Merge pull request #696 from NervanaSystems/leona/20_Mar

Updates to docs

doc/sphinx/source/conf.py

@@ -39,6 +39,7 @@ extensions = ['sphinx.ext.mathjax',
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
+static_path = ['static']
 
 # The suffix(es) of source filenames.
 # You can specify multiple suffix as a list of string:

doc/sphinx/source/howto/index.rst

@@ -38,13 +38,13 @@ devices, or it might mean programatically adjusting a model or the compute
 resources it requires, at an unknown or arbitray time after it has been deemed 
 to be trained well enough.
 
-To get started, we've provided a basic example for how to :doc:`execute` a 
-computation with an nGraph backend; this is analogous to a framework bridge.
+To get started, we've provided a basic example for how to :doc:`execute` with 
+an nGraph backend; this is analogous to a framework bridge.
 
 For data scientists or algorithm developers who are trying to extract specifics 
 about the state of a model at a certain node, or who want to optimize a model 
-at a more granular level, we provide an example for how to :doc:`import` a 
-model and run inference after it has been exported from a DL framework.    
+at a more granular level, we provide an example for how to :doc:`import` and 
+run inference after it has been exported from a DL framework.    
 
 This section is under development; we'll continually populate it with more 
 articles geared toward data scientists, algorithm designers, framework developers, 

doc/sphinx/source/index.rst

@@ -17,31 +17,52 @@
 nGraph library
 ###############
 
-Welcome to Intel® nGraph™, an open source C++ library and compiler. This 
-project enables modern compute platforms to run and train :abbr:`Deep Neural Network (DNN)` 
-models. It is framework neutral and supports a variety of backends used by 
-:abbr:`Deep Learning (DL)` frameworks. 
 
-.. image:: graphics/ngraph-ecosys.png
-   :width: 585px
+Welcome to nGraph™, an open-source C++ compiler library for running and 
+training :abbr:`Deep Neural Network (DNN)` models. This project is 
+framework-neutral and can target a variety of modern devices or platforms. 
 
-For this early release, we've provided :doc:`framework-integration-guides` to 
-compile and run MXNet\* and TensorFlow\*-based projects. If you already have 
-a trained model, see our section on How to :doc:`howto/import` and start working 
-with the nGraph APIs.  
+.. figure:: graphics/ngraph-ecosystem.png
+   :width: 585px   
+  
+nGraph currently supports :doc:`three popular <framework-integration-guides>` 
+frameworks for :abbr:`Deep Learning (DL)` models through what we call 
+a :term:`bridge` that can be integrated during the framework's build time. 
+For developers working with other frameworks (even those not listed above), 
+we've created a :doc:`How to Guide <howto/index>` guide so you can learn how to create 
+custom bridge code that can be used to :doc:`compile and run <howto/execute>` 
+a training model.
+
+We've recently added initial support for the ONNX format. Developers who 
+already have a "trained" model can use nGraph to bypass a lot of the 
+framework-based complexity and :doc:`howto/import` to test or run it 
+on targeted and efficient backends with our user-friendly ``ngraph_api``. 
+With nGraph, data scientists can focus on data science rather than worrying 
+about how to adapt models to train and run efficiently on different devices.
+
+
+Supported platforms
+--------------------
+
+Initially-supported backends include:
+
+* Intel® Architecture Processors (CPUs), 
+* Intel® Nervana™ Neural Network Processor™ (NNPs), and 
+* NVIDIA\* CUDA (GPUs). 
+
+Tentatively in the pipeline, we plan to add support for more backends,
+including:
+
+* :abbr:`Field Programmable Gate Arrays (FPGA)` (FPGAs)
+* Movidius 
 
 .. note:: The library code is under active development as we're continually 
    adding support for more kinds of DL models and ops, framework compiler 
    optimizations, and backends. 
 
-The nGraph library translates a framework’s representation of computations 
-into an :abbr:`Intermediate Representation (IR)` that promotes computational 
-efficiency on target hardware. Initially-supported backends include Intel 
-Architecture CPUs (``CPU``), the Intel® Nervana Neural Network Processor™ (Intel® 
-``NNP``), and NVIDIA\* GPUs. Currently-supported compiler optimizations include 
-efficient memory management and data layout abstraction. 
 
