Code for the experimental part of the paper Sparsified SGD with Memory. It contains the code for the following experiments:
- Theoretical convergence with different sparsification operator
- Comparison with QSGD
- Multi-core experiments
Use notebooks/plots.ipynb
to visualize the results.
Please open an issue if you have questions or problems.
Install Anaconda and create the sparsifedSGD
environment
conda env create -f environment.yaml
source activate sparsifedSGD
...
source deactivate # at the end
For LaTeX support in plots
sudo apt-get install texlive-full msttcorefonts
To reproduce the results, you can download the datasets from LibSVM
mkdir data
cd data/
wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/rcv1_test.binary.bz2
wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/epsilon_normalized.bz2
We decompress the libsvm file and use pickle format instead. It takes more space but is faster to load. You can create a file as follow
import pickle
import os
from sklearn.datasets import load_svmlight_file
if not os.path.exists('data'):
os.makedirs('data')
X, y = load_svmlight_file('data/rcv1_test.binary.bz2')
with open('rcv1.pickle', 'wb') as f:
pickle.dump((X, y), f)
X, y = load_svmlight_file('data/epsilon_normalized.bz2')
with open('epsilon.pickle', 'wb') as f:
pickle.dump((X, y), f)
You can run the baseline
python experiments/baselines.py ./data results/baselines
Run our experiments, for example
python experiments/rcv-th.py ./data results/rcv-th
python experiments/rcv-par.sh ./data results/rcv-par
And visualize the results with the notebooks.
If you use this code, please cite the following paper
@inproceedings{scj2018sparseSGD,
author = {Sebastian U. Stich and Jean-Baptiste Cordonnier and Martin Jaggi},
title = "{Sparsified {SGD} with Memory}",
booktitle = {NIPS 2018 - Advances in Neural Information Processing Systems},
year = 2018,
url = {https://arxiv.org/abs/1809.07599}
}