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8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK
Grigori Fursin

Grigori Fursin
CTO, dividiti

Website

Grigori Fursin is the CTO of dividiti and a founding member of the ACM taskforce on reproducibility of the ML, AI, and systems research. Previously, he was a tenured scientist at Inria and the head of the Optimization Group at the Intel Exascale Lab. Grigori is the architect of the Collective Knowledge technology used by a growing number of universities and Fortune 50 companies to enable automatic, collaborative, and reproducible development, optimization, and codesign of efficient software and hardware for emerging ML and AI workloads in terms of speed, accuracy, energy, size, and costs. He holds a PhD in computer science from the University of Edinburgh.

Sessions

13:45–14:25 Wednesday, 10 October 2018
Scaling deep learning on AWS using C5 instances with MXNet, TensorFlow, and BigDL: From the edge to the cloud
Location: Westminster Suite
Secondary topics:  Deep Learning tools, Edge computing and Hardware, Platforms and infrastructure
GAURAV KAUL (Amazon Web Services), Suneel Marthi (Amazon Web Services), Grigori Fursin (dividiti)
Gaurav Kaul, Grigori Fursin, and Suneel Marthi share trade-offs and design choices that are applicable to deep learning models when training in the cloud, specifically focusing on convergence and numerical stability, which are very important for autonomous driving and medical imaging. They then demonstrate how to optimize cost, performance, and convergence using CPU spot instances in AWS. Read more.