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* E4S: Conservatively add ecp-data-vis-sdk
* Remove ascent from CUDA SDK stack to stop hanging on Dray
* Adios2: Newer FindPython uses Python_EXECUTABLE
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This adds super-lazy maintainer mode to `spack checksum`: Instead of
only printing the new checksums to the terminal, `-a` and
`--add-to-package` will add the new checksums to the `package.py` file
and open it in the editor afterwards for final checks.
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This reverts commit 7f9af8d4a0bfbb1577e5ac9982624d8d0cb9b9ca.
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Co-authored-by: becker33 <becker33@users.noreply.github.com>
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These commands are slated for removal in v0.20
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This PR allows you to do:
```
spack env create -d .
spack -e . add python
spack -e . concretize
spack -e . env depfile -o Makefile
make in<tab> # -> install
make install-<tab> # -> install-deps/
make install-deps/py<tab> # -> install-deps/python-x.y.z-hash
make install/zl<tab> # -> install/zlib-x.y.z-hash
make SP<tab> # -> make SPACK
make SPACK_<tab> # -> make SPACK_INSTALL_FLAGS=
```
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Environments and environment views have taken over the role of `spack activate/deactivate`, and we should deprecate these commands for several reasons:
- Global activation is a really poor idea:
- Install prefixes should be immutable; since they can have multiple, unrelated dependents; see below
- Added complexity elsewhere: verification of installations, tarballs for build caches, creation of environment views of packages with unrelated extensions "globally activated"... by removing the feature, it gets easier for people to contribute, and we'd end up with fewer bugs due to edge cases.
- Environment accomplish the same thing for non-global "activation" i.e. `spack view`, but better.
Also we write in the docs:
```
However, Spack global activations have two potential drawbacks:
#. Activated packages that involve compiled C extensions may still
need their dependencies to be loaded manually. For example,
``spack load openblas`` might be required to make ``py-numpy``
work.
#. Global activations "break" a core feature of Spack, which is that
multiple versions of a package can co-exist side-by-side. For example,
suppose you wish to run a Python package in two different
environments but the same basic Python --- one with
``py-numpy@1.7`` and one with ``py-numpy@1.8``. Spack extensions
will not support this potential debugging use case.
```
Now that environments are established and views can take over the role of activation
non-destructively, we can remove global activation/deactivation.
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Use at most 32 jobs when available.
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"spack install foo" no longer adds package "foo" to the environment
(i.e. to the list of root specs) by default: you must specify "--add".
Likewise "spack uninstall foo" no longer removes package "foo" from
the environment: you must specify --remove. Generally this means
that install/uninstall commands will no longer modify the users list
of root specs (which many users found problematic: they had to
deactivate an environment if they wanted to uninstall a spec without
changing their spack.yaml description).
In more detail: if you have environments e1 and e2, and specs [P, Q, R]
such that P depends on R, Q depends on R, [P, R] are in e1, and [Q, R]
are in e2:
* `spack uninstall --dependents --remove r` in e1: removes R from e1
(but does not uninstall it) and uninstalls (and removes) P
* `spack uninstall -f --dependents r` in e1: will uninstall P, Q, and
R (i.e. e2 will have dependent specs uninstalled as a side effect)
* `spack uninstall -f --dependents --remove r` in e1: this uninstalls
P, Q, and R, and removes [P, R] from e1
* `spack uninstall -f --remove r` in e1: uninstalls R (so it is
"missing" in both environments) and removes R from e1 (note that e1
would still install R as a dependency of P, but it would no longer
be listed as a root spec)
* `spack uninstall --dependents r` in e1: will fail because e2 needs R
Individual unit tests were created for each of these scenarios.
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* py-transformers: add v4.24.0
* Internet access still required
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`coverage` sometimes failed to combine, even if there were multiple reports.
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This reverts commit b1559cc831620ee2b2cf8e57fdecc5bb3bf8edfd.
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* Deprecate spack bootstrap trust/untrust
* Update CI
* Update tests
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This commit extends the DSL that can be used in packages
to allow declaring that a package uses different build-systems
under different conditions.
It requires each spec to have a `build_system` single valued
variant. The variant can be used in many context to query, manipulate
or select the build system associated with a concrete spec.
The knowledge to build a package has been moved out of the
PackageBase hierarchy, into a new Builder hierarchy. Customization
of the default behavior for a given builder can be obtained by
coding a new derived builder in package.py.
The "run_after" and "run_before" decorators are now applied to
methods on the builder. They can also incorporate a "when="
argument to specify that a method is run only when certain
conditions apply.
For packages that do not define their own builder, forwarding logic
is added between the builder and package (methods not found in one
will be retrieved from the other); this PR is expected to be fully
backwards compatible with unmodified packages that use a single
build system.
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* Fast Gitlab CI job setup, and better legibility
* Use a non-broken, recent GNU Make
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* Use spack bootstrap now in containers
* Fix wrong path glob expression
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Delete code removing the symlink during CI
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Use --backtrace in ci instead of --debug to reduce verbosity
and don't show log on error, since log is already printed
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* backtraces without --debug
Currently `--debug` is too verbose and not-`--debug` gives to little
context about where exceptions are coming from.
