Software Libraries

Simplify and speed your development with software optimized to maximize hardware performance.


nGraph™ is the first compiler that lets data scientists use their preferred deep learning framework on any number of hardware architectures, for both training and inference.

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Intel® Math Kernel Library (MKL)

Intel® MKL is a ready-to-use math library for Intel® Processor-based systems that accelerates math processing routines, increases application performance, and reduces development time.

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Intel® Math Kernel Library for Deep Learning Networks (Intel® MKL-DNN)

Intel® MKL-DNN is an open source, performance-enhancing library for accelerating deep learning frameworks like like Caffe* and Theano* on Intel® Architecture.

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Intel® Open Sources Compute Library for Deep Neural Networks (clDNN)

clDNN is an open source performance library for deep learning applications that accelerates inference on Intel® Processor Graphics.

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Intel® Machine Learning Scaling Library (Intel® MLSL)

Intel® Machine Learning Scaling Library (Intel® MLSL) provides efficient implementation of communication patterns used in deep learning.

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Intel® Data Analytics Acceleration Library (Intel® DAAL)

Intel® Data Analytics Acceleration Library (Intel® DAAL) is a highly optimized library of computationally intensive routines for Intel® architecture-based platforms that helps speed big data analytics.

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Intel® Distribution for Python

The Intel® Distribution for Python* speeds up core computational packages and optimizes performance with integrated libraries and parallelism techniques.

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PlaidML is an open source tensor compiler. Combined with Intel’s nGraph graph compiler, it gives popular deep learning frameworks performance portability across a wide range of CPU, GPU and other accelerator processor architectures.

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BigDL* is an open-source distributed deep learning library that can run directly on top of existing Intel® Xeon® processor-based Apache Spark* or Apache Hadoop* clusters. It leverages Intel® Math Kernel Library (Intel® MKL) to enable comprehensive support for deep learning on frameworks including Caffe* and Torch*.

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