Accepting request 839525 from home:Guillaume_G:branches:science:machinelearning
- Rename mkl-dnn to onednn to follow upstream
- Obsoletes mkl-dnn* <= %{version}
OBS-URL: https://build.opensuse.org/request/show/839525
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/onednn?expand=0&rev=1
This commit is contained in:
Tomáš Chvátal
Git OBS Bridge
commit
ade11b5481
23
.gitattributes
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.gitattributes
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## Default LFS
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.bsp filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.gem filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.jar filter=lfs diff=lfs merge=lfs -text
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*.lz filter=lfs diff=lfs merge=lfs -text
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*.lzma filter=lfs diff=lfs merge=lfs -text
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*.obscpio filter=lfs diff=lfs merge=lfs -text
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*.oxt filter=lfs diff=lfs merge=lfs -text
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*.pdf filter=lfs diff=lfs merge=lfs -text
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*.png filter=lfs diff=lfs merge=lfs -text
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*.rpm filter=lfs diff=lfs merge=lfs -text
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*.tbz filter=lfs diff=lfs merge=lfs -text
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*.tbz2 filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.ttf filter=lfs diff=lfs merge=lfs -text
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*.txz filter=lfs diff=lfs merge=lfs -text
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*.whl filter=lfs diff=lfs merge=lfs -text
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*.xz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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.gitignore
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.gitignore
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.osc
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_constraints
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_constraints
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<?xml version="1.0"?>
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<constraints>
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<hardware>
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<memory>
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<size unit="G">8</size>
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</memory>
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</hardware>
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</constraints>
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3
onednn-1.6.3.tar.gz
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onednn-1.6.3.tar.gz
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version https://git-lfs.github.com/spec/v1
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oid sha256:471c877671f672e4119e5f49143890c5ce2efff80a52a5eaf7ef3730eb3e1738
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size 5795520
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107
onednn.changes
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onednn.changes
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-------------------------------------------------------------------
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Mon Oct 5 06:16:30 UTC 2020 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Obsoletes mkl-dnn* <= %{version}
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-------------------------------------------------------------------
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Fri Oct 2 12:47:08 UTC 2020 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Rename mkl-dnn to onednn to follow upstream
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-------------------------------------------------------------------
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Wed Sep 23 13:36:02 UTC 2020 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Update to 1.6.3
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- Drop upstream patch:
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* cmake-no-install-ocl-cmake.patch
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-------------------------------------------------------------------
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Wed Sep 23 13:16:39 UTC 2020 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Build on aarch64 and ppc64le which are now also supported
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- Provide oneDNN and oneDNN-devel as it is the new official name
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-------------------------------------------------------------------
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Tue May 5 07:38:34 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>
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- Update to 1.4:
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* Performance improvements all over the board
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- Rebase patch cmake-no-install-ocl-cmake.patch
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-------------------------------------------------------------------
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Tue Mar 24 10:50:57 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>
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- Add constraints to not crash during testing on OOM
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-------------------------------------------------------------------
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Thu Feb 27 12:44:00 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>
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- Do not disable LTO there is no actual reason for that
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- Export LD_LIBRARY_PATH to fix older releases build
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-------------------------------------------------------------------
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Wed Feb 26 10:36:26 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>
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- There is no actual reason to not use github tag for tarball
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fetching -> remove the service
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- Format with spec-cleaner
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- Use proper %cmake macros everywhere
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- Add configure options for cmake to set it up in a way we really
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want
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- Add patch from Debian to not install OpenCL cmake finder:
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* cmake-no-install-ocl-cmake.patch
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-------------------------------------------------------------------
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Thu Feb 20 10:26:52 UTC 2020 - Christian Goll <cgoll@suse.com>
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- enabled tests
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-------------------------------------------------------------------
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Thu Jan 30 14:20:22 UTC 2020 - Christian Goll <cgoll@suse.com>
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- packaged separate benchnn packae with its input files
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- updated to v1.1.3 which includes
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* Fixed the mean and variance memory descriptors in layer
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normalization (65f1908)
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* Fixed the layer normalization formula (c176ceb)
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-------------------------------------------------------------------
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Wed Jan 8 15:21:54 UTC 2020 - Christian Goll <cgoll@suse.com>
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- updated to v1.1.2
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* Fixed threading over the spatial in bfloat16 batched
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normalization (017b6c9)
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* Fixed read past end-of-buffer error for int8 convolution (7d6f45e)
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* Fixed condition for dispatching optimized channel blocking in
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fp32 backward convolution on Intel Xeon Phi(TM) processor (846eba1)
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* Fixed fp32 backward convolution for shapes with spatial strides
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over the depth dimension (002e3ab)
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* Fixed softmax with zero sizes on GPU (936bff4)
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* Fixed int8 deconvolution with dilation when ih <= dh (3e3bacb)
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* Enabled back fp32 -> u8 reorder for RNN (a2c2507)
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* Fixed segmentation fault in bfloat16 backward convolution from
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kd_padding=0 computation (52d476c)
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* Fixed segmentation fault in bfloat16 forward convolution due
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to push/pop imbalance (4f6e3d5)
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* Fixed library version for OS X build (0d85005)
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* Fixed padding by channels in concat (a265c7d)
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* Added full text of third party licenses and
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copyright notices to LICENSE file (79f204c)
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* Added separate README for binary packages (28f4c96)
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* Fixed computing per-oc mask in RNN (ff3ffab)
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* Added workaround for number of cores calculation in Xbyak (301b088)
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-------------------------------------------------------------------
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Mon Feb 11 16:35:48 UTC 2019 - cgoll@suse.com
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- added ARCH_OPT_FLAGS=""
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-------------------------------------------------------------------
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Tue Feb 5 07:45:53 UTC 2019 - Christian Goll <cgoll@suse.com>
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- Initial checking of the Intel(R) Math Kernel Library for
