Result for 025CD69340C5524E508F4B50A03D152CC6699DF1

Query result

Key Value
FileName./usr/share/doc/libmkldnn-dev/html/search/groups_4.js
FileSize584
MD51EEC6A6089C92E528E835B8D73113F62
SHA-1025CD69340C5524E508F4B50A03D152CC6699DF1
SHA-256B477D24563A47A793EBA053E8FCA3D8553A23E5354A9E8603859A3025F17C223
SSDEEP6:qQRQe7VGCMzCm9TLIZBu/CmRfRulUD7Cm/5T2TpmZB4sm/58jdsVwek0RQsmqVGq:NQXCyL6BOfRxZNApwBW6s26+tIOfRtC
TLSHT1BFF090603899DF4C03C57B7BC6BBB3160569246B12A3E5013D7D95147D08236E2BA26C
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