Package: ngraph-tensorflow-bridge
Intel(R) nGraph(TM) Compiler and Runtime for TensorFlow*This repository contains the code needed to enable Intel(R) nGraph(TM) Compiler and
runtime engine for TensorFlow. Use it to speed up your TensorFlow training and
inference workloads. The nGraph Library and runtime suite can also be used to
customize and deploy Deep Learning inference models that will "just work" with
a variety of nGraph-enabled backends: CPU, GPU, and custom silicon like theIntel(R) Nervana(TM) NNP.InstallationSoftware requirementsUsing pre-built packagesBuilding from sourcePython 3Python 3TensorFlow v1.14GCC 4.8 (Ubuntu), Clang/LLVM (macOS)cmake3.4 or higherBazel 0.25.2virtualenv16.0.0Use pre-built packagesnGraph bridge enables you to use the nGraph Library with TensorFlow.
Complete the following steps to install a pre-built nGraph bridge for
TensorFlow.Install TensorFlow:pip install -U tensorflow==1.14.0Installngraph-tensorflow-bridge:pip install -U ngraph-tensorflow-bridgeBuild nGraph from sourceTo use the latest version of nGraph Library, complete the following steps to
build nGraph bridge from source.Note to macOS usersThe build and installation instructions are identical for Ubuntu 16.04 and
macOS. However, the Python setup may vary across different versions of Mac OS.
TensorFlow build instructions recommend using Homebrew but developers often use
Pyenv. Some users prefer Anaconda/Miniconda. Before building nGraph, ensure that
you can successfully build TensorFlow on macOS with a suitable Python
environment.The requirements for building nGraph bridge are identical to the requirements for building TensorFlow from source. For more information, review theTensorFlow configurationdetails.Prepare your build environmentInstall the following requirements before buildingnGraph-bridge.TensorFlow uses a build system called "bazel". For the current
version ofbazel, usebazel version.Installbazel:wget https://github.com/bazelbuild/bazel/releases/download/0.25.2/bazel-0.25.2-installer-linux-x86_64.sh      
    bash bazel-0.25.2-installer-linux-x86_64.sh --userAdd and source thebinpath to your~/.bashrcfile to call
bazel:export PATH=$PATH:~/bin
    source ~/.bashrcInstallcmake,virtualenv, andgcc 4.8.Build an nGraph bridgeOnce TensorFlow's dependencies are installed, clone thengraph-bridgerepo:git clone https://github.com/tensorflow/ngraph-bridge.git
    cd ngraph-bridge
    git checkout v0.18.0Run the following Python script to build TensorFlow, nGraph, and the bridge. Use Python 3.5:python3 build_ngtf.py --use_prebuilt_tensorflowWhen the build finishes, a newvirtualenvdirectory is created inbuild_cmake/venv-tf-py3. Build artifacts (i.e., thengraph_tensorflow_bridge-<VERSION>-py2.py3-none-manylinux1_x86_64.whl) are created in thebuild_cmake/artifactsdirectory.Add the following flags to build PlaidML and Intel GPU backends (optional):--build_plaidml_backend
    --build_intelgpu_backendFor more build options:python3 build_ngtf.py --helpTest the installation:python3 test_ngtf.pyThis command runs all C++ and Python unit tests from thengraph-bridgesource tree. It also runs various TensorFlow Python tests using nGraph.To use thengraph-tensorflow-bridge, activate the followingvirtualenvto start using nGraph with TensorFlow.source build_cmake/venv-tf-py3/bin/activateAlternatively, you can also install the TensorFlow and nGraph bridge outside of avirtualenv. The Pythonwhlfiles are located in thebuild_cmake/artifacts/andbuild_cmake/artifacts/tensorflowdirectories, respectively.Select the help option ofbuild_ngtf.pyscript to learn more about various build options and how to build other backends.Verify thatngraph-bridgeinstalled correctly:python -c "import tensorflow as tf; print('TensorFlow version: ',tf.__version__);\
            import ngraph_bridge; print(ngraph_bridge.__version__)"

This will produce something like this:

