I/O for Deep Learning at Scale

<!--HTML-->Deep Learning is revolutionizing the fields of computer vision, speech recognition and control systems. In recent years, a number of scientific domains (climate, high-energy physics, nuclear physics, astronomy, cosmology, etc) have explored applications of Deep Learning to tackle a range of data analytics problems. As one attempts to scale Deep Learning to analyze massive scientific datasets on HPC systems, data management becomes a key bottleneck. This talk will explore leading scientific use cases of Deep Learning in climate, cosmology, and high-energy physics on NERSC and OLCF platforms; enumerate I/O challenges and speculate about potential solutions.