Intel® nGraph™

An open source library for developing frameworks that can efficiently run deep learning computations on a variety of compute platforms

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nGraph APIs enable support for multiple deep learning frameworks

We currently support TensorFlow*, MXNet*, and neon directly through nGraph. CNTK*, PyTorch*, and Caffe2* are supported indirectly through ONNX.

Optimizing Complier

Explore and experiment without needing hand tuning to achieve high performance. Perform kernel fusion, efficient memory buffer allocation, and improved data layouts.

Flexibility Across Hardware

Intelligently enable distributed training across a variety of CPU, NNP, and GPU hardware without requiring the writing of new libraries from scratch.

nGraph: A New Open Source Compiler for Deep Learning Systems

We are pleased to announce the open sourcing of nGraph, a framework-neutral Deep Neural Network (DNN) model compiler that can target a variety of devices. With nGraph, data scientists can focus on data science rather than worrying about how to adapt their DNN models to train and run efficiently on different devices. Continue reading below…

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Intel® Nervana™ Graph Beta

We are building the Intel Nervana Graph project to be the LLVM for deep learning, and today we are excited to announce a beta release of our work we previously announced in a technical preview. We see the Intel Nervana Graph project as the beginning of an ecosystem of optimization passes, hardware backends and frontend…

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Preview Release: Intel® Nervana™ Graph

The field of deep learning is moving at a rapid pace.  Practitioners need tools that are flexible enough to keep up. Theano popularized the notion of computational graphs as a powerful abstraction, and more recently, TensorFlow iterated on that concept. Together, they demonstrate some first steps in unlocking the potential of deep learning, but we…

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