Resource-Efficient Distributed Deep Learning:...

Resource-Efficient Distributed Deep Learning: Optimizing Training for Scalability

Author Name : Praveen Kumar Thopalle

ABSTRACT As deep learning models become more powerful and complex, the demand for high-end computational resources grows, but not everyone has access to unlimited hardware. Limited access to GPUs, memory constraints, and slow communication between devices can lead to frustrating challengeslong training times, inconsistent results, and wasted resources. This paper explores practical strategies to optimize distributed deep learning training, helping overcome these limitations. By leveraging distributed training methods, optimizing GPU usage with tools like Horovod, and utilizing communication libraries such as NCCL, we demonstrate how these techniques can drastically improve performance, scalability, and consistency, even in resource-constrained environments. The goal is to show that deep learning models can be trained efficiently, even when resources are limited.