Update version, clean up ToC, add more detail to section on inspectin… (#2947)

* Update version, clean up ToC, add more detail to section on inspecting graphs...

* Minor adjustments to version module
* Move distributed training page to extras since there's not much there
* Fix links that break when doing that
* Consistent casing on section titles
* Orphan governance page so we don't have blank/empty links
* Update release notes with new version module structure

* PR feedback

doc/sphinx/conf.py

@@ -73,11 +73,11 @@ author = 'Intel Corporation'
 # built documents.
 #
 # The short X.Y version.
-version = '0.19'
+version = '0.20'
 
 # The Documentation full version, including alpha/beta/rc tags. Some features
 # available in the latest code will not necessarily be documented first
-release = 'rc4'
+release = '0.20.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -121,23 +121,20 @@ else:
 
     # borrow this from the zephyr docs theme
     html_context = {
-        # 'show_license': html_show_license, we have a custom footer to attribute WTD and Sphinx contributors
-        # so we do not enable this
+        # 'show_license': html_show_license, we have a custom footer to attribute 
+        # RTD, WTD, and Sphinx contributors; so we do not enable this
         'docs_title': docs_title,
         'is_release': is_release,
         'theme_logo_only': False,
         'current_version': version,
-        'versions': ( ("latest", "/"),
-                     ("0.19.0", "/0.19.0/"),
+        'versions': ( ("latest", "../"),
+                     ("0.19.0", "/0.19.0/"), #not yet sure how we'll do this
                      ("0.18.0", "/0.18.0/"),
                      ("0.17.0", "/0.17.0/"),
                      ("0.16.0", "/0.16.0/"),
-                     ("0.15.0", "/0.15.0/"),
                     )
     }
 
-
-
 html_logo = '../ngraph_theme/static/favicon.ico'
 
 # The name of an image file (within the static path) to use as favicon of the
@@ -165,6 +162,25 @@ html_sidebars = {
 }
 
 
+# Custom added feature to allow redirecting old URLs
+#
+# list of tuples (old_url, new_url) for pages to redirect
+# (URLs should be relative to document root, only)
+html_redirect_pages = [
+        ('backends', 'backend-support/index'),
+        ('core/core', 'core/overview.rst'),
+        ('core/fusion', 'core/fusion/index'),
+        ('frameworks/mxnet', 'frameworks/mxnet_intg.rst'),
+        ('frameworks/onnx', 'frameworks/onnx_intg.rst'),
+        ('frameworks/tensorflow', 'frameworks/tensorflow_connect.rst'),
+        ('frameworks/paddle', 'frameworks/paddle_integ.rst'),
+        ('inspection/inspection', 'inspection/index'),
+        ('releases/release-notes', 'releases/index'),
+        # ('getting_started/getting_starting', 'getting_started/index'),
+        ('project/project', 'project/index'),
+        ('python_api/', 'python_api/index'),
+]
+
 # -- Options for HTMLHelp output ------------------------------------------
 
 # Output file base name for HTML help builder.
@@ -227,7 +243,6 @@ rst_epilog = u"""
 .. |codename| replace:: Intel nGraph
 .. |project| replace:: Intel nGraph Library
 .. |InG| replace:: Intel® nGraph
-.. |nGl| replace:: nGraph library
 .. |copy|   unicode:: U+000A9 .. COPYRIGHT SIGN
    :ltrim:
 .. |deg|    unicode:: U+000B0 .. DEGREE SIGN
@@ -239,7 +254,7 @@ rst_epilog = u"""
 .. |trade|  unicode:: U+02122 .. TRADEMARK SIGN
    :ltrim:
 .. |reg|    unicode:: U+000AE .. REGISTERED TRADEMARK SIGN
-   :ltrim:
+   :ltrim:   
 
