Memory visualization tool for training neural network

TL;DR

This paper introduces a memory visualization tool that helps analyze and understand memory usage during the training of deep learning models, aiding optimization and resource management.

Contribution

The paper presents a novel tool for real-time visualization of memory utilization in deep learning training processes, addressing a key challenge in efficient model development.

Findings

Effective visualization of memory usage during training

Identification of parameters affecting memory consumption

Enhanced understanding of memory bottlenecks in deep learning

Abstract

Software developed helps world a better place ranging from system software, open source, application software and so on. Software engineering does have neural network models applied to code suggestion, bug report summarizing and so on to demonstrate their effectiveness at a real SE task. Software and machine learning algorithms combine to make software give better solutions and understanding of environment. In software, there are both generalized applications which helps solve problems for entire world and also some specific applications which helps one particular community. To address the computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training time. Machine learning algorithms have a greater impact in the world but there is a considerable amount of memory utilization during the process. We propose a new tool for analysis of memory utilized for developing and training deep learning models. Our tool results in visual utilization of memory concurrently. Various parameters affecting the memory utilization are analysed while training. This tool helps in knowing better idea of processes or models which consumes more memory.