So, instead, it'd be nice to have `spack --backtrace` and
`SPACK_BACKTRACE=1` as methods to get something inbetween: no verbose
debug messages, but always a full backtrace.
This is useful for CI, where we don't want to drown in debug messages
when installing deps, but we do want to get details where something goes
wrong if it goes wrong.
* completion
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When we lose a running pod (possibly loss of spot instance) or encounter
some other infrastructure-related failure of this job, we need to retry
it. This retries the job the maximum number of times in those cases.
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`reuse` and `when_possible` concretization broke the invariant that
`spec[pkg_name]` has unique keys. This invariant is relied on in tons of
places, such as when setting up the build environment.
When using `when_possible` concretization, one may end up with two or
more `perl`s or `python`s among the transitive deps of a spec, because
concretization does not consider build-only deps of reusable specs.
Until the code base is fixed not to rely on this broken property of
`__getitem__`, we should disable reuse in CI.
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When installing some/all specs from a buildcache, build edges are pruned
from those specs. This can result in a much smaller effective DAG. Until
now, `spack env depfile` would always generate a full DAG.
Ths PR adds the `spack env depfile --use-buildcache` flag that was
introduced for `spack install` before. This way, not only can we drop
build edges, but also we can automatically set the right buildcache
related flags on the specific specs that are gonna get installed.
This way we get parallel installs of binary deps without redundancy,
which is useful for Gitlab CI.
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Currently "spack ci generate" chooses the first matching entry in
gitlab-ci:mappings to fill attributes for a generated build-job,
requiring that the entire configuration matrix is listed out
explicitly. This unfortunately causes significant problems in
environments with large configuration spaces, for example the
environment in #31598 (spack.yaml) supports 5 operating systems,
3 architectures and 130 packages with explicit size requirements,
resulting in 1300 lines of configuration YAML.
This patch adds a configuraiton option to the gitlab-ci schema called
"match_behavior"; when it is set to "merge", all matching entries
are applied in order to the final build-job, allowing a few entries
to cover an entire matrix of configurations.
The default for "match_behavior" is "first", which behaves as before
this commit (only the runner attributes of the first match are used).
In addition, match entries may now include a "remove-attributes"
configuration, which allows matches to remove tags that have been
aggregated by prior matches. This only makes sense to use with
"match_behavior:merge". You can combine "runner-attributes" with
"remove-attributes" to effectively override prior tags.
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* env depfile: allow deps only install
- Refactor `spack env depfile` to use a Jinja template, making it a bit
easier to follow as a human being.
- Add a layer of indirection in the generated Makefile through an
`<prefix>/.install-deps/<hash>` target, which allows one to specify
different options when installing dependencies. For example, only
verbose/debug mode on when installing some particular spec:
```
$ spack -e my_env env depfile -o Makefile --make-target-prefix example
$ make example/.install-deps/<hash> -j16
$ make example/.install/<hash> SPACK="spack -d" SPACK_INSTALL_FLAGS=--verbose -j16
```
This could be used to speed up `spack ci rebuild`:
- Parallel install of dependencies from buildcache
- Better readability of logs, e.g. reducing verbosity when installing
dependencies, and splitting logs into deps.log and current_spec.log
* Silence please!
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Caches used by repositories don't reference the global spack.repo.path instance
anymore, but get the repository they refer to during initialization.
Spec.virtual now use the index, and computation done to compute the index
use Repository.is_virtual_safe.
Code to construct mock packages and mock repository has been factored into
a unique MockRepositoryBuilder that is used throughout the codebase.
Add debug print for pushing and popping config scopes.
Changed spack.repo.use_repositories so that it can override or not previous repos
spack.repo.use_repositories updates spack.config.config according to the modifications done
Removed a peculiar behavior from spack.config.Configuration where push would always
bubble-up a scope named command_line if it existed
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Basic stack of ML packages we would like to test and generate binaries for in CI.
Spack now has a large CI framework in GitLab for PR testing and public binary generation.
We should take advantage of this to test and distribute optimized binaries for popular ML
frameworks.
This is a pretty extensive initial set, including CPU, ROCm, and CUDA versions of a core
`x96_64_v4` stack.
### Core ML frameworks
These are all popular core ML frameworks already available in Spack.
- [x] PyTorch
- [x] TensorFlow
- [x] Scikit-learn
- [x] MXNet
- [x] CNTK
- [x] Caffe
- [x] Chainer
- [x] XGBoost
- [x] Theano
### ML extensions
These are domain libraries and wrappers that build on top of core ML libraries
- [x] Keras
- [x] TensorBoard
- [x] torchvision
- [x] torchtext
- [x] torchaudio
- [x] TorchGeo
- [x] PyTorch Lightning
- [x] torchmetrics
- [x] GPyTorch
- [x] Horovod
### ML-adjacent libraries
These are libraries that aren't specific to ML but are still core libraries used in ML pipelines
- [x] numpy
- [x] scipy
- [x] pandas
- [x] ONNX
- [x] bazel
Co-authored-by: Jonathon Anderson <17242663+blue42u@users.noreply.github.com>
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