 """
 

doc/sphinx/ngraph_theme/ngversions.html

   <div class="rst-versions" data-toggle="rst-versions" role="note" aria-label="versions">
     <span class="rst-current-version" data-toggle="rst-current-version">
       <span class="docvs">nGraph Compiler stack</span>
-      v: {{ version }}           <dd><a href="../docs/latest">{{release}}</a></dd>
-      <span class="fa fa-caret-down"></span>
+      v: {{ version }}
+      <span class="▼"></span>
     </span>
     <div class="rst-other-versions">
       <dl>
         <dt>{{ _('Previous Verified Versions') }}</dt>
         <dd><!-- Until our https://docs.ngraph.ai/ publishing is set up, we link to GitHub -->  
           <ul>
-           <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.18.1">0.18</a></li>
+           <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.19.0-rc.2">0.19.0-rc.2</a></li>
+           <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.18.1">0.18.1</a></li>
            <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.17.0-rc.1">0.17.0-rc.1</a></li>
            <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.16.0-rc.3">0.16.0-rc.3</a></li>
            <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.16.0-rc.2">0.16.0-rc.2</a></li>

doc/sphinx/source/buildlb.rst

@@ -23,7 +23,7 @@ packages and prerequisites:
    :widths: 25, 15, 25, 20, 25
    :escape: ~
 
-   CentOS 7.4 64-bit, GCC 4.8, CMake 3.5.0, supported, ``wget zlib-devel ncurses-libs ncurses-devel patch diffutils gcc-c++ make git perl-Data-Dumper`` 
+   CentOS 7.4 64-bit, GCC 4.8, CMake 3.9.0, supported, ``wget zlib-devel ncurses-libs ncurses-devel patch diffutils gcc-c++ make git perl-Data-Dumper`` 
    Ubuntu 16.04 or 18.04 (LTS) 64-bit, Clang 3.9, CMake 3.5.1 + GNU Make, supported, ``build-essential cmake clang-3.9 clang-format-3.9 git curl zlib1g zlib1g-dev libtinfo-dev unzip autoconf automake libtool``
    Clear Linux\* OS for Intel® Architecture version 28880, Clang 8.0, CMake 3.14.2, experimental, bundles ``machine-learning-basic c-basic python-basic python-basic-dev dev-utils``
 
@@ -58,7 +58,7 @@ the ``CMakeLists.txt`` file for other experimental options' details:
    -- NGRAPH_NOP_ENABLE:               ON
    -- NGRAPH_GPUH_ENABLE:              OFF
    -- NGRAPH_GENERIC_CPU_ENABLE:       OFF
-   -- NGRAPH_DEBUG_ENABLE:             OFF
+   -- NGRAPH_DEBUG_ENABLE:             OFF  # Set to "ON" to enable logging
    -- NGRAPH_ONNX_IMPORT_ENABLE:       OFF
    -- NGRAPH_DEX_ONLY:                 OFF
    -- NGRAPH_CODE_COVERAGE_ENABLE:     OFF

doc/sphinx/source/conf.py

@@ -73,11 +73,11 @@ author = 'Intel Corporation'
 # built documents.
 #
 # The short X.Y version.
-version = '0.19'
+version = '0.20'
 
 # The Documentation full version, including alpha/beta/rc tags. Some features
 # available in the latest code will not necessarily be documented first
-release = '0.19.0'
+release = '0.20.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

doc/sphinx/source/core/constructing-graphs/import.rst

@@ -26,8 +26,7 @@ usually named ``<some_model>.onnx`` or ``<some_model>.onnx.pb``. These
    an ``.onnx.pb`` formatted file, you should be able to run the inference without
    needing to dig into anything from the "Frameworks" sections. You will, however, 
    need to have completed the steps outlined in our :doc:`../../buildlb` guide.  
-   If you intend to build nGraph for distributed-training, you will need 
-   to follow instructions on the documentation for :doc:`../../distr/index`.
+
 
