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Commit 237ceee6 authored by Avijit's avatar Avijit Committed by Scott Cyphers
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Updated the documentation for ngraph-tensorflow-bridge and ngraph-tensorflow release. (#784)

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