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.
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.
Intel® MKL-DNN is an open source, performance-enhancing library for accelerating deep learning frameworks like like Caffe* and Theano* on Intel® Architecture.
clDNN is an open source performance library for deep learning applications that accelerates inference on Intel® Processor Graphics.
Intel® Machine Learning Scaling Library (Intel® MLSL) provides efficient implementation of communication patterns used in deep learning.
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.
The Intel® Distribution for Python* speeds up core computational packages and optimizes performance with integrated libraries and parallelism techniques.
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*.