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Deep Neural Networks which can be used by:
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* tensorflow
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* Caffee
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* PyTorch
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and other machine learning tools
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175
onednn.spec
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onednn.spec
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#
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# spec file for package onednn
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#
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# Copyright (c) 2020 SUSE LLC
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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# Please submit bugfixes or comments via https://bugs.opensuse.org/
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#
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%ifarch x86_64
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%bcond_without opencl
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%else
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# Build broken on non-x86, with openCL
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%bcond_with opencl
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%endif
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%define libname libdnnl1
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Name: onednn
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Version: 1.6.3
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Release: 0
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Summary: Intel(R) Math Kernel Library for Deep Neural Networks
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License: Apache-2.0
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URL: https://01.org/onednn
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Source0: https://github.com/oneapi-src/oneDNN/archive/v%{version}/%{name}-%{version}.tar.gz
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BuildRequires: cmake
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BuildRequires: doxygen
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BuildRequires: fdupes
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BuildRequires: gcc-c++
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BuildRequires: graphviz
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BuildRequires: texlive-dvips-bin
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%if %{with opencl}
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BuildRequires: opencl-headers
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BuildRequires: pkgconfig
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BuildRequires: pkgconfig(OpenCL)
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%endif
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ExclusiveArch: x86_64 aarch64 ppc64le
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Provides: mkl-dnn = %{version}
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Obsoletes: mkl-dnn <= %{version}
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Provides: oneDNN = %{version}
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%description
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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%package -n benchdnn
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Summary: Header files of Intel(R) Math Kernel Library
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Requires: %{libname} = %{version}
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%description -n benchdnn
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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This package only includes the benchmark utility including its input files.
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%package devel
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Summary: Header files of Intel(R) Math Kernel Library
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Requires: %{libname} = %{version}
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Provides: mkl-dnn-devel = %{version}
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Obsoletes: mkl-dnn-devel <= %{version}
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Provides: oneDNN-devel = %{version}
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%description devel
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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This package includes the required headers and library files to develop software
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with the Intel(R) MKL-DNN.
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%package doc
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Summary: Reference documentation for the Intel(R) Math Kernel Library
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BuildArch: noarch
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%description doc
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The reference documentation for the Intel(R) Math Kernel Library can be installed
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with this package.
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%package -n %{libname}
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Summary: Header files of Intel(R) Math Kernel Library
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%description -n %{libname}
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Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
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open-source performance library for deep-learning applications. The library
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accelerates deep-learning applications and frameworks on Intel architecture.
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Intel MKL-DNN contains vectorized and threaded building blocks that you can use
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to implement deep neural networks (DNN) with C and C++ interfaces.
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%prep
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%setup -q -n oneDNN-%{version}
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%autopatch -p1
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%build
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%cmake \
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-DCMAKE_INSTALL_LIBDIR=%{_lib} \
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-DMKLDNN_ARCH_OPT_FLAGS="" \
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-DDNNL_CPU_RUNTIME=OMP \
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%if %{with opencl}
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-DDNNL_GPU_RUNTIME=OCL \
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%endif
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-DDNNL_INSTALL_MODE=DEFAULT \
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-DDNNL_BUILD_TESTS=ON \
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-DDNNL_WERROR=OFF
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%cmake_build
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%cmake_build doc
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%install
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%cmake_install
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# move the built doxygen data to normal location
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mkdir -p %{buildroot}%{_docdir}/%{name}
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mv %{buildroot}%{_datadir}/doc/dnnl/reference/* %{buildroot}%{_docdir}/%{name}
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%fdupes %{buildroot}%{_docdir}/%{name}
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# do use macros to install license/docu
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rm -r %{buildroot}%{_datadir}/doc/dnnl
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# Keep compatibility with mkl-dnn
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pushd %{buildroot}%{_includedir}
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ln -s . mkl-dnn
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popd
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# install the benchmark
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install -D build/tests/benchdnn/benchdnn %{buildroot}/%{_bindir}/benchdnn
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#move install shared lib
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mkdir -vp %{buildroot}%{_datadir}/benchdnn
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cp -vr build/tests/benchdnn/inputs %{buildroot}%{_datadir}/benchdnn
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%check
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# do not use macro so we can exclude all gpu and cross (gpu and cpu) tests (they need gpu set up)
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pushd build
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export LD_LIBRARY_PATH=%{buildroot}%{_libdir}
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ctest --output-on-failure --force-new-ctest-process %{_smp_mflags} -E '(gpu|cross)'
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popd
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%post -n %{libname} -p /sbin/ldconfig
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%postun -n %{libname} -p /sbin/ldconfig
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%files -n benchdnn
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%{_bindir}/benchdnn
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%{_datadir}/benchdnn
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%files devel
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%{_includedir}/mkl-dnn
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%{_includedir}/mkldnn*.h*
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%{_includedir}/dnnl*.h*
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%{_libdir}/libdnnl.so
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%{_libdir}/libmkldnn.so
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%dir %{_libdir}/cmake/dnnl
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%{_libdir}/cmake/dnnl/*.cmake
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%dir %{_libdir}/cmake/mkldnn
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%{_libdir}/cmake/mkldnn/*.cmake
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%files doc
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%{_docdir}/%{name}
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%files -n %{libname}
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%license LICENSE
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%doc README.md
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%{_libdir}/libdnnl.so.*
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%{_libdir}/libmkldnn.so.*
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%changelog
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