01ede3c595
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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