Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/nervanasystems/ngraph

nGraph has moved to OpenVINO
https://github.com/nervanasystems/ngraph

caffe2 compiler deep-learning deep-neural-networks mxnet neural-network ngraph onnx paddlepaddle performance pytorch tensorflow

Last synced: about 1 month ago
JSON representation

nGraph has moved to OpenVINO

Awesome Lists containing this project

README 

        
nGraph has moved to OpenVINO: https://github.com/openvinotoolkit/openvino



![nGraph Compiler stack](doc/sphinx/source/graphics/ngraph_header.png)





  


    Architecture & features |   

    Ecosystem |  

	Release notes |  

    Documentation |  

    Contribution guide | 

    License: Apache 2.0

 





## Quick start



To begin using nGraph with popular frameworks, please refer to the links below. 



|  Framework (Version)       | Installation guide                     | Notes  

|----------------------------|----------------------------------------|-----------------------------------

| TensorFlow*                | [Pip install](https://www.ngraph.ai/tutorials/tensorflow-tutorial#use-pre-built-packages) or [Build from source](https://www.ngraph.ai/tutorials/tensorflow-tutorial#build-from-source) | 20 [Validated workloads]   

| ONNX 1.5                   | [Pip install](https://www.ngraph.ai/tutorials/onnx-tutorial#use-pre-built-packages)                          | 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**](https://ai.intel.com/ngraph-compiler-stack-beta-release/) 

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. 



![](doc/sphinx/source/graphics/nGraph_main.png)



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.



[Ecosystem]: ./ecosystem-overview.md

[Architecture and features]: ./ABOUT.md

[Documentation]: https://www.ngraph.ai/documentation

[build the Library]:  https://www.ngraph.ai/documentation/buildlb

[Getting Started Guides]: Getting-started-guides

[Validated workloads]: https://www.ngraph.ai/documentation/frameworks/validated/list

[Functional]: https://github.com/NervanaSystems/ngraph-onnx/ 

[How to contribute]: How-to-contribute

[framework integration guides]: https://ngraph.ai/documentation/frameworks/overview

[release notes]: https://www.ngraph.ai/documentation/project/release-notes

[Github issues]: https://github.com/NervanaSystems/ngraph/issues

[contrib guide]: https://www.ngraph.ai/documentation/contributing/guide

[pull request]: https://github.com/NervanaSystems/ngraph/pulls

[how to import]: https://www.ngraph.ai/tutorials/onnx-tutorial#import-a-model-with-onnx-and-ngraph

[ngraph_wireframes_with_notice]: doc/sphinx/source/graphics/nGraph_main.png "nGraph components"

[build-status]: https://travis-ci.org/NervanaSystems/ngraph/branches

[build-status-badge]: https://travis-ci.org/NervanaSystems/ngraph.svg?branch=master

[PlaidML]: https://github.com/plaidml/plaidml

[Source compile]: https://github.com/NervanaSystems/ngraph-mxnet/blob/master/README.md

[nGraph-ONNX]: https://github.com/NervanaSystems/ngraph-onnx/blob/master/README.md

[nGraph-ONNX adaptable]: https://ai.intel.com/adaptable-deep-learning-solutions-with-ngraph-compiler-and-onnx/

[nGraph for PyTorch developers]: https://ai.intel.com/investing-in-the-pytorch-developer-community