ODSC Speakers 67/72

ODSC Speakers 67/72


VISHNU, ABHINAV

Topic : USER-TRANSPARENT DEEP LEARNING ON EXTREME SCALE SYSTEMS

Abstract:  Deep Learning (DL) is ubiquitous. Yet leveraging distributed memory systems for DL ​​algorithms is incredibly hard. In this talk, we will present approaches to bridge this critical gap. We will start by scaling DL algorithms on large scale systems such as supercomputers, And cloud computing systems.

Specifically, we will:
1) present our TensorFlow and Keras runtime extensions which require negligible changes in user-code for scaling DL implementations,
2) present approaches to lending services practices and the results will be Pointers and discussion on the general availability of our research under the umbrella of Machine Learning Toolkit on Extreme Scale (MaTEx) available at http://github.com/matex-org/matex.

Bio:  Abhinav Vishnu is a chief scientist and team lead for scalable machine learning at Pacific Northwest National Laboratory. He focused on extreme scale Deep learning algorithms which are capable of execution on supercomputers and cloud computing systems. The specific objectives are designed user- Transparent layered tolerances
deep algorithms and applications of several techniques on several domains such as high energy physics, computational chemistry and general computer vision tasks