A Resource Effective Approach for Distributed Machine Learning over a Local Network
Aarish Iyer, Haramrit Singh Khurana, Ivan Maria, Amiya Kumar Tripathy · 2019
Areas of computer science such as data science and machine learning have boomed in recent years due to the increase in the availability of data. This has led to a need in the upsurge of computational power of various systems to handle large datasets consisting of images, text, audios, etc. However, training a machine learning model on a single node system can prove to be a tedious as well as time consuming task. One of the solutions to this problem is distributing the model training process. Some of the current solutions that allow distributed machine learning are limited to multiple GPUs on a single node. This work attempts to propose a system which aims to solve this problem by providing a platform that allows distributing the machine learning process over a local network (using locally available nodes). This will ensure the effective use of all the resources available.