* LLVM: Python Dependency
Effort to expose the linked python library when building LLVM.
This might fix the forward propagation of libintl that comes
with the static python library build on darwin.
* LLDB Py: Remove Ignored Old Flags
Changed in LLVM 10.0+
Packages in Spack are classes, and we need to be able to execute class
methods on mock packages. The previous design used instances of a single
MockPackage class; this version gives each package its own class that can
spider depenencies. This allows us to implement class methods like
`possible_dependencies()` on mock packages.
This design change moves mock package creation into the
`MockPackageMultiRepo`, and mock packages now *must* be created from a
repo. This is required for us to mock `possible_dependencies()`, which
needs to be able to get dependency packages from the package repo.
Changes include:
* `MockPackage` is now `MockPackageBase`
* `MockPackageBase` instances must now be created with
`MockPackageMultiRepo.add_package()`
* add `possible_dependencies()` method to `MockPackageBase`
* refactor tests to use new code structure
* move package mocking infrastructure into `spack.util.mock_package`,
as it's becoming a more sophisticated class and it gets lots in `conftest.py`
The variants table in `spack info` is cramped, as the *widest* it can be
is 80 columns. And that's actually only sort of true -- the padding
calculation is off, so it still wraps on terminals of size 80 because it
comes out *slightly* wider.
This change looks at the terminal size and calculates the width of the
description column based on it. On larger terminals, the output looks
much nicer, and on small terminals, the output no longer wraps.
Here's an example for `spack info qmcpack` with 110 columns.
Before:
Name [Default] Allowed values Description
==================== ==================== ==============================
afqmc [off] on, off Install with AFQMC support.
NOTE that if used in
combination with CUDA, only
AFQMC will have CUDA.
build_type [Release] Debug, Release, The build type to build
RelWithDebInfo
complex [off] on, off Build the complex (general
twist/k-point) version
cuda [off] on, off Build with CUDA
After:
Name [Default] Allowed values Description
==================== ==================== ========================================================
afqmc [off] on, off Install with AFQMC support. NOTE that if used in
combination with CUDA, only AFQMC will have CUDA.
build_type [Release] Debug, Release, The build type to build
RelWithDebInfo
complex [off] on, off Build the complex (general twist/k-point) version
cuda [off] on, off Build with CUDA
This allows horovod to be built with frameworks=pytorch,tensorflow.
I tracked down the crash I observed in #15719, where loading torch
before tensorflow would cause a crash in:
google::protobuf::internal::(anonymous
namespace)::InitSCC_DFS(google::protobuf::internal::SCCInfoBase*)
The solution is to make tensorflow compile against the protobuf
version Spack provides, instead of allowing it to use it's own.
It's likely we'll want to go after some of the others
that are listed in third_party/systemlibs/syslibs_configure.bzl
in the future.
* Update var/spack/repos/builtin/packages/py-sqlparse/package.py
Co-Authored-By: Adam J. Stewart <ajstewart426@gmail.com>
* Add new package py-sqlparse
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
The major building blocks in many software stacks:
- CPython
- CMake (libuv)
do not build on macOS with GCC. The main problem is that some macOS
framework includes pull in objective-c code and that code does get
misinterpreted as (invalid) C by GCC by default.
Last month VTK-m releases its lastest version named `v1.5.1`. This new
release only contains bugfixes related to compiler error / warnings.
- Depends on CMake >= 3.12
- Set VTKm_NO_ASSERT=ON by default
- add maintainers
Signed-off-by: Vicente Adolfo Bolea Sanchez <vicente.bolea@kitware.com>
