* Add checksum for py-msgpack 1.0.4
* Update var/spack/repos/builtin/packages/py-msgpack/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Add checksum for py-virtualenv 20.16.4
* Add checksum for py-werkzeug 2.2.2
* Restore py-virtualenv/package.py
* Update var/spack/repos/builtin/packages/py-werkzeug/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Docs: Getting Started Dependencies
Finally document what one needs to install to use Spack on
Linux and Mac :-)
With <3 for minimal container users and my colleagues with
their fancy Macs.
* Debian Update Packages: GCC, Python
- build-essential: includes gcc, g++ (thx Cory)
- Python: add python3-venv, python3-distutils (thx Pradyun)
* Add RHEL8 Dependencies
* Add checksum for py-skl2onnx 1.12
* Update var/spack/repos/builtin/packages/py-skl2onnx/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Update package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
* Add checksum for py-tables 3.7.0
* Update package.py
* Update var/spack/repos/builtin/packages/py-tables/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
ROOT has a webgui which is available with the `+root7` variant. This is a fairly large part of a ROOT install (275MB out of 732MB on my system) which is not necessarily useful in all use cases (e.g. inside containers on network-restricted HPC/HTC compute nodes). This new variant adds the option to retain the ROOT7 functionality but not necessarily include the `webgui` aspects.
`__unused__` defined in `general.h` conflict with the one defined by libc headers,
so change it to `__attribute__unused__` according to s.zharkoff:
https://bugs.gentoo.org/828550#c11
cmd:
`grep -rl "__unused__" . | xargs -n1 sed -i -e 's/\b__unused__\b/__attribute__unused__/g' -e 's/(unused)/(__unused__)/g'`
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>
* Add checksum for py-scikit-build 0.15.0 and use sources from pypi
* Update var/spack/repos/builtin/packages/py-scikit-build/package.py
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>