 To demonstrate functionality, we'll use an already-serialized CIFAR10 model 
 trained via ResNet20. Remember that this model has already been trained and 

doc/sphinx/source/distr/index.rst

-
-.. distr/index.rst:
-
-################################
-Distributed training with nGraph
-################################
-
-
-.. important:: Distributed training is not officially supported as of version
-   |version|; however, some configuration options have worked for nGraph 
-   devices in testing environments.
-
-
-How? (Generic frameworks)
-=========================
-
-* :doc:`../core/constructing-graphs/distribute-train`
-
-To synchronize gradients across all workers, the essential operation for data
-parallel training, due to its simplicity and scalability over parameter servers,
-is ``allreduce``. The AllReduce op is one of the nGraph Library’s core ops. To
-enable gradient synchronization for a network, we simply inject the AllReduce op
-into the computation graph, connecting the graph for the autodiff computation
-and optimizer update (which then becomes part of the nGraph graph). The
-nGraph Backend will handle the rest.
-
-Data scientists with locally-scalable rack or cloud-based resources will likely
-find it worthwhile to experiment with different modes or variations of
-distributed training. Deployments using nGraph Library with supported backends
-can be configured to train with data parallelism and will soon work with model
-parallelism. Distributing workloads is increasingly important, as more data and
-bigger models mean the ability to :doc:`../core/constructing-graphs/distribute-train`
-work with larger and larger datasets, or to work with models having many layers
-that aren't designed to fit to a single device.
-
-Distributed training with data parallelism splits the data and each worker
-node has the same model; during each iteration, the gradients are aggregated
-across all workers with an op that performs "allreduce", and applied to update
-the weights.
-
-Using multiple machines helps to scale and speed up deep learning. With large 
-mini-batch training, one could train ResNet-50 with Imagenet-1k data to the
-*Top 5* classifier in minutes using thousands of CPU nodes. See
-`arxiv.org/abs/1709.05011`_.
-
-
-Future work
-===========
-
-More communication ops support is in the works. See also:  
-:doc:`../../core/passes/list-of-passes`. 
-
-
-.. _arxiv.org/abs/1709.05011: https://arxiv.org/format/1709.05011
-.. _based on the synchronous: https://arxiv.org/format/1602.06709 

doc/sphinx/source/index.rst

@@ -72,34 +72,20 @@ boost compared to native implementations. For a high-level overview, see the
    backend-support/cpp-api.rst
 
 
-.. toctree::
-   :maxdepth: 1
-   :caption: Distributed Training
-
-   distr/index.rst
-
-
 .. toctree::
    :maxdepth: 1
    :caption: Inspecting Graphs
 
    inspection/index.rst
 
-.. toctree::
-   :maxdepth: 1
-   :caption: Tutorials
-
-   tutorials/index.rst
-
 
 .. toctree::
    :maxdepth: 1
    :caption: Project Metadata
 
-   project/introduction.rst
    project/release-notes.rst
+   project/introduction.rst
    project/contribution-guide.rst
-   project/governance.rst
    project/doc-contributor-README.rst
    project/index.rst
    project/extras.rst 

doc/sphinx/source/inspection/index.rst

@@ -3,18 +3,30 @@
 Visualization Tools
 ###################
 
-nGraph provides serialization and deserialization facilities along with the 
-ability to create image formats. When visualization is enabled, a ``dot`` file 
-is generated, along with a ``png``. The default can be adjusted by setting the 
+nGraph provides serialization and deserialization facilities, along with the 
+ability to create image formats or a PDF. 
+
+When visualization is enabled, a ``dot`` file gets generated, along with a 
+``png``. The default can be adjusted by setting the 
 ``NGRAPH_VISUALIZE_TREE_OUTPUT_FORMAT`` flag to another format, like PDF. 
 
-Note: Large graphs are usually not legible with formats like PDF.  
+.. note:: Large graphs are usually not legible with formats like PDF.  
+
+Large graphs may require additional work to get into a human-readable format. 
+On the back end, very long edges will need to be cut to make (for example) a 
+hard-to-render training graph tractable. This can be a tedious process, so 
+incorporating the help of a rendering engine or third-party tool like those 
+listed below may be useful.  
+
 
-Large graphs may require additional work to get into a human-readable format. We 
-have provided a script to convert the `most common default output`_, nGraph JSON,
-to an output that is better able to handle detailed graphs; however, we do not 
-offer user support for this script. After running the script, you should have a 
-``.graphml`` that can be imported and inspected with third-party tools like: 
+.. Additional scripts
+.. ==================
+
+.. We have provided a script to convert the `most common default output`_, nGraph 
+.. ``JSON``, to an output that is better able to handle detailed graphs; however, 
+.. we do not offer user support for this script. The script will produce a 
+.. ``.graphml`` file that can be imported and inspected with third-party tools 
+.. like: 
 
