new file: b/Boost/Boost-1.66.0-intel-2017b-serial.eb

new file:   g/GROMACS/GROMACS-2018-intel-2017b-serial.eb
	new file:   s/scikit-learn/scikit-learn-0.19.1-Py-3.6.eb
This commit is contained in:
Lukáš Krupčík 2018-02-20 13:42:36 +01:00
parent 6975e6529d
commit 1152036513
3 changed files with 91 additions and 0 deletions

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# IT4Innovations 2018
name = 'Boost'
version = '1.66.0'
versionsuffix = '-serial'
homepage = 'http://www.boost.org/'
description = """Boost provides free peer-reviewed portable C++ source libraries."""
toolchain = {'name': 'intel', 'version': '2017b'}
toolchainopts = {'pic': True, 'usempi': True}
source_urls = [SOURCEFORGE_SOURCE]
sources = ['%%(namelower)s_%s.tar.gz' % '_'.join(version.split('.'))]
dependencies = [
('bzip2', '1.0.6', '', True),
('zlib', '1.2.11', '', True),
]
configopts = '--with-libraries=serialization'
configopts = '--without-libraries=python'
# also build boost_mpi
boost_mpi = True
moduleclass = 'devel'

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name = 'GROMACS'
version = '2018'
homepage = 'http://www.gromacs.org'
description = """GROMACS is a versatile package to perform molecular dynamics,
i.e. simulate the Newtonian equations of motion for systems with hundreds to millions of particles."""
toolchain = {'name': 'intel', 'version': '2017b'}
toolchainopts = {'openmp': True, 'usempi': True}
source_urls = ['http://ftp.gromacs.org/pub/gromacs/']
sources = [SOURCELOWER_TAR_GZ]
checksums = ['6467ffb1575b8271548a13abfba6374c']
configopts = ' -DGMX_GPU=OFF -DBUILD_SHARED_LIBS=OFF -DGMX_PREFER_STATIC_LIBS=ON -DGMX_DOUBLE=OFF -DGMX_SIMD=AVX2_256 -DGMX_BUILD_OWN_FFTW=ON -DGMX_MPI=ON'
builddependencies = [
('CMake', '3.9.1', '', True),
]
dependencies = [
('Boost', '1.66.0', '-serial'),
]
sanity_check_paths = {
'files': ['bin/gmx_mpi'],
'dirs': [''],
}
moduleclass = 'bio'

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# IT4Innovations 2018
easyblock = 'PythonPackage'
name = 'scikit-learn'
version = '0.19.1'
versionsuffix = '-Python-%(pyver)s'
homepage = 'http://scikit-learn.org/stable/index.html'
description = """Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world,
building upon numpy, scipy, and matplotlib. As a machine-learning module,
it provides versatile tools for data mining and analysis in any field of science and engineering.
It strives to be simple and efficient, accessible to everybody, and reusable in various contexts."""
toolchain = {'name': 'dummy', 'version': ''}
source_urls = [PYPI_SOURCE]
sources = [SOURCE_TAR_GZ]
dependencies = [
('Python', '3.6.1'),
('matplotlib', '2.0.2', versionsuffix + '-libpng-1.6.29'),
]
options = {'modulename': 'sklearn'}
sanity_check_paths = {
'files': [],
'dirs': ['lib/python%(pyshortver)s/site-packages/sklearn'],
}
moduleclass = 'data'