Keeping deep learning GPUs well fed using object storage
Or Ozeri, Effi Ofer, Ronen I. Kat · 2018
In recent years, machine learning and deep learning techniques such as deep neural networks and recurrent neural networks have found uses in diverse fields including computer vision, speech recognition, natural language processing, social network analysis, bioinformatics and medicine, where they have produced results comparable to and in some cases surpassing human experts. Machine learning requires large amount of data for training its models with much of this data residing in object storage, an inexpensive and scalable data store. Also, deep learning make use of state of the art processing capabilities from high-end GPUs and accelerators, such as Google Tensor Processing Units (TPUs), which enable parallel and efficient execution. The throughput that such GPUs can support is very high.