The new features include an augmented Deep Learning Plugin that provides best-in-class deep neural network performance training and broadened support for deep learning frameworks. In performance studies, the plugin showed training time reductions up to 23% over open source alternatives for a single node, dense GPU configuration. Both the reference configuration and plugin are designed for IT and AI teams implementing complex infrastructure to support Geospatial AI workloads. Cray also has delivered and installed a Cray CS Series system at the U.S. Geological Survey agency to support AI initiatives in geospatial analysis and the agencys mission to provide reliable information for understanding the Earth.
Geospatial AI is the marriage of geospatial data and artificial intelligence. It promises to be one of the most important uses of AI across a range of industries such as oil and gas companies, state and local governments, property and casualty insurance businesses, weather forecasting centers, and beyond. Data scientists are exploring the use of AI, deep learning and machine learning to deliver new applications and insights based on geospatial data. For example:
As Geospatial AI becomes core to organisational missions, the time to develop and refine neural network models at optimal accuracy becomes a challenging factor to innovation. To shorten the time data scientists spend developing Geospatial AI applications, Cray is releasing updates to the Urika-CS and Urika-XC AI and Analytics software suites. The augmented Cray Programming Environment (PE) Deep Learning Plugin will significantly reduce training times for complex neural network models. Internal performance studies, using the widely-available ResNet-152 and Inception-V4 neural network models, have shown significant training time improvements. Coupled with Cray's hyperparameter optimization capabilities, the Cray Urika AI and Analytics suites dramatically improve data scientist productivity and accelerate the development of advanced Geospatial AI applications.
The availability of new sources of geospatial data is driving the adoption of AI. Implementing a Geospatial AI workflow requires a balanced system that is able to handle the demands of data preparation and model development. Cray is introducing a new Geospatial AI Reference Configuration comprised of CS-Storm 500NX GPU accelerated nodes and CS500 CPU nodes that will be able to handle the entire Geospatial AI workflow.
"Geospatial AI presents both data and compute challenges for data science and IT teams tasked with developing new applications. Our forte has long been understanding performance issues and improving performance with supercomputing technologies", stated Per Nyberg, vice president market development, AI at Cray. "Complete systems optimized for the geospatial workflow and enhanced with high-performance deep learning eliminate boundaries faced by geospatial teams exploring and implementing advanced AI applications."
The US Geological Survey (USGS), the science arm of the U.S. Department of the Interior, has selected a Cray CS Series system to further the use of AI in natural sciences. USGS is active in promoting the use of machine and deep learning in areas ranging from earth observation, numerical weather prediction, hydrology, solid earth geoscience and land imaging.
The updated versions of the Urika-CS AI and Analytics software suites and the Geospatial Reference Configuration are expected to be available within 30 days.
To learn more and to see a live demo of Cray geospatial capabilities, you can stop by the Cray booth E-921 at ISC19.