Leona/doc distr (#2417)

* Update docs for PR 2353

* Fix missing flag designator

* Refer to the Intel® MLSL properly as a spelled-out acronym for first instance

* Fix missing compilation designator

* Finalize editing on CPU backend

* Update doc version to be even with what is in master branch

* Removed typo from docu build instruction command line cd command probably accidental

* Add PR feedback to doc

doc/sphinx/source/conf.py

@@ -73,14 +73,15 @@ author = 'Intel Corporation'
 # built documents.
 #
 # The short X.Y version.
-version = '0.15'
+version = '0.14'
 
 # The Documentation full version, including alpha/beta/rc tags. Some features
 # available in the latest code will not necessarily be documented first
-release = '0.15.0-rc0'
+release = '0.14.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
+
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.

doc/sphinx/source/howto/distribute-train.rst

 .. howto/distribute-train.rst 
 
 
-Train using multiple nGraph CPU backends with data parallel 
-===========================================================
+Distribute training across multiple nGraph backends 
+===================================================
 
 In the :doc:`previous section <../howto/derive-for-training>`, we described the 
 steps needed to create a "trainable" nGraph model. Here we demonstrate how to 
 train a data parallel model by distributing the graph to more than one device.
 
-As of release version 0.12, the default build is with OpenMPI.  To use the 
-`Intel MLSL`_ library, set the following compilation flag at build time: 
+These options are currently supported for available backends:
 
-``-DNGRAPH_DISTRIBUTED_ENABLE=TRUE``.
+* Use ``-DNGRAPH_DISTRIBUTED_OMPI_ENABLE=TRUE`` to enable distributed training 
+  with OpenMPI. Use of this flag requires that OpenMPI be a pre-existing library 
+  in the system. If it's not present on the system, install `OpenMPI`_ version 
+  ``2.1.1`` or later before running the compile. 
 
-To deploy data-parallel training on backends supported by nGraph API, the 
-``AllReduce`` op should be added after the steps needed to complete the 
-:doc:`backpropagation <../howto/derive-for-training>`.
+* Use ``-DNGRAPH_DISTRIBUTED_MLSL_ENABLE=TRUE`` to enable the option for 
+  :abbr:`Intel® Machine Learning Scaling Library (MLSL)` for Linux* OS:
+
+  .. important:: The Intel® MLSL option applies to Intel® Architecture CPUs 
+     (``CPU``) and ``Interpreter`` backends only. For all other backends, 
+     ``OpenMPI`` is presently the only supported option. We recommend the 
+     use of `Intel MLSL` for CPU backends to avoid an extra download step.
+
+Finally, to run the training using two nGraph devices, invoke 
+
+.. code-block:: console 
+
+   $ mpirun 
+
+To deploy data-parallel training, the ``AllReduce`` op should be added after the 
+steps needed to complete the :doc:`backpropagation <../howto/derive-for-training>`; 
+the new code is highlighted below: 
 
 .. literalinclude:: ../../../examples/mnist_mlp/dist_mnist_mlp.cpp
    :language: cpp
    :lines: 180-196
    :emphasize-lines: 8-11
 
-We need to initialize and finalize distributed training with ``Distributed`` object;
-see the `full raw code`_. 
-
-Finally, to run the training using two nGraph devices, invoke :command:`mpirun` which 
-is distributed with `Intel MLSL`_ library.  This will launch two nGraph CPU backends.
-
+See the `full code`_ in the ``examples`` folder ``/doc/examples/mnist_mlp/dist_mnist_mlp.cpp``. 
 
 .. code-block:: console 
 
@@ -35,4 +46,5 @@ is distributed with `Intel MLSL`_ library.  This will launch two nGraph CPU back
 
 
 .. _Intel MLSL: https://github.com/intel/MLSL/releases
-.. _full raw code: https://github.com/NervanaSystems/ngraph/blob/master/doc/examples/mnist_mlp/dist_mnist_mlp.cpp 
+.. _OpenMPI: https://www.open-mpi.org/software/ompi/v2.1/  
+.. _full code: https://github.com/NervanaSystems/ngraph/blob/master/doc/examples/mnist_mlp/dist_mnist_mlp.cpp 

doc/sphinx/source/project/doc-contributor-README.rst

@@ -127,7 +127,7 @@ To build documentation locally, run:
 
    .. code-block:: console
 
-      $ sudo apt-get install python3-sphinxcd
+      $ sudo apt-get install python3-sphinx
       $ pip3 install [-I] Sphinx==1.7.5 [--user] 
       $ pip3 install [-I] breathe numpy [--user]
       $ cd doc/sphinx/