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.
| 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 |
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.
Please submit your questions, feature requests and bug reports via GitHub issues.
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.