README.md



nGraph Library 

Welcome to the open-source repository for the Intel nGraph Library. Our code
base provides a Compiler and runtime suite of tools (APIs) designed to give
developers maximum flexibility for their software design, allowing them to
create or customize a scalable solution using any framework while also avoiding
device-level hardware lock-in that is so common with many AI vendors. A neural
network model compiled with nGraph can run on any of our currently-supported
backends, and it will be able to run on any backends we support in the future
with minimal disruption to your model. With nGraph, you can co-evolve your
software and hardware's capabilities to stay at the forefront of your industry.

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 Architecture Processors (CPUs), Intel Nervana Neural Network Processors
(NNPs), cuDNN-compatible graphics cards (GPUs), custom VPUs like [Movidius], and
many others. The default CPU Backend also provides an interactive Interpreter
mode that can be used to zero in on a DL model and create custom nGraph
optimizations that can be used to further accelerate training or inference, in
whatever scenario you need.
nGraph provides both  a C++ API for framework developers and a Python API which
can run inference on models imported from ONNX.
See the Release Notes for recent changes.


Framework
bridge available?
ONNX support?


TensorFlow*
yes
yes


MXNet*
yes
yes


PaddlePaddle
yes
yes


PyTorch*
no
yes


Chainer*
no
yes


CNTK*
no
yes


Caffe2*
no
yes


Backend
current support
future support


Intel Architecture CPU
yes
yes


Intel Nervana Neural Network Processor (NNP)
yes
yes


Intel Movidius Myriad 2 VPUs
coming soon
yes


Intel Architecture GPUs
via PlaidML
yes


AMD* GPUs
via PlaidML
yes


NVIDIA* GPUs
via PlaidML
some


Field Programmable Gate Arrays (FPGA)
no
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.