Source: mkl-dnn Section: science Priority: optional Maintainer: Debian Science Maintainers Uploaders: Mo Zhou Build-Depends: debhelper (>=11~), cmake, Build-Depends-Indep: doxygen, graphviz, Standards-Version: 4.3.0 Homepage: https://github.com/intel/mkl-dnn Vcs-Browser: https://salsa.debian.org/science-team/mkl-dnn Vcs-Git: https://salsa.debian.org/science-team/mkl-dnn.git # Note, the Architecture of this package is set to amd64 only as suggested # by upstream. https://github.com/intel/mkl-dnn/issues/206 Package: libmkldnn-dev Section: libdevel Architecture: amd64 Multi-Arch: same Depends: ${shlibs:Depends}, ${misc:Depends}, libmkldnn0 (= ${binary:Version}), Description: Intel Math Kernel Library for Deep Neural Networks (dev) Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces. . DNN functionality optimized for Intel architecture is also included in Intel(R) Math Kernel Library (Intel(R) MKL). API in this implementation is not compatible with Intel MKL-DNN and does not include certain new and experimental features. . One can choose to build Intel MKL-DNN without binary dependency. The resulting version will be fully functional, however performance of certain convolution shapes and sizes and inner product relying on SGEMM function may be suboptimal. . This package contains the header files, and symbol links to the shared object. Package: libmkldnn0 Section: libs Architecture: amd64 Multi-Arch: same Depends: ${shlibs:Depends}, ${misc:Depends}, Description: Intel Math Kernel Library for Deep Neural Networks (lib) Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces. . DNN functionality optimized for Intel architecture is also included in Intel(R) Math Kernel Library (Intel(R) MKL). API in this implementation is not compatible with Intel MKL-DNN and does not include certain new and experimental features. . One can choose to build Intel MKL-DNN without binary dependency. The resulting version will be fully functional, however performance of certain convolution shapes and sizes and inner product relying on SGEMM function may be suboptimal. . This package contains the shared object. Package: libmkldnn-doc Section: doc Architecture: all Depends: ${misc:Depends}, Description: Math Kernel Library for Deep Neural Networks (doc) Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces. . DNN functionality optimized for Intel architecture is also included in Intel(R) Math Kernel Library (Intel(R) MKL). API in this implementation is not compatible with Intel MKL-DNN and does not include certain new and experimental features. . One can choose to build Intel MKL-DNN without binary dependency. The resulting version will be fully functional, however performance of certain convolution shapes and sizes and inner product relying on SGEMM function may be suboptimal. . This package contains the doxygen documentation.