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}
}