Infinite compute power for GPU accelerated Deep Learning
NVIDIA has been a pioneer in accelerating deep learning and has been developing deep learning software, libraries and tools for a number of years. Today's deep learning solutions rely almost exclusively on NVIDIA GPU-accelerated computing to train and speed up challenging applications such as image, handwriting, and voice identification.
This presentation will provide an overview about the latest hardware and software developments for deep learning at NVIDIA, focusing in particular on NVIDIA® DGX-1™, the world’s first purpose-built system for deep learning. The software stack includes major deep learning frameworks, the NVIDIA Deep Learning SDK, the DIGITS™ GPU training system, drivers, and CUDA® for rapidly designing deep neural networks (DNN). This powerful system also provides access to cloud management services for container creation and deployment, system updates, and an application repository.
Axel Koehler is Principal Solution Architect at NVIDIA. He has been with NVIDIA since January 2011. In his role Axel supports researchers, scientists, engineers and hardware and software partners in the implementation of GPU-based Machine Learning and HPC solutions. Prior to NVIDIA Axel worked at Sun Microsystems for 14 years in the global HPC team as lead architect and was responsible for the architecture and the design of large HPC cluster installations.