# DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

arXiv: https://arxiv.org/abs/1602.08191 · HTML: https://mlsanity.com/paper/1602.08191/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2016.

## Built on by (most cited first, 2 of 2)

- [A Survey on Class Imbalance in Federated Learning](https://mlsanity.com/paper/2303.11673/) (2023)
- [On the Efficiency of Privacy Attacks in Federated Learning](https://mlsanity.com/paper/2404.09430/) (2024)

## Abstract (arXiv)

The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operations, DeepSpark automatically distributes workloads and parameters to Caffe/Tensorflow-running nodes using Spark, and iteratively aggregates training results by a novel lock-free asynchronous variant of the popular elastic averaging stochastic gradient descent based update scheme, effectively complementing the synchronized processing capabilities of Spark. DeepSpark is an on-going project, and the current release is available at http://deepspark.snu.ac.kr.