Framework Optimizations

 

Explore resources available for popular AI frameworks optimized on Intel® Architecture, including installation guides and other learning material. We are continuously expanding our list of supported frameworks, so bookmark this page to stay up-to-date.

neon

Intel's innovation and reference framework is committed to the best performance on all hardware.

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Resources

TensorFlow

Intel and Google engineers have been working together to optimize TensorFlow, a flexible open-source AI framework, for Intel® Xeon® and Intel® Xeon Phi™ processors.

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Caffe

The Berkeley Vision and Learning Center (BVLC) has made the Intel® Optimization of Caffe* an available branch off of their main line. Intel has contributed to one of the most popular frameworks for image recognition by improving Caffe* performance when running on Intel® Xeon® and Intel® Xeon Phi™ processors.

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Resources

Theano

This fork of the very popular Python* library, Theano, improves performance on CPU devices—in particular Intel® Xeon® and Intel® Xeon Phi™ processors.

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MXNet

The open-source, deep learning framework MXNet* includes built-in support for the Intel® Math Kernel Library (Intel® MKL). This includes optimizations for Intel® Advanced Vector Extensions 2 (Intel® AVX2) and Intel® Advanced Vector Extension 512 (Intel® AVX-512) instructions, which are supported in Intel® Xeon® and Intel® Xeon Phi™ processors.

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BigDL

BigDL is an open-source distributed deep learning framework for Apache Spark. It brings native support for deep learning functionalities to Apache Spark with single-node Intel®
Xeon® processors' performance, and efficiently scales out deep learning workloads based on the Spark architecture.

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Resources

Chainer

Chainer* is a deep learning framework based on Python*. It provides automatic differentiation APIs based on the define-by-run approach, as well as APIs to build and train neural networks.

Installation

Download on GitHub

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