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124 lines
4.3 KiB
124 lines
4.3 KiB
10 years ago
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##
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## cntk.spec -- OpenPKG RPM Package Specification
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## Copyright (c) 2000-2016 OpenPKG Foundation e.V. <http://openpkg.net/>
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##
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## Permission to use, copy, modify, and distribute this software for
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## any purpose with or without fee is hereby granted, provided that
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## the above copyright notice and this permission notice appear in all
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## copies.
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##
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## THIS SOFTWARE IS PROVIDED ``AS IS'' AND ANY EXPRESSED OR IMPLIED
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## WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
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## MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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## IN NO EVENT SHALL THE AUTHORS AND COPYRIGHT HOLDERS AND THEIR
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## CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
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## USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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## ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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## OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT
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## OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
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## SUCH DAMAGE.
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##
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# package information
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Name: cntk
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Summary: Computational Network ToolKit (CNTK)
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URL: https://github.com/Microsoft/CNTK
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Vendor: Microsoft
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Packager: OpenPKG Foundation e.V.
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Distribution: OpenPKG Community
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Class: EVAL
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Group: Algorithm
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License: BSD-style
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Version: 20160526
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Release: 20160526
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# list of sources
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Source0: http://download.openpkg.org/components/versioned/cntk/CNTK-%{version}.tar.xz
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Source1: cntk.sh
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Patch0: cntk.patch
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# build information
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BuildPreReq: OpenPKG, openpkg >= 20160101, gcc, gcc::with_cxx = yes
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PreReq: OpenPKG, openpkg >= 20160101
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BuildPreReq: opencv, openmpi, openmpi::with_cxx = yes, openblas, libzip
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PreReq: opencv, openmpi, openmpi::with_cxx = yes, openblas, libzip
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%description
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CNTK, the Computational Network Toolkit by Microsoft Research, is a
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unified deep-learning toolkit that describes neural networks as a
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series of computational steps via a directed graph. In this directed
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graph, leaf nodes represent input values or network parameters,
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while other nodes represent matrix operations upon their inputs.
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CNTK allows to easily realize and combine popular model types such
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as feed-forward DNNs, convolutional nets (CNNs), and recurrent
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networks (RNNs/LSTMs). It implements stochastic gradient descent
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(SGD, error backpropagation) learning with automatic differentiation
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and parallelization across multiple GPUs and servers.
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%track
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prog cntk = {
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version = %{version}
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url = http://download.openpkg.org/components/versioned/cntk/
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regex = CNTK-(__VER__)\.tar\.xz
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}
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%prep
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%setup -q -n CNTK
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%patch -p0
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%build
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# patch the build tools
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%{l_shtool} subst \
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-e 's;/bin/bash;%{l_bash};g' \
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Tools/build-and-test \
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Tools/generate_build_info \
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Tools/make_binary_drop_linux
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# configure the toolkit
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CC="%{l_cc}" \
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CXX="%{l_cxx}" \
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CFLAGS="%{l_cflags -O}" \
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CXXFLAGS="%{l_cxxflags -O}" \
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CPPFLAGS="%{l_cppflags}" \
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LDFLAGS="%{l_ldflags}" \
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%{l_bash} ./configure \
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--with-buildtype=release \
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--with-openblas=%{l_prefix} \
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--with-opencv=%{l_prefix} \
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--with-libzip \
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--with-code-coverage=no
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# build the toolkit
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%{l_make} %{l_mflags -O}
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%install
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# create installation hierarchy
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%{l_shtool} mkdir -f -p -m 755 \
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$RPM_BUILD_ROOT%{l_prefix}/bin \
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$RPM_BUILD_ROOT%{l_prefix}/libexec/cntk \
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$RPM_BUILD_ROOT%{l_prefix}/lib/cntk
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# install the binary wrapper
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%{l_shtool} install -c -m 755 %{l_value -s -a} \
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%{SOURCE cntk.sh} $RPM_BUILD_ROOT%{l_prefix}/bin/cntk
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# install the toolkit binaries
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%{l_shtool} install -c -m 755 \
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bin/cntk $RPM_BUILD_ROOT%{l_prefix}/libexec/cntk/
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%{l_shtool} install -c -m 644 \
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bin/cntk.core.bs $RPM_BUILD_ROOT%{l_prefix}/libexec/cntk/
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# install the toolkit libraries
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%{l_shtool} install -c -m 644 \
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lib/* $RPM_BUILD_ROOT%{l_prefix}/lib/cntk/
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# determine installation files
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%{l_rpmtool} files -v -ofiles -r$RPM_BUILD_ROOT %{l_files_std}
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%files -f files
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%clean
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