Accurately predicting the training time of deep learning (DL) workloads is critical for
optimizing the utilization of data centers and allocating the required cluster resources for …

With the rapidly increasing size and complexity of DNNs, equally sophisticated methods are
needed to train them efficiently, including distributed training and various model/hybrid …

The prediction of the resource consumption for the distributed training of deep learning
models is of paramount importance, as it can inform a priori users how long their training …

Big deep neural network (DNN) models trained on large amounts of data have recently
achieved the best accuracy on hard tasks, such as image and speech recognition. Training …

Deep neural networks (DNNs) have recently yielded strong results on a range of
applications. Training these DNNs using a cluster of commodity machines is a promising …

Deep Learning applications are pervasive today, and efficient strategies are designed to
reduce the computational time and resource demand of the training process. The Distributed …

It becomes an increasingly popular trend for deep neural networks with large-scale datasets
to be trained in a distributed manner in the cloud. However, widely known as resource …

In the last decade, Distributed deep learning has been widely used and introduced in
research for highly computational tasks where time is very critical due to its capability to train …

This paper studies the problem of predicting the deep learning (DL) jobs' training time which
is the fundamental guide for training resources allocating and job scheduling. The existing …

Cloud computing provides simple on-demand access to a centralized shared pool of
computing resources. Performance and efficient utilization of cloud computing resources …