Some "concrete" versions on the command line, e.g. `qt@5` are really
meant to satisfy some actual concrete version from a package. We should
only assume the user is introducing a new, unknown version on the CLI
if we, well, don't know of any version that satisfies the user's
request. So, if we know about `5.11.1` and `5.11.3` and they ask for
`5.11.2`, we'd ask the solver to consider `5.11.2` as a solution. If
they just ask for `5`, though, `5.11.1` or `5.11.3` are fine solutions,
as they satisfy `@5`, so use them.
Co-authored-by: Harmen Stoppels <harmenstoppels@gmail.com>
* geant4-data: use build+run-only depends
* geant4: point to dependent datadir
This is "used" in the configure step to set up the Geant4Config.cmake
file's persistent pointers to the data directory, but the dependency
is still listed as "run" -- though I'm not sure this is the right behavior
since the geant4 installation really does change as a function of the
data directory, and the installation is incomplete/erroneous
without using one.
* Style
* trilinos: disable dl on macOS
* py-sphinx-argparse: add explicit poetry dependency
* libzmq: fix libbsd dependency
libbsd is *always* required when +libbsd (introduced in #28503) . #20893
had previously removed the macos dependency because libbsd wasn't always
enabled. Libbsd support is only available after 4.3.2 so change it to a
conflict rather than bumping the dependency.
* hdf5: work around GCC11.2 monterey fortran bug
* go-bootstrap: mark conflict for monterey
* py-tensorflow: add versions 2.5.0 and 2.6.0
- add version 2.5.0
- add version 2.6.0
- add patches for newer protobuf
- set constraints
* Remove import os. left over from testing
* Remove unused patch file
* Update var/spack/repos/builtin/packages/py-tensorflow/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Update var/spack/repos/builtin/packages/py-tensorflow/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Update var/spack/repos/builtin/packages/py-tensorflow/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Update var/spack/repos/builtin/packages/py-tensorflow/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Update var/spack/repos/builtin/packages/py-tensorflow/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Update var/spack/repos/builtin/packages/py-tensorflow/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Add py-clang dependency
* Adjust py-clang constraint
* Build tensorflow with tensorboard
- tensorflow
- added 2.6.1 and 2.6.2 versions
- tensorboard
- have bazel use number of jobs set by spack
- add versions and constraints
- new package: py-tensorboard-data-server
- use wheel for py-tensorboard-plugin-wit
This package can not build with newer versions of bazel that are
needed for newer versions of py-tensorboard.
* Update var/spack/repos/builtin/packages/py-clang/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Remove empty line at end of file
* Fix import sorting
* Adjust python dependencies on py-clang
* Add version 2.7.0 of pt-tensorflow and py-tensorboard
* Adjust bazel constraints
* bazel-4 support begins with py-tensorflow-2.7.0
* Adjust dependencies
* Loosen cuda constraint on versions > 2.5
Tensorflow-2.5 and above can use cuda up to version 11.4.
* Add constraints to patch
The 0008-Fix-protobuf-errors-when-using-system-protobuf.patch patch
should only apply to versions 2.5 and above.
* Adjust constraints
- versions 2.4 and below need protobuf-3.12 and below
- versions 2.4 and above can use up to cuda-11.4
- versions 2.2 and below can not use cudnn-8
- the null_linker_bin patch should only be applied to versions 2.5 and
above.
* Update var/spack/repos/builtin/packages/py-tensorflow/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Update var/spack/repos/builtin/packages/py-tensorflow/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Fix py-grpcio dependency for version 2.7
Also, make sure py-h5py mpi specs are consistent.
* Add llvm as run dependency.
* Fix python spec for py-tensorboard
* Fix py-google-auth spec for py-tensorboard
* Do not override the pip spec for tensorboard-plugin-wit
* Converted py-tensorboard-plugin-wit to wheel only package
* Fix bazel dependency spec in tensorflow
* Adjust pip masks
- allow tensorboard to be specified in pip constraints
- mask tensorflow-estimator
* Remove blank line at end of file
* Adjust pip constraints in setup.py
Also, adjust constraint on a patch that is fixed in 2.7
* Fix flake8 error
Adjust formatting for consistency.
* Get bazel dep right
* Fix old cudnn dependency, caught in audit test
* Adjust the regex to ensure proper line is changed
* Add py-libclang package
- Stripped the py-clang package down to just version 5
- added comments to indicate the purpose of py-clang and that
py-libclang should be preferred
- set dependencies accordingly in py-tensorflow
* Remove cap on py-h5py dependency for v2.7
* Add TODO entries for tensorflow-io-gcs-filesystem
* Edit some comments
* Add phases and select python in PATH for tensorboard-data-server
* py-libclang
- remove py-wheel dependency
- remove raw string notation in filter_file
* py-tensorboard-data-server
- remove py-wheel dep
- remove py-pip dep
- use python from package class
* py-tensorboard-plugin-wit
- switch to PythonPackage
- add version 1.8.1
- remove unneeded code
* Add comment as to why a wheel is need for tensorboard-plugin-wit
* remove which pip from tensorboard-data-server
* Fix dependency specs in tensorboard
* tweak dependencies for tensorflow
* fix python constraint
* Use llvm libs property
* py-tensorboard-data-server
- merge build into install
- use std_pip_args
* remove py-clang dependency
* remove my edits to py-tensorboard-plugin-wit
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
See https://github.com/spack/spack/issues/25353#issuecomment-1041868116
This commit changes the default behavior of
```
$ spack external find
```
from searching all the possible packages Spack knows about to
search only for the ones tagged as being a "build-tool".
It also introduces a `--all` option to restore the old behavior.
Prefer `sw_vers` to `platform.mac_ver`. In anaconda3 installation, for example, the latter reports 10.16 on Monterey -- I think this is affected by how and where the python instance was built.
Use MACOSX_DEPLOYMENT_TARGET if present to override the operating system choice.
It will be useful for metrics gathering and possibly debugging to
have this environment variable available in the runner pods that
do the actual rebuilds.