 #. `Gephi`_
 
@@ -23,9 +35,9 @@ offer user support for this script. After running the script, you should have a
 .. #. `Netron`_ support tentatively planned to come soon
 
 
-.. _CMakeLists.txt: https://github.com/NervanaSystems/ngraph/blob/master/CMakeLists.txt
-.. _most common default output: https://github.com/NervanaSystems/ngraph/contrib/tools/graphml/ngraph_json_to_graphml.py
-.. _Netron: https://github.com/lutzroeder/netron/blob/master/README.md
-.. _Gephi: https://gephi.org
-.. _Cytoscape: https://cytoscape.org
-
+.. _CMakeLists.txt: https:github.com/NervanaSystems/ngraph/blob/master/CMakeLists.txt
+.. _most common default output: https:github.com/NervanaSystems/ngraph/contrib/tools/graphml/ngraph_json_to_graphml.py
+.. _visualize_tree.cpp: https://github.com/NervanaSystems/ngraph/blob/master/src/ngraph/pass/visualize_tree.cpp
+.. _Netron: https:github.com/lutzroeder/netron/blob/master/README.md
+.. _Gephi: https:gephi.org
+.. _Cytoscape: https:cytoscape.org

doc/sphinx/source/project/contribution-guide.rst

 .. contribution-guide:
 
 ##################
-Contribution Guide
+Contribution guide
 ##################
 
 License

doc/sphinx/source/project/extras.rst

@@ -5,6 +5,16 @@
 Extras
 #######
 
+
+* :ref:`homomorphic_encryption`
+* :ref:`distributed_training`
+
+This section contains extra tools and tips for working with up-and-coming 
+features of the nGraph Compiler stack.
+
+
+.. _homomorphic_encryption:
+
 Homomorphic Encryption (HE)
 ===========================
 
@@ -20,9 +30,53 @@ Homomorphic Encryption (HE)
 
 
 
+.. _distributed_training:
+
+Distributed training with nGraph
+================================
+
+.. important:: Distributed training is not officially supported as of version
+   |version|; however, some configuration options have worked for nGraph 
+   devices in testing environments.
+
+
+How? (Generic frameworks)
+-------------------------
+
+See also: :doc:`../core/constructing-graphs/distribute-train`
+
+To synchronize gradients across all workers, the essential operation for data
+parallel training, due to its simplicity and scalability over parameter servers,
+is ``allreduce``. The AllReduce op is one of the nGraph Library’s core ops. To
+enable gradient synchronization for a network, we simply inject the AllReduce op
+into the computation graph, connecting the graph for the autodiff computation
+and optimizer update (which then becomes part of the nGraph graph). The
+nGraph Backend will handle the rest.
+
+Data scientists with locally-scalable rack or cloud-based resources will likely
+find it worthwhile to experiment with different modes or variations of
+distributed training. Deployments using nGraph Library with supported backends
+can be configured to train with data parallelism and will soon work with model
+parallelism. Distributing workloads is increasingly important, as more data and
+bigger models mean the ability to :doc:`../core/constructing-graphs/distribute-train`
+work with larger and larger datasets, or to work with models having many layers
+that aren't designed to fit to a single device.
+
+Distributed training with data parallelism splits the data and each worker
+node has the same model; during each iteration, the gradients are aggregated
+across all workers with an op that performs "allreduce", and applied to update
+the weights.
+
+Using multiple machines helps to scale and speed up deep learning. With large 
+mini-batch training, one could train ResNet-50 with Imagenet-1k data to the
+*Top 5* classifier in minutes using thousands of CPU nodes. See
+`arxiv.org/abs/1709.05011`_.
+
+
 
