Installation: Use pipenv to install required packages by running pipenv install in the top directory containing Pipfile.lock and start up the virtual environment with pipenv shell.
The SLURM job scripts to obtain MNIST training results for both SGD and entropy-SGD runs are contained in the directory job_scripts. Parameters and RNG seeds used in the paper are written as default in the scripts. Each will produce an output directory.
Once those jobs are completed, point dir1, dir2 in plot_mnist_expt.ipynb to the resulting output directories to generate plots.
Point directories in run_local_coeff_computation.py the same output directories and and run python run_local_coeff_computation.py to produce local learning coefficient estimates on many combinations of hyper parameters.
This experiment is handled by sacred with MongoDB observer running on localhost:27017, you might need mongodb database process running on your computer or change to a different observer.
Run python parallel_sgld_2dnormalcrossing_experiments.py to reproduce the 2D normal crossing potential experiments for the same combination of parameters and RNG seeds. This experiment is again observed by sacred + MongoDB.
In plot_toypotential_expt.ipynb, point the document database filter {"status": "COMPLETED", "expt_name": YOUR_EXPT_NAME} to the correct name to generate plots and tables for these experiments.
Run
python two_component_potential_experiment.py --num_repeat 100 --outputdir ./outputs/twocomp/expt_migration_test/ --max_workers 6 --num_training_data 1000
to reproduce the two component potential experiments. The corresponding histogram and json result file will be saved at the specified output directory.
One can point plot_toypotential_expt.ipynb, point RESULT_DIR to corresponding output directory to read in the result and generate the same histogram.