Distributed deep learning has emerged as an essential approach for training large-scale deep neural networks by utilising multiple computational nodes. This methodology partitions the workload either ...

We called it Machine Learning October Fest. Last week saw the nearly synchronized breakout of a number of news centered around machine learning (ML): The release of PyTorch 1.0 beta from Facebook, ...

Poor utilization is not the single domain of on-prem datacenters. Despite packing instances full of users, the largest cloud providers have similar problems. However, just as the world learned by ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Cory Benfield discusses the evolution of ...

FREMONT, Calif. -- Your development staff just put the finishing touches on a brilliant new Web-based application. Tens of thousands were invested in the project, and several politicians are already ...

Google today is announcing the release of version 0.8 of its TensorFlow open-source machine learning software. The release is significant because it supports the ability to train machine learning ...

Two months ago, Facebook’s AI Research Lab (FAIR) published some impressive training times for massively distributed visual recognition models. Today IBM is firing back with some numbers of its own.

Data science is hard work, not a magical incantation. Whether an AI model performs as advertised depends on how well it’s been trained, and there’s no “one size fits all” approach for training AI ...

HPE highlights recent research that explores the performance of GPUs in scale-out and scale-up scenarios for deep learning training. As companies begin to move deep learning projects from the ...