    TensorFlow version:  <1.14.0>
    nGraph bridge version: <b'0.18.0'>
    nGraph version used for this build: b'0.25.0+005c118'
    TensorFlow version used for this build: <v1.14.0-...>
    CXX11_ABI flag used for this build: 0
    nGraph bridge built with Grappler: False
    nGraph bridge built with Variables and Optimizers Enablement: False

Note: The version of the ngraph-tensorflow-bridge is not going to be exactly the same as when you build from source. This is due to delay in the source release and publishing the corresponding Python wheel.Classify an imageOnce you have installed nGraph bridge, you can use TensorFlow to train a neural network or run inference using a trained model.Use TensorFlow with nGraph to classify an image using afrozen model.Download the Inception v3 trained model and labels file:wget https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gzExtract the frozen model and labels file from the tarball:tar xvf inception_v3_2016_08_28_frozen.pb.tar.gzDownload the image file:wget https://github.com/tensorflow/tensorflow/raw/master/tensorflow/examples/label_image/data/grace_hopper.jpgDownload the TensorFlow script:wget https://github.com/tensorflow/tensorflow/raw/master/tensorflow/examples/label_image/label_image.pyModify the downloaded TensorFlow script to run TensorFlow with nGraph optimizations:import ngraph_bridge
    ...
    config = tf.ConfigProto()
    config_ngraph_enabled = ngraph_bridge.update_config(config)
    sess = tf.Session(config=config_ngraph_enabled)Run the classification:python label_image.py --graph inception_v3_2016_08_28_frozen.pb \
            --image grace_hopper.jpg --input_layer=input \
            --output_layer=InceptionV3/Predictions/Reshape_1 \
            --input_height=299 --input_width=299 \
            --labels imagenet_slim_labels.txtThis will print the following results:military uniform 0.8343056
    mortarboard 0.021869544
    academic gown 0.010358088
    pickelhaube 0.008008157
    bulletproof vest 0.005350913The above instructions are derived from theTensorFlow C++ and Python Image Recognition Demo.All of the above commands are available in thenGraph TensorFlow examplesdirectory. To classify your own images, modify theinfer_image.pyfile in this directory.Add runtime options for a CPU backendAdding runtime options for a CPU backend applies to training and inference.By default nGraph runs with a CPU backend. To get the best performance of the CPU backend, add the following option:OMP_NUM_THREADS=<num_cores> KMP_AFFINITY=granularity=fine,compact,1,0 \ 
    python label_image.py --graph inception_v3_2016_08_28_frozen.pb 
            --image grace_hopper.jpg --input_layer=input \
            --output_layer=InceptionV3/Predictions/Reshape_1 \
            --input_height=299 --input_width=299 \
            --labels imagenet_slim_labels.txtWhere<num_cores>equals the number of cores in your processor.Measure the timenGraph is a Just In Time (JIT) compiler meaning that the TensorFlow computation graph is compiled to nGraph during the first instance of the execution. From the second time onwards, the execution speeds up significantly.Add the following Python code to measure the computation time:# Warmupsess.run(output_operation.outputs[0],{input_operation.outputs[0]:t})# Runimporttimestart=time.time()results=sess.run(output_operation.outputs[0],{input_operation.outputs[0]:t})elapsed=time.time()-startprint('Time elapsed:%fseconds'%elapsed)Observe that the output time runs faster than TensorFlow native (i.e., without nGraph).Add additional backendsYou can substitute the default CPU backend with a different backend such asPLAIDMLorINTELGPU. Use the following API:ngraph_bridge.set_backend('PLAIDML')To determine what backends are available on your system, use the following API:ngraph_bridge.list_backends()More detailed examples on how to use ngraph_bridge are located in theexamplesdirectory.DebuggingDuring the build, often there are missing configuration steps for building TensorFlow. If you run into build issues, first ensure that you can build TensorFlow. For debugging run time issues, see the instructions provided in thediagnosticsdirectory.SupportPlease submit your questions, feature requests and bug reports viaGitHub issues.How to ContributeWe welcome community contributions to nGraph. If you have an idea for how to
improve it:Share your proposal viaGitHub issues.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 apull request.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.About Intel(R) nGraph(TM)See the full documentation here:http://ngraph.nervanasys.com/docs/latest