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Accepting request 895673 from home:jengelh:branches:science:machinelearning

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opensuse trademark using guidelines says not to use these marks

OBS-URL: https://build.opensuse.org/request/show/895673
OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/onednn?expand=0&rev=9
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Guillaume GARDET 2021-05-27 08:30:46 +00:00 committed by Git OBS Bridge
parent a14ab9290a
commit 861717c4f1
2 changed files with 16 additions and 11 deletions
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onednn.changes
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@ -1,3 +1,8 @@
-------------------------------------------------------------------
Thu May 27 08:10:13 UTC 2021 - Jan Engelhardt <jengelh@inai.de>
- Update descriptions.
-------------------------------------------------------------------
Wed May 26 13:29:27 UTC 2021 - Guillaume GARDET <guillaume.gardet@opensuse.org>

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onednn.spec
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@ -33,7 +33,7 @@
Name: onednn
Version: 2.2.2
Release: 0
Summary: Intel(R) Math Kernel Library for Deep Neural Networks
Summary: Intel Math Kernel Library for Deep Neural Networks
License: Apache-2.0
URL: https://01.org/onednn
Source0: https://github.com/oneapi-src/oneDNN/archive/v%{version}/%{name}-%{version}.tar.gz
@ -59,18 +59,18 @@ Obsoletes: mkl-dnn <= %{version}
Provides: oneDNN = %{version}
%description
Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
open-source performance library for deep-learning applications. The library
accelerates deep-learning applications and frameworks on Intel architecture.
Intel MKL-DNN contains vectorized and threaded building blocks that you can use
to implement deep neural networks (DNN) with C and C++ interfaces.
%package -n benchdnn
Summary: Header files of Intel(R) Math Kernel Library
Summary: Header files of Intel Math Kernel Library
Requires: %{libname} = %{version}
%description -n benchdnn
Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
open-source performance library for deep-learning applications. The library
accelerates deep-learning applications and frameworks on Intel architecture.
Intel MKL-DNN contains vectorized and threaded building blocks that you can use
@ -79,35 +79,35 @@ to implement deep neural networks (DNN) with C and C++ interfaces.
This package only includes the benchmark utility including its input files.
%package devel
Summary: Header files of Intel(R) Math Kernel Library
Summary: Header files of Intel Math Kernel Library
Requires: %{libname} = %{version}
Provides: mkl-dnn-devel = %{version}
Obsoletes: mkl-dnn-devel <= %{version}
Provides: oneDNN-devel = %{version}
%description devel
Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
open-source performance library for deep-learning applications. The library
accelerates deep-learning applications and frameworks on Intel architecture.
Intel MKL-DNN contains vectorized and threaded building blocks that you can use
to implement deep neural networks (DNN) with C and C++ interfaces.
This package includes the required headers and library files to develop software
with the Intel(R) MKL-DNN.
with the Intel MKL-DNN.
%package doc
Summary: Reference documentation for the Intel(R) Math Kernel Library
Summary: Reference documentation for the Intel Math Kernel Library
BuildArch: noarch
%description doc
The reference documentation for the Intel(R) Math Kernel Library can be installed
The reference documentation for the Intel Math Kernel Library can be installed
with this package.
%package -n %{libname}
Summary: Header files of Intel(R) Math Kernel Library
Summary: Header files of Intel Math Kernel Library
%description -n %{libname}
Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an
Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) is an
open-source performance library for deep-learning applications. The library
accelerates deep-learning applications and frameworks on Intel architecture.
Intel MKL-DNN contains vectorized and threaded building blocks that you can use