Quick start
To begin using nGraph with popular frameworks, please refer to the links below., Framework (Version), Installation guide, Notes, ----------------------------, ----------------------------------------, -----------------------------------, TensorFlow*, Pip install or Build from source, 20 Validated workloads, ONNX 1.5, Pip install, 17 Validated workloads
Python wheels for nGraph
The Python wheels for nGraph have been tested and are supported on the following
64-bit systems:
- Ubuntu 16.04 or later
- CentOS 7.6
- Debian 10
- macOS 10.14.3 (Mojave)
To install via pip, run:
pip install --upgrade pip==19.3.1
pip install ngraph-core
Frameworks using nGraph Compiler stack to execute workloads have shown
up to 45X
performance boost when compared to native framework implementations. We've also
seen performance boosts running workloads that are not included on the list of
Validated workloads, thanks to nGraph's powerful subgraph pattern matching.
Additionally we have integrated nGraph with PlaidML to provide deep learning
performance acceleration on Intel, nVidia, & AMD GPUs. More details on current
architecture of the nGraph Compiler stack can be found in Architecture and features,
and recent changes to the stack are explained in the Release Notes.
What is nGraph Compiler?
nGraph Compiler aims to accelerate developing AI workloads using any deep learning
framework and deploying to a variety of hardware targets. We strongly believe in
providing freedom, performance, and ease-of-use to AI developers.
The diagram below shows deep learning frameworks and hardware targets
supported by nGraph. NNP-T and NNP-I in the diagram refer to Intel's next generation
deep learning accelerators: Intel® Nervana™ Neural Network Processor for Training and
Inference respectively. Future plans for supporting addtional deep learning frameworks
and backends are outlined in the ecosystem section.
Our documentation has extensive information about how to use nGraph Compiler
stack to create an nGraph computational graph, integrate custom frameworks,
and to interact with supported backends. If you wish to contribute to the
project, please don't hesitate to ask questions in GitHub issues after
reviewing our contribution guide below.
How to contribute
We welcome community contributions to nGraph. If you have an idea how
to improve it:
- 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: You can test locally with
make check.
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.
