Updated the documentation for ngraph-tensorflow-bridge and ngraph-tensorflow release. (#784)

doc/sphinx/source/framework-integration-guides.rst

@@ -172,29 +172,15 @@ TensorFlow\*
 Build with an XLA plugin to ``libngraph``
 ------------------------------------------
 
-.. important:: These instructions pick up where the :doc:`install` 
-   installation instructions left off, so they presume that your system already
-   has the |nGl| installed. If the |nGl| code has not yet been installed to
-   your system, please go back to complete those steps, and return here when
-   you are ready to build TensorFlow\*.
-
-
-#. Set the ``LD_LIBRARY_PATH`` path to the location where we built the nGraph 
-   libraries:
-
-   .. code-block:: bash
-
-      export LD_LIBRARY_PATH=$HOME/ngraph_dist/lib/
-
 #. To prepare to build TensorFlow with an XLA plugin capable of running |nGl|, 
    use the standard build process which is a system called "bazel". These 
-   instructions were tested with `bazel version 0.5.4`_. 
+   instructions were tested with `bazel version 0.11.0`_. 
 
    .. code-block:: console
 
-      $ wget https://github.com/bazelbuild/bazel/releases/download/0.5.4/bazel-0.5.4-installer-linux-x86_64.sh
-      $ chmod +x bazel-0.5.4-installer-linux-x86_64.sh
-      $ ./bazel-0.5.4-installer-linux-x86_64.sh --user
+      $ wget https://github.com/bazelbuild/bazel/releases/download/0.11.0/bazel-0.11.0-installer-linux-x86_64.sh
+      $ chmod +x bazel-0.11.0-installer-linux-x86_64.sh
+      $ ./bazel-0.11.0-installer-linux-x86_64.sh --user
 
 #. Add and source the ``bin`` path to your ``~/.bashrc`` file in order to be 
    able to call bazel from the user's installation we set up:
@@ -207,8 +193,8 @@ Build with an XLA plugin to ``libngraph``
 
       $ source ~/.bashrc   
 
-#. Ensure that all the TensorFlow 1.3 dependencies are installed, as per the
-   TensorFlow `1.3 installation guide`_:
+#. Ensure that all the TensorFlow dependencies are installed, as per the
+   TensorFlow `installation guide`_:
 
    .. note:: You do not need CUDA in order to use the nGraph XLA plugin.
 
@@ -218,24 +204,26 @@ Build with an XLA plugin to ``libngraph``
 
    .. code-block:: console
 
-      $ git clone git@github.com:NervanaSystems/ngraph-tensorflow-1.3.git
-      $ cd ngraph-tensorflow-1.3
+      $ git clone git@github.com:NervanaSystems/ngraph-tensorflow.git
+      $ cd ngraph-tensorflow
+      $ git checkout ngraph-tensorflow-preview-0
 
-#. Now run :command:`configure` and choose `y` when prompted to build TensorFlow
+#. Now run :command:`./configure` and choose `y` when prompted to build TensorFlow
    with XLA just-in-time compiler.
 
    .. code-block:: console
-      :emphasize-lines: 5-6
+      :emphasize-lines: 4-5
 
       . . .
 
-      Do you wish to build TensorFlow with Hadoop File System support? [y/N]
-      No Hadoop File System support will be enabled for TensorFlow
-      Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N] y
-      XLA JIT support will be enabled for TensorFlow
-      Do you wish to build TensorFlow with VERBS support? [y/N]
-      No VERBS support will be enabled for TensorFlow
-      Do you wish to build TensorFlow with OpenCL support? [y/N]
+      Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: n
+      No Apache Kafka Platform support will be enabled for TensorFlow.
+
+      Do you wish to build TensorFlow with XLA JIT support? [y/N]: y
+      XLA JIT support will be enabled for TensorFlow.
+
+      Do you wish to build TensorFlow with GDR support? [y/N]: 
+      No GDR support will be enabled for TensorFlow.
 
       . . .
 
