# nGraph library Welcome to Intel® nGraph™, 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][ngraph-ecosystem] ## 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]. * 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. [install]: http://ngraph.nervanasys.com/docs/latest/install.html [framework integration guides]: http://ngraph.nervanasys.com/docs/latest/framework-integration-guides.html [Github issues]: https://github.com/NervanaSystems/ngraph/issues [pull request]: https://github.com/NervanaSystems/ngraph/pulls [how to import]: http://ngraph.nervanasys.com/docs/latest/howto/import.html [ngraph-ecosystem]: doc/sphinx/source/graphics/ngraph-ecosystem.png "nGraph Ecosystem"