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.ci/travis/ubuntu [Travis] Check code style before build (#1015)
cmake Don't build mkl-dnn if user manually provides mkl-dnn. (#1018)
contrib/docker add apt-transport-https for contrib/docker/Dockerfile for GPU on Ubuntu 16.04 (#953)
doc Docs/editing (#1026)
licenses updating ngraph theme with IntelNeoSans font on headings, better OFL font for doc readability, better rendering of certain text, etc (#727)
maint Build NNP with ngraph as a library (#1005)
python [Py] Check input shape is the same as tensor shape (#1016)
src fix the op list generator script (#1049)
test add gpu product (#1040)
third-party Build NNP with ngraph as a library (#1005)
.clang-format Use weak_ptr for node in inputs/outputs, turn off alignment style.
.gitignore Update python wrapper to new Backend API (#863)
.gitmodules Silee2/single repo (#646)
.travis.yml Becky/version ngraph travis ci fix (#942)
CMakeLists.txt Fix warning-as-error (#1029)
INSTALL.md removed contrib/docker/Dockerfile for gcc 4.8 for Ubuntu 16.04 - not tested (#648)
LICENSE Add LICENSE and switch to Intel Copyright (#466)
README.md Enable Travis CI (#806)
VERSION.in Auto generate version number and apply it to install dir and libngraph.so (#925)
changes.md Replace using aliases with actual classes (#428)
README.md

nGraph library Build Status

Welcome to Intel:registered: nGraph:tm:, an open source C++ library, compiler and runtime. This project enables modern compute platforms to run and train Deep Neural Network (DNN) models. It is framework-neutral and supports a variety of backends used by Deep Learning (DL) frameworks.

nGraph ecosystem

Framework bridge available? ONNX support?
neon yes yes
MXNet* yes yes
TensorFlow* yes yes
PyTorch* not yet yes
CNTK* not yet yes
Caffe2* not yet yes

Documentation

See our install docs for how to get started.

For this early release, we provide framework integration guides to compile MXNet and TensorFlow-based projects. If you already have a trained model, we've put together a getting started guide for how to import a deep learning model and start working with the nGraph APIs.

Support

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 how to improve the library:

  • 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.
  • Make sure your PR passes all CI tests. Note: our Travis-CI service runs only on a CPU backend on Linux. We will run additional tests in other environments.
  • 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.