Microway Provides Vyasa Analytics NVIDIAR DGX-1T and NumberSmasher GPU Server

By Tiera Oliver

Associate Editor

Embedded Computing Design

December 23, 2019


AI analytics leader enhances scale, develops new capabilities with new deployment.

Microway, a provider of computational clusters, servers, and workstations for AI and HPC applications, announces it has provided an NVIDIA DGX-1 supercomputer and Microway NumberSmasher Tesla GPU Server to deep-learning leader Vyasa Analytics.

The NVIDIA DGX-1 Deep Learning Appliance delivers performance available when training neural networks and running production-scale classification workloads. The system leverages the power of eight built-in NVIDIA Tesla V100 GPUs with NVIDIA NVLink Technology and Tensor Cores to boost the speed of deep learning training. According to the company, NVIDIA DGX-1 performs 140X faster deep learning training when compared to a CPU-only server.

The system includes NVIDIA's Deep Learning software stack and NGC containers. According to the company, immediately after installation, the system was ready to train models and scale Vyasa's software. The 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 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.

Vyasa's 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.

For more information, please visit: https://www.microway.com/preconfiguredsystems/nvidia-dgx-1-deep-learning-system/

Tiera Oliver, Associate Editor for Embedded Computing Design, is responsible for web content edits, product news, and constructing stories. She also assists with newsletter updates as well as contributing and editing content for ECD podcasts and the ECD YouTube channel. Before working at ECD, Tiera graduated from Northern Arizona University where she received her B.S. in journalism and political science and worked as a news reporter for the university’s student led newspaper, The Lumberjack.

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