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.
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>
