Commit a0b0c981 authored by easybuild's avatar easybuild

new file: b/Bazel/Bazel-0.5.4-GCC-7.1.0-2.28.eb

	modified:   b/Bazel/CROSSTOOL.patch
	new file:   k/Keras/Keras-2.1.2-Tensorflow-1.2.1-Python-3.6.1.eb
	new file:   l/LAMMPS/LAMMPS-17Jan2018-intel-2017a.eb
	new file:   o/OpenSSL/OpenSSL-1.0.2n.eb
	new file:   t/Tensorflow/Tensorflow-1.2.1-Python-3.6.1.eb
	deleted:    l/LAMMPS/log.lammps
parent 3543423b
# @author: Robert Schmidt (OHRI)
# @author: Guilherme Peretti-Pezzi (CSCS)
easyblock = "CmdCp"
name = 'Bazel'
version = '0.5.4'
homepage = 'http://bazel.io/'
description = """Bazel is a build tool that builds code quickly and reliably.
It is used to build the majority of Google's software."""
toolchain = {'name': 'GCC', 'version': '7.1.0-2.28'}
sources = ['%(namelower)s-%(version)s-dist.zip']
source_urls = ['https://github.com/bazelbuild/bazel/releases/download/%(version)s']
patches = [
('cc_configure.patch'),
('CROSSTOOL.patch'),
]
prebuildopts = "export EXTRA_BAZEL_ARGS='--ignore_unsupported_sandboxing --genrule_strategy=standalone --spawn_strategy=standalone --jobs=24' && "
dependencies = [
('Java', '1.8.0_121', '', True),
]
cmds_map = [('.*', "./compile.sh")]
files_to_copy = [(['output/bazel'], 'bin')]
sanity_check_paths={
'files': ['bin/bazel'],
'dirs': [],
}
moduleclass = 'devel'
......@@ -3,7 +3,7 @@
@@ -100,34 +100,34 @@
target_system_name: "local"
toolchain_identifier: "local_linux"
- tool_path { name: "ar" path: "/usr/bin/ar" }
- tool_path { name: "compat-ld" path: "/usr/bin/ld" }
- tool_path { name: "cpp" path: "/usr/bin/cpp" }
......@@ -19,7 +19,7 @@
- linker_flag: "-B/usr/bin/"
+ linker_flag: "-lstdc++, -W1"
+ linker_flag: "-B/apps/all/binutils/2.28-GCCcore-7.1.0/bin"
# TODO(bazel-team): In theory, the path here ought to exactly match the path
# used by gcc. That works because bazel currently doesn't track files at
# absolute locations and has no remote execution, yet. However, this will need
......@@ -32,12 +32,12 @@
+ cxx_builtin_include_directory: "/apps/all/GCCcore/7.1.0/lib/gcc/x86_64-pc-linux-gnu/7.1.0/include-fixed/"
+ cxx_builtin_include_directory: "/apps/all/GCCcore/7.1.0/include/c++/7.1.0"
+ tool_path { name: "gcov" path: "/apps/all/GCCcore/7.1.0/bin/gcov" }
# C(++) compiles invoke the compiler (as that is the one knowing where
# to find libraries), but we provide LD so other rules can invoke the linker.
- tool_path { name: "ld" path: "/usr/bin/ld" }
+ tool_path { name: "ld" path: "/apps/all/binutils/2.28-GCCcore-7.1.0/bin/ld" }
- tool_path { name: "nm" path: "/usr/bin/nm" }
- tool_path { name: "objcopy" path: "/usr/bin/objcopy" }
+ tool_path { name: "nm" path: "/apps/all/binutils/2.28-GCCcore-7.1.0/bin/nm" }
......@@ -48,6 +48,6 @@
- tool_path { name: "strip" path: "/usr/bin/strip" }
+ tool_path { name: "objdump" path: "/apps/all/binutils/2.28-GCCcore-7.1.0/bin/objdump" }
+ tool_path { name: "strip" path: "/apps/all/binutils/2.28-GCCcore-7.1.0/bin/strip" }
# Anticipated future default.
unfiltered_cxx_flag: "-no-canonical-prefixes"
easyblock = 'PythonPackage'
name = 'Keras'
version = '2.1.2'
versionsuffix = '-Tensorflow-1.2.1-Python-%(pyver)s'
homepage = 'https://keras.io/'
description = """Keras is a minimalist, highly modular neural networks library, written in Python and
capable of running on top of either TensorFlow or Theano."""
