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Accelerating Data Science Workflows with RAPIDS

Data science workflow has traditionally been slow and cumbersome when it came to loading, filtering and manipulating data, as well as ML training itself. These processes were constrained to slow, CPU-based compute, and resulted in lengthy cycle times impacting data science productivity. RAPIDS delivers GPU accelerated machine learning and data analytics libraries, deployed on NVIDIA GPU platforms, for maximized data science productivity, performance and insights. Bring the power of GPU acceleration to your research and decrease your time to new discoveries with RAPIDS.

By watching this webinar you will learn:
  • How RAPIDS accelerates your Python data science toolchain with minimal code changes and no new tools to learn.
  • How RAPIDS can accelerate model training and time to deployment.
  • The wealth of accelerated apps available to maximize datacenter throughput, utilization, and efficiency.

ON DEMAND WEBINAR REGISTRATION

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DGX Station Datasheet

Get a quick low-down and technical specs for the DGX Station.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.
DGX Station Whitepaper

Dive deeper into the DGX Station and learn more about the architecture, NVLink, frameworks, tools and more.

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Speaker

Zahra Ronaghi
Senior Solutions Architect, NVIDIA

Zahra Ronaghi is a Senior Solution Architect at NVIDIA working with Higher Education and Research institutions in High Performance Computing and Deep Learning. Prior to NVIDIA, Zahra was a postdoctoral fellow at Lawrence Berkeley National Laboratory (NERSC), where she worked on performance optimization and portability on DOE supercomputers. She graduated from Clemson University with a Ph.D. in biomedical engineering and received her B.S. and M.S. degrees in electrical engineering.

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Date & Time: Wednesday, April 22, 2018