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

December 23, 2019 Tiera Oliver

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/

About the Author

Tiera Oliver, edtorial intern for Embedded Computing Design, is responsible for web content edits as well as newsletter updates. She also assists in news content as far as constructing and editing stories. Before interning for ECD, Tiera had recently graduated from Northern Arizona University where she received her B.A. in journalism and political science and worked as a news reporter for the university's student led newspaper, The Lumberjack.

Follow on Twitter Follow on Linkedin Visit Website More Content by Tiera Oliver
Previous Article
Expert Predictions for 2020 Part 4: AI and Machine Learning
Expert Predictions for 2020 Part 4: AI and Machine Learning

In part four of our five-part series, experts from Western Digital and MathWorks share in detail their thou...

Next Article
Aetina Jetson TX2 4GB Edge AI Computing Platform Ready for Wider Access of AIoT
Aetina Jetson TX2 4GB Edge AI Computing Platform Ready for Wider Access of AIoT

AI Computing Deployment with Double the Price-Performance-Ratio of TX1