toolchain = {'name': 'dummy', 'version': ''}
source_urls = ['https://github.com/keras-team/keras/archive']
sources = ['%(version)s.tar.gz']
use_pip = True
dependencies = [
('Python', '3.6.1'),
('Tensorflow', '1.2.1'),
# ('h5py', '2.7.0', '%(versionsuffix)s'), internal Python 3.6.1
# ('PyYAML', '3.12', versionsuffix), internal Python 3.6.1
]
# it defaults to Tensorflow
modextravars = {'KERAS_BACKEND': 'tensorflow'}
sanity_check_paths = {
'files': [],
'dirs': ['lib/python%(pyshortver)s/site-packages'],
}
moduleclass = 'math'
# IT4Innovations 2017
easyblock = 'CmdCp'
name = 'LAMMPS'
version = '17Jan2018'
homepage = 'http://lammps.sandia.gov'
description = """LAMMPS is a classical molecular dynamics code,
and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator.
Has potentials for solid-state materials (metals, semiconductors) and soft
matter (biomolecules, polymers) and coarse-grained or mesoscopic systems.
It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic,
meso, or continuum scale.
"""
toolchain = {'name': 'intel', 'version': '2017a'}
source_urls = ['https://github.com/lammps/lammps/archive']
sources = ['patch_17Jan2018.tar.gz']
#patches = [
# 'LAMMPS-Makefile.kokkos_cuda_mpi.patch',
#]
dependencies = [
('tbb', '2017.6.196', '', True),
]
builddependencies = [
('Python', '2.7.13', '-base', True),
('CMake', '3.5.2', '', True)
]
commands = "cd src && make yes-kokkos && "
commands += "make yes-user-phonon && "
commands += "make lib-reax args='-m ifort' && make yes-reax &&"
commands += "make yes-kspace && make yes-manybody && make yes-molecule && "
commands += "make yes-qeq && make yes-rigid && make yes-user-misc && "
commands += "make yes-user-reaxc && make yes-user-omp && "
commands += "make -j 16 intel_cpu_intelmpi && mv lmp_intel_cpu_intelmpi lammps"
cmds_map = [('.*', commands)]
files_to_copy = [
(['src/lammps'], 'bin'),
]
sanity_check_paths = {
'files': ['bin/lammps'],
'dirs': [''],
}
moduleclass = 'chem'
LAMMPS (17 Jan 2018)
?
ERROR: Unknown command: ? (../input.cpp:246)
Last command: ?
name = 'OpenSSL'
version = '1.0.2n'
homepage = 'http://www.openssl.org/'
description = """The OpenSSL Project is a collaborative effort to develop a robust, commercial-grade, full-featured,
and Open Source toolchain implementing the Secure Sockets Layer (SSL v2/v3) and Transport Layer Security (TLS v1)
protocols as well as a full-strength general purpose cryptography library. """
toolchain = {'name': 'dummy', 'version': ''}
toolchainopts = {'pic': True}
sources = [SOURCELOWER_TAR_GZ]
source_urls = ['http://www.openssl.org/source/']
dependencies = [('zlib', '1.2.11', '', True)]
#runtest = 'test'
moduleclass = 'system'
easyblock = 'PythonPackage'
name = 'Tensorflow'
version = '1.2.1'
homepage = 'https://www.tensorflow.org/'
description = "An open-source software library for Machine Intelligence"
toolchain = {'name': 'dummy', 'version': ''}
pymaj = '3'
pymin = '6'
pymajmin = pymaj + pymin
#source_urls = ['https://pypi.python.org/packages/1f/38/c255432f18005362dd2459e3bff2b2411eb7d02ca09fa20ee1dd1311f69f/']
sources = [
'tensorflow-%(version)s-cp36-cp36m-linux_x86_64.whl',
]
dependencies = [
('Python', '%s.%s.1' % (pymaj, pymin)),
('GCC', '7.1.0-2.28'),
]
use_pip = True
unpack_sources = False
sanity_check_paths = {
'files': ['bin/tensorboard'],
'dirs': ['lib/python%(pyshortver)s/site-packages'],
}
moduleclass = 'lib'
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