 .. _nGraph Bridge repo: https://github.com/tensorflow/ngraph-bridge
 .. _Intel HE_transformer: https://github.com/NervanaSystems/he-transformer
 .. _Blog post: https://www.intel.ai/he-transformer-for-ngraph-enabling-deep-learning-on-encrypted-data/
-
-.. _examples: https://github.com/NervanaSystems/he-transformer#examples
\ No newline at end of file
+.. _examples: https://github.com/NervanaSystems/he-transformer#examples
+.. _arxiv.org/abs/1709.05011: https://arxiv.org/format/1709.05011
+.. _based on the synchronous: https://arxiv.org/format/1602.06709 

doc/sphinx/source/project/governance.rst

 .. governance: 
 
+:orphan:
+
 
 Governance
 ##########
\ No newline at end of file

doc/sphinx/source/project/index.rst

@@ -10,7 +10,6 @@ This section contains documentation about the project and how to contribute.
 .. toctree::
    :maxdepth: 2
 
-   release-notes.rst
    about.rst
    contribution-guide.rst
    governance.rst

doc/sphinx/source/project/release-notes.rst

@@ -3,17 +3,20 @@
 Release Notes
 #############
 
-
-
 |version|
-|release|
 
 For downloads formatted as ``.zip`` and ``tar.gz``, see https://github.com/NervanaSystems/ngraph/releases; 
 
 .. important:: Pre-releases (``-rc-0.*``) have newer features, and are less stable.  
 
-CHANGELOG |release|
-===================
+
+Changelog 
+=========
+
+0.19
+----
+
+**Download** `0.19.0-rc.2`_
 
 + More dynamic shape preparation
 + Distributed interface factored out
@@ -30,12 +33,12 @@ CHANGELOG |release|
 + Update doxygen to be friendlier to frontends
 
 
-Changelog 
-=========
 
+0.18
+----
+
+**Download** `0.18.1`_
 
-nGraph v0.18.1
---------------
 
 + Python formatting issue
 + mkl-dnn work-around
@@ -46,22 +49,25 @@ nGraph v0.18.1
 + More fusions
 
 
-nGraph v0.17.0-rc.0
--------------------
+0.17
+----
+
+**Download** `0.17.0-rc.1`_
 
 + Allow negative padding in more places
 + Add code generation for some quantized ops
 + Preliminary dynamic shape support
 + initial distributed ops
++ Pad op takes CoordinateDiff instead of Shape pad values to allow for negative padding.
 
-Recent API Changes
-~~~~~~~~~~~~~~~~~~
 
-+ Pad op takes CoordinateDiff instead of Shape pad values to allow for negative padding.
+0.16
+----
 
+* **Download**: `0.16.0-rc.3`_
+* **Download** `0.16.0-rc.2`_
+* **Download** `0.16.0-rc.1`_
 
-nGraph v0.16.0-rc.3
--------------------
 
 + NodeInput and NodeOutput classes prepare for simplifications of Node
 + Test improvements
@@ -70,3 +76,10 @@ nGraph v0.16.0-rc.3
 + Fix memory leak
 + Concat optimization
 + Doc updates
+
+.. _0.19.0-rc.2: https://github.com/NervanaSystems/ngraph/releases/tag/v0.19.0-rc.2_
+.. _0.18.1: https://github.com/NervanaSystems/ngraph/releases/tag/v0.18.1_
+.. _0.17.0-rc.1: `https://github.com/NervanaSystems/ngraph/releases/tag/v0.17.0-rc.1
+.. _0.16.0-rc.3: https://github.com/NervanaSystems/ngraph/releases/tag/v0.16.0-rc.3
+.. _0.16.0-rc.2: https://github.com/NervanaSystems/ngraph/releases/tag/v0.16.0-rc.2
+.. _0.16.0-rc.1: https://github.com/NervanaSystems/ngraph/releases/tag/v0.16.0-rc.1

doc/sphinx/source/tutorials/index.rst

-.. guides/index:
-
-.. This will hold the organization of the tutorials we put on ngraph.ai  
-
-   .. it will need to be organized in a way that is navigable for the many kinds of frameworks and backends we support in the "Compiler stack".  It will need to be workable with a sitemap structure. The initial example is for the latest nGraph-TensorFlow bridge.  
-
-#######
-Guides
-#######
-
-Coming soon 
-
-.. toctree::
-   :maxdepth: 1