Intel nGraph compiler and runtime for TensorFlow
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 the Intel(R) Nervana(TM) NNP.
Option 1: Use a pre-built nGraph-TensorFlow bridge
You need to instantiate a specific kind of
virtualenvto be able to proceed with the
ngraph-tfbridge installation. For systems with Python 3.5 or Python 2.7, these commands are
virtualenv --system-site-packages -p python3 your_virtualenv virtualenv --system-site-packages -p /usr/bin/python2 your_virtualenv source your_virtualenv/bin/activate # bash, sh, ksh, or zsh
Install TensorFlow v1.13.1:
pip install -U tensorflow
Install nGraph-TensorFlow bridge:
pip install -U ngraph-tensorflow-bridge
Test the installation by running the following command:
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: 1.13.1 nGraph bridge version: b'0.12.0' nGraph version used for this build: b'0.18.0-rc.2+c5d52f1' TensorFlow version used for this build: v1.13.1-0-g6612da8951 CXX11_ABI flag used for this build: 1
Next you can try out the TensorFlow models by adding one line to your existing TensorFlow model scripts and running them the usual way:
Detailed examples on how to use ngraph_bridge are located in the examples directory.
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.
Option 2: Build nGraph bridge from source
To use the latest version, or to run unit tests, or if you are planning to contribute, install the nGraph bridge using the TensorFlow source tree as follows:
Prepare the build environment
The installation prerequisites are the same as described in the TensorFlow prepare environment for linux.
TensorFlow uses a build system called "bazel". These instructions were tested with bazel version 0.21.0.
wget https://github.com/bazelbuild/bazel/releases/download/0.21.0/bazel-0.21.0-installer-linux-x86_64.sh chmod +x bazel-0.21.0-installer-linux-x86_64.sh ./bazel-0.21.0-installer-linux-x86_64.sh --user
Add and source the
binpath to your
~/.bashrcfile in order to be able to call bazel from the user's installation we set up:
export PATH=$PATH:~/bin source ~/.bashrc
Additionally, you need to install
cmakeversion 3.1 or higher and gcc 4.8 or higher.
Once TensorFlow's dependencies are installed, clone
git clone https://github.com/NervanaSystems/ngraph-tf.git cd ngraph-tf git checkout v0.12.0
Next run the following Python script to build TensorFlow, nGraph and the bridge. Please use Python 3.5:
:warning: Note that if you are running TensorFlow on a Skylake family processor then you need to pass the target_arch parameter, as below
python3 build_ngtf.py --target_arch=broadwell
This is due to an issue in TensorFlow tracked here: https://github.com/tensorflow/tensorflow/issues/17273
Once the build finishes, a new virtualenv directory is created in the
build_cmake/venv-tf-py3. The build artifacts i.e., the
ngraph_tensorflow_bridge-<VERSION>-py2.py3-none-manylinux1_x86_64.whl is created in the
Test the installation by running the following command:
This command will run all the C++ and python unit tests from the ngraph-tf source tree. Additionally this will also run various TensorFlow python tests using nGraph.
To use the ngraph-tensorflow bridge, activate this virtual environment to start using nGraph with TensorFlow.
Once the build and installation steps are complete, you can start using TensorFlow with nGraph backends.
Please add the following line to enable nGraph:
Using OS X
The build and installation instructions are identical for Ubuntu 16.04 and OS X. However, please note that the Python setup is not always the same across various Mac OS versions. TensorFlow build instructions recommend using Homebrew and often people use Pyenv. There is also Anaconda/Miniconda which some users prefer. Ensure that you can build TenorFlow successfully on OS X with a suitable Python environment prior to building nGraph.
See the instructions provided in the diagnostics directory.
Please submit your questions, feature requests and bug reports via GitHub issues.
How to Contribute
We welcome community contributions to nGraph. If you have an idea for how to improve it:
- Share your proposal via GitHub 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 a pull 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
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