- Parallel HDF5 isn't required -- the comment seems to be about a
transitive dependency with pnetcdf.
- Boost usage should respect the variant, not automatically be reenabled
when choosing DTK.
Versions of py-tensorflow between versions 1.1 and 1.14 need a patch to
avoid an import error on the cloud package even if built without support
for the cloud package.
* bazel: Update for use with Fujitsu compiler
* bazel: Fix for use with Fujitsu compiler
* bazel: Fix flake8 error
* bazel: add conflicts setting for use with Fujitsu compiler
* fix flake8 error
* fix flake8 error
In Python 3.8, the reserved "tp_print" slot was changed from a function
pointer to a number, which broke the Python wrapping code in vtk@8
(causing "cannot convert 'std::nullptr_t' to 'Py_ssize_t'" errors in
various places). This is fixed in vtk@9.0.0.
This patch:
1) adds vtk@9.0.0
2) updates depends_on constraints to only use python@3.8: for vtk@9:
vtk@:8 depends on python@2, and vtk@8.0.1:8.9.9 depends on python@:3.7
3) Adds CMake flag VTK_PYTHON_VERSION=3 when using python@3 with vtk@9
* python: adding a distutils fix to improve build compatibility for C++ extension modules (e.g. py-matplotlib)
* python: added C/C++ distutils patches for python@3.6:3.8
Allow Spack to build with ROOT as an external dependency by setting
LD_LIBRARY_PATH: given that the external package was not built by
Spack, dependents would not be able to locate libraries using RPATHs
when running ROOT binaries.
Specified Python to be v2.7 only, as Python3 support is not currently
implemented in chill.
Update chill dependency versions for the following libraries to the
specific versions:
* rose: v0.9.13.0
* bison: v3.4.2
Both rose and iegenlib are build time dependencies, but are also run
time dependencies. Added 'run' to the build type for both dependencies.
cuda: 10.1 and onward, installers will crash if /tmp/cuda-installer.log
exists
Try to help if user owns the file, otherwise try to provide useful
info. Clean up the file post-install to try to avoid the whole issue.
The release number in the README had not been updated since we did the
relicense to Apache-2.0 OR MIT in v0.12.0. LLNL-CODE-811652 is Spack's
new LLNL release number.
* save edits
* tidy up
* Update var/spack/repos/builtin/packages/py-lmfit/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* add python version constraints
Co-authored-by: Sinan81 <sbulut@3vgeomatics.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
Co-authored-by: Sinan81 <Sinan81@github>
