GaussianK-SGD

Introduction

This repository contains the codes for the paper: Understanding Top-k Sparsification in Distributed Deep Learning. Key features include

• Distributed training with gradient sparsification.
• Measurement of gradient distribution on various deep learning models including feed foward neural networks (FFNs), CNNs and LSTMs.
• A computing-efficient top-k approximation (called gaussian-k) for gradient sparsification.

For more details about the algorithm, please refer to our papers.

Installation

Prerequisites

• Python 2 or 3
• PyTorch-0.4.+
• OpenMPI-3.1.+
• Horovod-0.14.+

Quick Start

git clone https://github.com/hclhkbu/GaussianK-SGD.git
cd GaussianK-SGD
HOROVOD_GPU_ALLREDUCE=NCCL pip install --no-cache-dir horovod (optional if horovod has been installed)
pip install -r requirements.txt
dnn=resnet20 nworkers=4 compressor=topk density=0.001 ./run.sh

Assume that you have 4 GPUs on a single node and everything works well, you will see that there are 4 workers running at a single node training the ResNet-20 model with the Cifar-10 data set using SGD with top-k sparsification.

Papers

• S. Shi, X.-W. Chu, K. Cheung and S. See, “Understanding Top-k Sparsification in Distributed Deep Learning,” 2019.

Referred Models

• Deep speech: https://github.com/SeanNaren/deepspeech.pytorch
• PyTorch examples: https://github.com/pytorch/examples