* 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.
Since Spack does not install external packages, this commit skips them by
default when running stand-alone tests. The assumption is that such packages
have likely undergone an acceptance test process.
However, the tests can be run against installed externals using
```
% spack test run --externals ...
```
fixes#28260
Since we iterate over different variants from many packages, the variant
values may have types which are not comparable, which causes errors
at runtime. This is not a real issue though, since we don't need the facts
to be ordered. Thus, to avoid needless sorting, the sorted function has
been removed and a comment has been added to tip any developer that
might need to inspect these clauses for debugging to add back sorting
on the first two items only.
It's kind of difficult to add a test for this, since the error depends on
whether Python sorting algorithm ever needs to compare the third
value of a tuple being ordered.
* extensions: allow multiple "extends" directives
This will allow multiple extends directives in a package as long as only one of
them is selected as a dependency in the concrete spec.
* document the option to have multiple extends
Reuse previously was a very invasive change that required parameters to be added to all
the methods that called `concretize()` on a `Spec` object. With the addition of
concretizer configuration, we can use the config system to simplify this argument
passing and keep the code cleaner.
We decided that concretizer config options should be read at `Solver` instantiation
time, and if config changes between instnatiation of a particular solver and
`solve()` invocation, the `Solver` should use the settings from `__init__()`.
- [x] remove `reuse` keyword argument from most concretize functions
- [x] refactor usages to use `spack.config.override("concretizer:reuse", True)`
- [x] rework argument passing in `Solver` so that parameters are set from config
at instantiation time
`--reuse` was previously handled individually by each command that
needed it. We are growing more concretization options, and they'll
need their own section for commands that support them.
Now there are two concretization options:
* `--reuse`: Attempt to reuse packages from installs and buildcaches.
* `--fresh`: Opposite of reuse -- traditional spack install.
To handle thes, this PR adds a `ConfigSetAction` for `argparse`, so
that you can write argparse code like this:
```
subgroup.add_argument(
'--reuse', action=ConfigSetAction, dest="concretizer:reuse",
const=True, default=None,
help='reuse installed dependencies/buildcaches when possible'
)
```
With this, you don't need to add logic to pull the argument out and
handle it; the `ConfigSetAction` just does it for you. This can probably
be used to clean up some other commands later, as well.
Code that was previously passing `reuse=True` around everywhere has
been refactored to use config, and config is set from the CLI using
a new `add_concretizer_args()` function in `spack.cmd.common.arguments`.
- [x] Add `ConfigSetAction` to simplify concretizer config on the CLI
- [x] Refactor code so that it does not pass `reuse=True` to every function.
- [x] Refactor commands to use `add_concretizer_args()` and to pass
concretizer config using the config system.
Config scopes were different for `config` and `mutable_config`,
and `mutable_config` did not have a command line scope.
- [x] Fix by consolidating the creation logic for the two fixtures.
The concretizer is going to grow to have many more configuration,
and we really need some structured config for that.
* We have the `config:concretizer` option that chooses the solver,
but extending that is awkward (we'd need to replace a string with
a `dict`) and the solver choice will be deprecated eventually.
* We have the `concretization` option in environments, but it's
not a top-level config section -- it's just for environments,
and it also only admits a string right now.
To avoid overlapping with either of these and to allow the most
extensibility in the future, this adds a new `concretizer` config
section that can be used in and outside of environments. There
is only one option right now: `reuse`. This can expand to include
other options later.
Likely, we will soon deprecate `config:concretizer` and warn when
the user doesn't use `clingo`, and we will eventually (sometime later)
move the `together` / `separately` options from `concretization` into
the top-level `concretizer` section.
This commit just adds the new section and schema. Fully wiring it
up is TBD.
The solver has a lot of configuration associated with it. Rather
than adding arguments to everything, we should encapsulate that
in a class. This is the start of that work; it replaces `solve()`
and its kwargs with a class and properties.
* Add 'stable' to the list of infinity version names.
Rename libunwind 1.5-head to 1.5-stable.
* Add stable to the infinite version list in packaging_guide.rst.
* py-etelemetry: add 0.3.0
* Update var/spack/repos/builtin/packages/py-etelemetry/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
No version of py-nbconvert@5: can be concretized due to conflicting versions
of flit-core that are required. This issue could be solved by separate
concretization of build deps.
