Microway Provides Vyasa Analytics With NVIDIA DGX-1, NumberSmasher GPU Server

By Bio-IT World Staff

August 26, 2019 | Microway announced it has provided an NVIDIA DGX-1 supercomputer and Microway NumberSmasher Tesla GPU Server to Vyasa Analytics. The new hardware enables Vyasa Analytics next phase of growth.

The NVIDIA DGX-1 Deep Learning Appliance includes NVIDIA's Deep Learning software stack and NGC containers. Immediately after installation, the system was ready to train models and scale Vyasa's software. The easy-to-use DIGITS deep learning training system and interface available on DGX-1 helps users manage training data, monitor performance, and design, compare, and select networks.

Microway's NumberSmasher Tesla GPU Servers integrate 1-10 NVIDIA Tesla V100 GPUs with flexible GPU density. These servers are fully configurable for any customized workload. The Vyasa Analytics deployment utilized this configurability to deploy early R&D environments and test new concepts-scaled up onto the DGX-1 when ready.

"These systems have enabled us to branch out into a number of R&D areas that were really critical for us to be able to innovate and build out new types of deep learning approaches," Christopher Bouton, founder and CEO of Vyasa Analytics, said in a press release. "As a company working in the deep learning space, we see Microway and NVIDIA as key partners in our ability to build innovative novel deep learning algorithms for a wide range of content types."

Vyasa Analytics provides a deep learning analytics platform for leading organizations in the life sciences, healthcare, business intelligence, and legal verticals. Vyasa's highly-scalable deep learning software, Cortex, operating on NVIDIA GPUs and Microway server hardware, applies deep learning-based analytics to enterprise data of a variety of types: text, image, chemical structure, and more. Use cases include analyzing multiple large-scale text sources and streams that include millions of documents in order to discover patterns, relationships, and trends for patent analysis, competitive intelligence or drug repurposing.

Cortex applies artificial intelligence (AI) narrowly, a strategy Bouton told Clinical Research News in December will allow the AI system to identify specific problems.

“[With narrow AI], each AI or deep learning algorithm is specific to a certain kind of data, trained to do a certain kind of thing, and the advantage is that it can provide an output or prediction without rule sets,” said Bouton. “What that means is that humans don’t have to preprogram these algorithms with what they’re looking for. Instead, you can train the algorithm and have the algorithm figure out what to look for.”