Merge pull request #3486 from NervanaSystems/leona/doc_v0.25.1

Leona/doc v0.25.1

README.md

@@ -77,9 +77,7 @@ to improve it:
 * Ensure you can build the product and run all the examples with your patch.
 * In the case of a larger feature, create a test.
 * Submit a [pull request].
-* Make sure your PR passes all CI tests. Note: our [Travis-CI][build-status] service
-  runs only on a CPU backend on Linux. We will run additional tests
-  in other environments.
+* Make sure your PR passes all CI tests. Note: You can test locally with `make check`.
 * We will review your contribution and, if any additional fixes or
   modifications are necessary, may provide feedback to guide you. When
   accepted, your pull request will be merged to the repository.

doc/sphinx/ngraph_theme/ngversions.html

@@ -9,11 +9,12 @@
         <dt>{{ _('Recent 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/v0.26.0">0.26.0</a></li> -->
+           <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.25.0">0.25.0</a></li>
            <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.24.0">0.24.0</a></li>
            <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.23.0">0.23.0</a></li>
            <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.22.0">0.22.0</a></li>
            <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.21.0">0.21.0</a></li>
-           <li><a href="https://github.com/NervanaSystems/ngraph/releases/tag/v0.20.0">0.20.0</a></li>
          </ul></dd>
       </dl>
       <dl>

doc/sphinx/source/backends/index.rst

@@ -21,9 +21,9 @@ from a framework on a CPU, GPU, or ASIC; it can also be used with an
 *Interpreter* mode, which is primarily intended for testing, to analyze a 
 program, or to help a framework developer customize targeted solutions. 
 
-.. nGraph also provides a way to use the advanced tensor compiler PlaidML 
-.. as a backend; you can learn more about this backend and how to build it 
-.. from source in our documentation: :ref:`ngraph_plaidml_backend`.
+nGraph also provides a way to use the advanced tensor compiler PlaidML 
+as a backend; you can learn more about this backend and how to build it 
+from source in our documentation: :ref:`ngraph_plaidml_backend`.
 
 .. csv-table::
    :header: "Backend", "Current nGraph support", "Future nGraph support"
@@ -31,7 +31,6 @@ program, or to help a framework developer customize targeted solutions.
 
    Intel® Architecture Processors (CPUs), Yes, Yes
    Intel® Nervana™ Neural Network Processor™ (NNPs), Yes, Yes
-   NVIDIA\* CUDA (GPUs), Yes, Some 
    AMD\* GPUs, Yes, Some
 
 

doc/sphinx/source/buildlb.rst

@@ -5,7 +5,7 @@ Build and Test
 ###############
 
 * :ref:`default_ngflags`
-.. :ref:`ngraph_plaidml_backend`
+* :ref:`ngraph_plaidml_backend`
 
 There are a few common paths to take when manually building the |project| 
 from source code. Today nGraph supports various developers working on all 
@@ -161,17 +161,17 @@ The process documented here will work on CentOS 7.4.
       $ make && sudo make install 
 
 
-.. .. _ngraph_plaidml_backend:  hide this until announcement is official
+.. _ngraph_plaidml_backend:
 
-.. Building nGraph-PlaidML from source
-.. ===================================
+Building nGraph-PlaidML from source
+===================================
 
-.. The following instructions will create the ``~/ngraph_plaidml_dist`` 
-.. locally:
+The following instructions will create the ``~/ngraph_plaidml_dist`` 
+locally:
 
-.. #. Ensure you have installed the :ref:`prerequisites` for your OS.
+#. Ensure you have installed the :ref:`prerequisites` for your OS.
 
-.. #. Install the prerequisites for the backend. Our hybrid ``NGRAPH_PLAIDML``
+#. Install the prerequisites for the backend. Our hybrid ``NGRAPH_PLAIDML``
    backend works best with Python3 versions. We recommend that you use a 
    virtual environment, due to some of the difficulties that users have 
    seen when trying to install outside of a venv.
@@ -182,20 +182,20 @@ The process documented here will work on CentOS 7.4.
       $ pip install plaidml 
       $ plaidml-setup
 
-.. #. Clone the source code, create and enter your build directory:
+#. Clone the source code, create and enter your build directory:
 
    .. code-block:: console
 
       $ git clone https://github.com/NervanaSystems/ngraph.git
       $ cd ngraph && mkdir build && cd build
 
-.. #. Prepare the CMake files as follows: 
+#. Prepare the CMake files as follows: 
 
    .. code-block:: console
 
       $ cmake .. -DCMAKE_INSTALL_PREFIX=~/ngraph_plaidml_dist -DNGRAPH_CPU_ENABLE=OFF -DNGRAPH_PLAIDML_ENABLE=ON 
 
-.. #. Run :command:`make` and ``make install``. Note that if you are building 
+#. Run :command:`make` and ``make install``. Note that if you are building 
    outside a local or user path, you may need to run ``make install`` as the 
    root user.
 
@@ -210,8 +210,8 @@ The process documented here will work on CentOS 7.4.
    tests can be run when PlaidML devices are available at the machine 
    level.
 
