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https://github.com/NervanaSystems/ngraph-python

Original Python version of Intel® Nervana™ Graph
https://github.com/NervanaSystems/ngraph-python

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Original Python version of Intel® Nervana™ Graph

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README 

        
**DISCONTINUATION OF PROJECT.** This project will no longer be maintained by Intel. Intel will not provide or guarantee development of or support for this project, including but not limited to, maintenance, bug fixes, new releases or updates. Patches to this project are no longer accepted by Intel. If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the community, please create your own fork of the project.



# nGraph Python 



### An Intermediate Representation, Compiler, and Executor for Deep Learning



*Updated: March 17, 2018* 



Welcome to the nGraph Python repo. Now that we've released our [C++ rewrite]

to the community, we are transitioning away from this original Python

implementation. You can browse here to learn a bit about the roots of the

[legacy] project.  



## Why did we build nGraph?



When Deep Learning (DL) frameworks first emerged as the vehicle for training and 

inference models, they were designed around kernels optimized for a particular 

platform. As a result, many backend details were being exposed in the model 

definitions, making the adaptability and portability of DL models to other or 

more advanced backends inherently complex and expensive.



The traditional approach means that an algorithm developer cannot easily adapt 

his or her model to different backends. Making a model run on a different 

framework is also problematic because the developer must separate the essence of 

the model from the performance adjustments made for the backend, translate to 

similar ops in the new framework, and finally make the necessary changes for 

the preferred backend configuration on the new framework.



We designed the Intel nGraph project to substantially reduce these kinds of 

engineering complexities. While optimized kernels for deep-learning primitives 

are provided through the project and via libraries like Intel® Math Kernel Library 

for Deep Neural Networks (Intel® MKL-DNN), there are several compiler-inspired 

ways in which performance can be further optimized. 



## How does it work in practice?



Install the nGraph library and write or compile a framework with the library, 

in order to run training and inference models. Using the command line on any 

supported system (currently Linux) specify the backend you want to use. Our 

Intermediate Representation (IR) layer handles all the hardware abstraction 

details and frees developers to focus on their data science, algorithms and 

models, rather than on machine code.



At a more granular level of detail: 



* The **nGraph core** creates a strongly-typed and platform-neutral stateless 

  graph representation of computations. Each node, or *op*, in the graph 

  corresponds to one step in a computation, where each step produces zero or 

  more tensor outputs from zero or more tensor inputs.



* We've developed a **framework bridge** for each supported framework; it acts 

  as an intermediary between the *ngraph core* and the framework. A **transformer** 

  then plays a similar role between the ngraph core and the various execution 

  platforms.



* **Transformers** handle the hardware abstraction; they compile the graph with 

  a combination of generic and platform-specific graph transformations. The 

  result is a function that can be executed from the framework bridge. 

  Transformers also allocate and deallocate, as well as read and write tensors 

  under direction of the bridge.

  

You can read more about design decisions and what is tentatively in the pipeline 

for backends and development in our [ARXIV abstract and conference paper].



[C++ rewrite]:http://github.com/NervanaSystems/ngraph 

[legacy]:legacy_README.md

[ARXIV abstract and conference paper]:https://arxiv.org/pdf/1801.08058.pdf