Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing. Read this technical white paper to learn reasons for and benefits of an end-to-end training system. It also shows performance benchmarks based on a system that combines the NVIDIA® DGX-1™, a multi-GPU server purpose-built for deep learning applications and FlashBlade, a scale-out, high performance, dynamic data hub for the entire AI data pipeline.
Keeping ahead of the curve is a must when it comes to fashion and luxury. But to create cutting-edge designs that set trends, designers need state-of-the art tools and technology. The FashionLab provides high-end designers with innovative tools to create 3D designs of everything from dresses to jewelry.
A new 14th generation Dell EMC PowerEdge R740xd server equipped with NVIDIA Tesla M10 GPUs and powered by Intel Xeon Scalable processors delivered high-density graphics acceleration in a virtual enterprise environment
As more users move to modern, graphics-intensive Microsoft® Windows® 10 environments, companies using a virtual desktop infrastructure (VDI) need increasingly robust server resources
to support them. Your company should strive to get the most from each server and maximize datacenter space while providing an excellent end-user experience.
Published By: Onapsis
Published Date: Oct 21, 2014
NVIDIA is a US-headquartered global technology company best known as a manufacturer of graphics and is now able to effectively manage threats to its SAP systems, perform pre and post audit scans of its whole application environment, and conduct basic scans on a regular basis with continuous monitoring and detection of vulnerabilities.
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Download the new white paper from NVIDIA that addresses the challenges described in PLASTER, which is important in any deep learning solution, and it is especially useful for developing and delivering the inference engines underpinning AI-based services.