-.. For more about working with the PlaidML backend from nGraph, see our 
-.. API documentation :doc:`backends/plaidml-ng-api/index`. 
+For more about working with the PlaidML backend from nGraph, see our 
+API documentation :doc:`backends/plaidml-ng-api/index`. 
 
 
 macOS\* development

doc/sphinx/source/conf.py

@@ -77,7 +77,7 @@ version = '0.25'
 
 # The Documentation full version, including alpha/beta/rc tags. Some features
 # available in the latest code will not necessarily be documented first
-release = '0.25.0'
+release = '0.25.1'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

doc/sphinx/source/frameworks/getting_started.rst

@@ -69,7 +69,7 @@ See also nGraph's :doc:`../python_api/index`.
 PlaidML
 =======
 
-See :ref:`ngraph_plaidml_backend` section on how to build the 
+See the :ref:`ngraph_plaidml_backend` section on how to build the 
 nGraph-PlaidML.
 
 

doc/sphinx/source/frameworks/validated/list.rst

@@ -10,7 +10,7 @@ workloads:
 * :ref:`tensorflow_valid`
 * :ref:`mxnet_valid`
 * :ref:`onnx_valid`
-* :doc:`../../project/extras/testing_latency.rst`
+* :ref:`testing_latency`
 
 .. _tensorflow_valid:
 

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

-.. contribution-guide:
+.. project/contribution-guide.rst:
+
+
+.._contribution_guide:
 
 ##################
 Contribution guide
@@ -261,5 +264,8 @@ it is automatically enforced and reduces merge conflicts.
 To contribute documentation for your code, please see the :doc:`doc-contributor-README`. 
 
 
+.. include:: doc-contributor-README.rst 
+
+
 .. _Apache 2: https://www.apache.org/licenses/LICENSE-2.0
 .. _repo wiki: https://github.com/NervanaSystems/ngraph/wiki
\ No newline at end of file

doc/sphinx/source/project/extras/testing_latency.rst

 .. project/extras/testing_latency.rst:
 
 
+.. _testing_latency:
+
 Testing latency  
 ===============
 
+.. important:: This tutorial was tested using previous versions. While it is 
+   not currently or officially supported in the latest nGraph Compiler 
+   stack |version|, some configuration options may still work.
+
+
 Many open-source DL frameworks provide a layer where experts in data science 
 can make use of optimizations contributed by machine learning engineers. Having 
 a common API benefits both: it simplifies deployment and makes it easier for ML 
 engineers working on advanced deep learning hardware to bring highly-optimized 
-performance to a wide range of models, especially in inference.  
+performance to a wide range of models, especially in inference.
 
 One DL framework with advancing efforts on graph optimizations is Apache 
 MXNet\*, where `Intel has contributed efforts showing`_ how to work with our
@@ -17,7 +24,7 @@ nGraph Compiler stack as an `experimental backend`_. Our approach provides
 optimizations **than would be available to the MXNet framework alone**, for 
 reasons outlined in our `introduction`_ documentation.  Note that the 
 MXNet bridge requires trained models only; it does not support distributed 
-training.  
+training.
 
 
 
@@ -62,7 +69,7 @@ install MXNet to the virtual environment:
 Now we're ready to use nGraph to run any model on a CPU backend. Building MXNet 
 with nGraph automatically enabled nGraph on your model scripts, and you 
 shouldn't need to do anything special. If you run into trouble, you can disable 
-nGraph by setting 
+nGraph by setting
 
 .. code-block:: console
    
@@ -81,14 +88,14 @@ Note that the nGraph-MXNet bridge supports static graphs only (dynamic graphs
 are in the works); so for this example, we begin by converting the gluon model 
 into a static graph. Also note that any model with a saved checkpoint can be 
 considered a "static graph" in nGraph. For this example, we'll presume that the 
-model is pre-trained.   
+model is pre-trained.
 
 .. literalinclude:: ../../../../examples/subgraph_snippets/mxnet-gluon-example.py
    :language: python
    :lines: 17-32
 
 
-To load the model into nGraph, we simply bind the symbol into an Executor. 
+To load the model into nGraph, we simply bind the symbol into an Executor.
 
 .. literalinclude:: ../../../../examples/subgraph_snippets/mxnet-gluon-example.py
    :language: python

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

@@ -19,32 +19,31 @@ We are pleased to announce the release of version |version|.
 Core updates for |version|
 --------------------------
 
-+ PlaidML support
-+ Double-buffering support
-+ Constant folding
-+ Support for static linking
-+ Additional ops
-+ Preliminary static linking support
+Allow DLLs that link nGraph statically to load backends
 
-Known issues
-~~~~~~~~~~~~
 
-+ No PlaidML training support
+.. important:: Pre-releases (``-rc-0.*``) have newer features, and are less stable.  
 
 
-Latest doc updates |version|-doc
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Changelog on Previous Releases
+==============================
 
-+ Instructions how to build ``NGRAPH_PLAIDML`` backend added to :doc:`../buildlb`. 
-+ Publishing interim version of doc navigation for updates at ngraph.ai
-+ GPU validations: added 5 functional TensorFlow workloads and 4 functional ONNX workloads to :doc:`../frameworks/validated/list`.
 
+0.25.0
+------
 
-.. important:: Pre-releases (``-rc-0.*``) have newer features, and are less stable.  
-
++ Better PlaidML support
++ Double-buffering support
++ Constant folding
++ Support for static linking
++ Additional ops
++ Preliminary static linking support
++ Known issue: No PlaidML training support
++ Doc: Add instructions how to build NGRAPH_PLAIDML backend
++ Published interim version of doc navigation for updates at ngraph.ai
++ GPU validations: added 5 functional TensorFlow workloads and 4 functional 
+  ONNX workloads
 
-Changelog on Previous Releases
-==============================
 
 0.24
 ----