@@ -246,27 +234,54 @@ Build with an XLA plugin to ``libngraph``
       $ bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
       $ bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
 
-#. Finally install the pip package
+#. Next install the pip package
 
    .. code-block:: console
 
-      $ pip install /tmp/tensorflow_pkg/tensorflow-1.3.0-cp27-cp27mu-linux_x86_64.whl
+      $ pip install -U /tmp/tensorflow_pkg/tensorflow-1.*whl
 
+   .. note::  The actual name of the Python wheel file will be updated to the official 
+   version of TensorFlow as the ngraph-tensorflow repository is synchronized frequently 
+   with the original TensorFlow repository.
 
-Run MNIST MLP through the TensorFlow / XLA plugin to nGraph
-------------------------------------------------------------
+#. Now clone the ngraph-tensorflow-bridge repo one level above -- in the parent directory 
+   of the ngraph-tensorflow repo cloned in step 1:
+
+   .. code-block:: console
+
+      $ cd ..
+      $ git clone https://github.com/NervanaSystems/ngraph-tensorflow-bridge.git
+      $ cd ngraph-tensorflow-bridge
+
+#. Finally, build and install ngraph-tensorflow-bridge
+
+   .. code-block:: console
+
+      $ mkdir build
+      $ cd build
+      $ cmake ../
+      $ make install
+
+This final step automatically downloads the necessary version of ngraph and the dependencies. 
+The resulting plugin DSO named libngraph_plugin.so gets copied to the following directory 
+inside the TensorFlow installation directory: <Python site-packages>/tensorflow/plugins
 
-To test an example through the TensorFlow / XLA plugin to nGraph, you can use the 
-the MNIST softmax regression example script named `mnist_softmax_ngraph.py` that
-is available in the `/examples/mnist`_ directory.
+Once the build and installation steps are complete, you can start experimenting with 
+nGraph backends.
+
+
+Run MNIST Softmax with the activated bridge
+------------------------------------------------------------
 
-This script was modified from the example explained in the TensorFlow\* tutorial;
-the following changes were made from the original script:
+To see everything working together, you can run MNIST Softmax example with the now-activated 
+bridge to nGraph. The script named mnist_softmax_ngraph.py can be found under the 
+ngraph-tensorflow-bridge/test directory. It was modified from the example explained 
+in the TensorFlow\* tutorial; the following changes were made from the original script:
 
 .. code-block:: python
 
    def main(_):
-   with tf.device('/device:XLA_NGRAPH:0'):
+   with tf.device('/device:NGRAPH:0'):
      run_mnist(_)
 
    def run_mnist(_):
@@ -281,20 +296,23 @@ To test everything together, set the configuration options:
    export OMP_NUM_THREADS=4 
    export KMP_AFFINITY=granularity=fine,scatter
 
-And run the script as follows from within the `/examples/mnist`_ directory of 
-your cloned version of `ngraph-tensorflow`_:
+And run the script as follows from within the `/test`_ directory of 
+your cloned version of `ngraph-tensorflow-bridge`_:
 
 .. code-block:: console   
 
    $ python mnist_softmax_ngraph.py
 
+   .. note::  The number-of-threads parameter specified in the `OMP_NUM_THREADS` is 
+   a function of number of CPU cores that are available in your system. 
 
 
 .. _MXNet: http://mxnet.incubator.apache.org
 .. _bazel version 0.5.4: https://github.com/bazelbuild/bazel/releases/tag/0.5.4
-.. _1.3 installation guide: https://www.tensorflow.org/versions/r1.3/install/install_sources#prepare_environment_for_linux
+.. _installation guide: https://www.tensorflow.org/install/install_sources#prepare_environment_for_linux
 .. _ngraph-tensorflow: https://github.com/NervanaSystems/ngraph-tensorflow
-.. _/examples/mnist: https://github.com/NervanaSystems/ngraph-tensorflow/tree/develop/tensorflow/compiler/plugin/ngraph/examples/mnist
+.. _ngraph-tensorflow-bridge: https://github.com/NervanaSystems/ngraph-tensorflow-bridge
+.. _/test: https://github.com/NervanaSystems/ngraph-tensorflow-bridge/tree/master/test
 .. _ngraph-neon python README: https://github.com/NervanaSystems/ngraph/blob/master/python/README.md
 .. _ngraph-neon repo's README: https://github.com/NervanaSystems/ngraph-neon/blob/master/README.md
 .. _neon docs: https://github.com/NervanaSystems/neon/tree/master/doc