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.
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.
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*.