Generalization_Bound_Toolbox
Tools related to computing generlization error bounds for machine-learning applications. Note that standard use depends on the domain of the target functions to be
For directions on use, check out
tests/test_bound.py
tests/TestProductSinesCompression.ipynb
tests/TestProductSines.ipynb.
Installation
A release version is availalbe on PyPI. Currently requires Python version less than 3.12 and greater than 3.7.
pip install gbtoolbox
pip install gbtoolbox[GPU11]
pip install gbtoolbox[GPU12]
The following should be performed to manually install. See pyproject.toml for dependencies.
Install the build package
pip install build
First, from the same directory as this README, build the sdist and wheel using the following command.
python -m build
Then install the wheel (*** indicates text that is version and user specific)
pip install dist/gbtoolbox-***.whl
Build and install the c files
cd src/gbtoolbox
make && make install
Update your ld_library_path environment variable
export LD_LIBRARY_PATH=/usr/local/lib/gbtoolbox/
You may want to add the previous export statement to your ~/.bashrc file, otherwise the change is only for the currently open session.
CUDA
There is a legacy CUDA version of nu_dft that runs much faster than the C version, but that runs slower than the cupy version. The following should be helpful for getting set up to run the legacy CUDA version
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 cuda-toolkit=11.6 numba python-build scipy -c pytorch -c nvidia
conda install pytorch torchvision torchaudio pytorch-cuda cuda-toolkit numba python-build scipy -c pytorch-nightly -c nvidia
Information about pytorch is available at https://pytorch.org/.
Reference
This toolbox was developed by a collaboration between Euler Scientific ( www.euler-sci.com ) and Fermilab ( www.fnal.gov ). Papers are in progress. Initial developmenet was made possible by the National Geospatial-Intelligence Agency (NGA) under Contract No. HM047622C0003.
The central theory behind this was initially developed by Barron and then extended by E et al. Details in
https://arxiv.org/abs/1810.06397
https://arxiv.org/abs/2009.10713
