# Overview¶

Welcome to the documentation site for Intel® nGraph, an open-source C++ Compiler, Library, and runtime suite for running training and inference on Deep Neural Network models. nGraph is framework-neutral and can be targeted for programming and deploying Deep Learning applications on the most modern compute and edge devices.

## Features¶

### Develop without lock-in¶

Being able to increase training performance or reduce inference latency by simply adding another device of any specialized form factor – whether it be more compute (CPU), GPU or VPU processing power, custom ASIC or FPGA, or a yet-to-be invented generation of NNP or accelerator – is a key benefit for frameworks developers working with nGraph. Our commitment to bake flexibility into our ecosystem ensures developers’ freedom to design user-facing APIs for various hardware deployments directly into their frameworks.

nGraph currently supports three popular frameworks for Deep Learning models through what we call a bridge that can be integrated during the framework’s build time. For developers working with other frameworks (even those not listed above), we’ve created a How to Guide so you can learn how to create custom bridge code that can be used to compile and run a training model.

Additionally, nGraph Library supports the ONNX format. Developers who already have a “trained” model can use nGraph to bypass much of the framework-based complexity and Import a model to test or run it on targeted and efficient backends with our user-friendly ngraph_api. With nGraph, data scientists can focus on data science rather than worrying about how to adapt models to train and run efficiently on different devices. Be sure to add the -DNGRAPH_ONNX_IMPORT_ENABLE=ON option when running cmake to build the Library.

## Supported platforms¶

• Intel® Architecture Processors (CPUs),
• Intel® Nervana™ Neural Network Processor™ (NNPs), and
• NVIDIA* CUDA (GPUs).

We built the first-generation of the Intel Nervana™ NNP family of processors last year to show that the nGraph Library can be used to train a Neural Network more quickly. The more advanced the silicon, the more powerful a lightweight a library can be. So while we do currently support traditional GPUs, they are not advanced silicon, and trying to scale workloads using traditional GPU libraries is clunky and brittle with bottlenecks. Iteration from an already-trained NN model to one that can also perform inference computations is immensely simplified. Read more about these compute-friendly options on the documentation for Optimize Graphs.

Note

The library code is under active development as we’re continually adding support for more kinds of DL models and ops, framework compiler optimizations, and backends.

When Deep Learning (DL) frameworks first emerged as the vehicle for training 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 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 user 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. Our compiler-inspired approach means that developers have fewer constraints imposed by frameworks when working with their models; they can pick and choose only the components they need to build custom algorithms for advanced deep learning tasks. Furthermore, if working with a model that is already trained (or close to being trained), or if they wish to pivot and add a new layer to an existing model, the data scientist can Import a model and start working with Core Ops more quickly.

## How does it work?¶

The nGraph core uses a strongly-typed and platform-neutral stateless graph representation for 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. For a more detailed dive into how this works, read our documentation on how to Execute a computation.

## How do I connect it to a framework?¶

Currently, we offer framework bridges for some of the more widely-known frameworks. The bridge acts as an intermediary between the ngraph core and the framework, providing a means to use various execution platforms. The result is a function that can be executed from the framework bridge.

Given that we have no way to predict how many more frameworks might be invented for either model or framework-specific purposes, it would be nearly impossible for us to create bridges for every framework that currently exists (or that will exist in the future). Thus, the library provides a way for developers to write or contribute “bridge code” for various frameworks. We welcome such contributions from the DL community.

## How do I connect a DL training or inference model to nGraph?¶

Framework bridge code is not the only way to connect a model (function graph) to nGraph’s Core Ops. We’ve also built an importer for models that have been exported from a framework and saved as serialized file, such as ONNX. To learn how to convert such serialized files to an nGraph model, please see the Import a model documentation.

## What’s next?¶

We developed nGraph to simplify the realization of optimized deep learning performance across frameworks and hardware platforms. You can read more about design decisions and what is tentatively in the pipeline for development in our arXiv paper from the 2018 SysML conference.