Welcome to the open-source repository for the Intel
The nGraph Compiler is Intel's graph compiler for Artificial Neural Networks.
Documentation in this repo describes how you can program any framework
to run training and inference computations on a variety of Backends including
Intel
nGraph provides both a C++ API for framework developers and a Python API which can run inference on models imported from ONNX.
Framework | bridge available? | ONNX support? |
---|---|---|
neon | yes | yes |
MXNet* | yes | yes |
TensorFlow* | yes | yes |
PyTorch* | not yet | yes |
Chainer* | 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:
- See the contrib guide for code formatting and style guidelines.
- 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.