-Further project details can be found on our :doc:`project/about` page. 
+Further project details can be found on our :doc:`project/about` page, or see 
+our :doc:`install` guide for how to get started.   
 
 
 

doc/sphinx/source/project/about.rst

@@ -3,19 +3,47 @@
 About
 =====
 
-Welcome to Intel® nGraph™, an open source C++ library and compiler. This 
-project enables modern compute platforms to run and train 
-:abbr:`Deep Neural Network (DNN)` models. It is framework-neutral and supports 
-a variety of backends used by :abbr:`Deep Learning (DL)` frameworks. 
+Welcome to nGraph™, an open-source C++ compiler library for running and 
+training :abbr:`Deep Neural Network (DNN)` models. This project is 
+framework-neutral and can target a variety of modern devices or platforms. 
 
-.. figure:: ../graphics/ngraph-ecosys.png
-   :width: 585px	 
- 
-The nGraph library translates a framework’s representation of computations into 
-an :abbr:`Intermediate Representation (IR)` designed to promote computational 
-efficiency on target hardware. Initially-supported backends include Intel 
-Architecture CPUs, the Intel® Nervana Neural Network Processor™ (NNP), 
-and NVIDIA\* GPUs. 
+.. figure:: ../graphics/ngraph-ecosystem.png
+   :width: 585px   
+  
+nGraph currently supports :doc:`three popular <../framework-integration-guides>` 
+frameworks for :abbr:`Deep Learning (DL)` models through what we call 
+a :term:`bridge` that can be integrated during the framework's build time. 
+For developers working with other frameworks (even those not listed above), 
+we've created a :doc:`How to Guide <../howto/index>` so you can learn how to create 
+custom bridge code that can be used to :doc:`compile and run <../howto/execute>` 
+a training model.
+
+We've recently added initial support for the `ONNX`_ format. Developers who 
+already have a "trained" model can use nGraph to bypass a lot of the 
+framework-based complexity and :doc:`../howto/import` to test or run it 
+on targeted and efficient backends with our user-friendly ``ngraph_api``. 
+With nGraph, data scientists can focus on data science rather than worrying 
+about how to adapt models to train and run efficiently on different devices.
+
+
+Supported platforms
+--------------------
+
+Initially-supported backends include:
+
+* Intel® Architecture Processors (CPUs), 
+* Intel® Nervana™ Neural Network Processor™ (NNPs), and 
+* NVIDIA\* CUDA (GPUs). 
+
+Tentatively in the pipeline, we plan to add support for more backends,
+including:
+
+* :abbr:`Field Programmable Gate Arrays (FPGA)` (FPGAs)
+* `Movidius`_ compute stick 
+
+.. note:: The library code is under active development as we're continually 
+   adding support for more kinds of DL models and ops, framework compiler 
+   optimizations, and backends. 
 
 
 Why was this needed?
@@ -35,7 +63,7 @@ to similar ops in the new framework, and finally make the necessary changes
 for the preferred backend configuration on the new framework.
 
 We designed the Intel nGraph project to substantially reduce these kinds of 
-engineering complexities. Our conpiler-inspired approach means that developers 
+engineering complexities. Our compiler-inspired approach means that developers 
 have fewer constraints imposed by frameworks when working with their models; 
 they can pick and choose only the components they need to build custom algorithms 
 for advanced deep learning tasks. Furthermore, if working with a model that is 
@@ -89,7 +117,7 @@ design decisions and what is tentatively in the pipeline for development in
 our `arXiv paper`_ from the 2018 SysML conference.
 
 
-.. _widely-supported frameworks: http://ngraph.nervanasys.com/docs/latest/framework-integration-guides.html
 .. _arXiv paper: https://arxiv.org/pdf/1801.08058.pdf
+.. _ONNX: http://onnx.ai 
 .. _Intel® MKL-DNN: https://github.com/intel/mkl-dnn
-
+.. _Movidius: https://developer.movidius.com/