Result for 00EFB6C3862D25B2F69402BF4993405214A13211

Query result

Key Value
FileName./usr/share/doc/libmkldnn-dev/html/group__cpp__api__primitive__descriptors.html
FileSize3708
MD5CBF78C7B38F3CCD5E1A7A756939832DE
SHA-100EFB6C3862D25B2F69402BF4993405214A13211
SHA-2562EDE9199568C7AB866A0FC0F87C5B2DD4DADD5E4C35F344908BF5E9B46FAD24F
SSDEEP96:HvmnQdDu9b8niwEt88C8f89P7j1A05b9Z8i:PmnQyoniwEQS05pj
TLSHT1DB716319AC86D53B41F245C1F6E2FAA891C18211C3848424E8FCA4D667C7FC9D9AA24A
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize2241796
MD53CD007F27133590B561162C4EC2C6EBD
PackageDescriptionMath 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.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamelibmkldnn-doc
PackageSectiondoc
PackageVersion0.17.4-1
SHA-1A961EF4279631692A09386A272E4CD80F692E2BB
SHA-256D2432E59A25F42FFED0EEB90354A59DEFA5CD1CDCBDEF610DE2D4ED5CF5491C4