interrupted.
 This is particularly important if you are using Managed Spot training.
 
-To save MXNet model checkpoints, do the following in your training script: 
+To save MXNet model checkpoints, do the following in your training script:
 
 * Set the ``CHECKPOINTS_DIR`` environment variable and enable checkpoints.
 
@@ -213,7 +213,7 @@ To save MXNet model checkpoints, do the following in your training script:
 
 * Make sure you are emitting a validation metric to test the model. For information, see `Evaluation Metric API <https://mxnet.incubator.apache.org/api/python/metric/metric.html>`_.
 * After each training epoch, test whether the current model performs the best with respect to the validation metric, and if it does, save that model to ``CHECKPOINTS_DIR``.
-   
+
    .. code:: python
 
      if checkpoints_enabled and current_host == hosts[0]:
@@ -224,7 +224,7 @@ To save MXNet model checkpoints, do the following in your training script:
                 trainer.save_states(CHECKPOINTS_DIR + '/%.4f-cifar10-%d.states'%(best_accuracy, epoch))
 
 For a complete example of an MXNet training script that impelements checkpointing, see https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/mxnet_gluon_cifar10/cifar10.py.
-   
+
 
 Updating your MXNet training script
 -----------------------------------
@@ -331,7 +331,7 @@ The following code sample shows how you train a custom MXNet script "train.py".
                                              'learning-rate': 0.1})
     mxnet_estimator.fit('s3://my_bucket/my_training_data/')
 
-For more information about the sagemaker.mxnet.MXNet estimator, see `sagemaker.mxnet.MXNet Class`_. 
+For more information about the sagemaker.mxnet.MXNet estimator, see `sagemaker.mxnet.MXNet Class`_.
 
 
 
@@ -370,15 +370,19 @@ fit Optional arguments
 Distributed training
 ====================
 
-When writing a distributed training script, use an MXNet kvstore to store and share model parameters.
+If you want to use parameter servers for distributed training, set the following parameter in your ``MXNet`` constructor:
+
+.. code:: python
+
+    distributions={'parameter_server': {'enabled': True}}
+
+Then, when writing a distributed training script, use an MXNet kvstore to store and share model parameters.
 During training, SageMaker automatically starts an MXNet kvstore server and scheduler processes on hosts in your training job cluster.
 Your script runs as an MXNet worker task, with one server process on each host in your cluster.
 One host is selected arbitrarily to run the scheduler process.
 
 To learn more about writing distributed MXNet programs, please see `Distributed Training <https://mxnet.incubator.apache.org/versions/master/faq/distributed_training.html>`__ in the MXNet docs.
 
-
-
 *******************
 Deploy MXNet models
 *******************

src/sagemaker/mxnet/estimator.py

@@ -93,9 +93,9 @@ def __init__(
                     123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
                     custom-image:latest.
 
-                distributions (dict): A dictionary with information on how to run distributed
-                    training (default: None).
-            distributions:
+            distributions (dict): A dictionary with information on how to run distributed
+                training (default: None). To have parameter servers launched for training,
+                set this value to be ``{'parameter_server': {'enabled': True}}``.
             **kwargs: Additional kwargs passed to the
                 :class:`~sagemaker.estimator.Framework` constructor.
         """