Add CI stack for ML packages (#31592)

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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