According to a recent press release, “Global supercomputer leader Cray Inc. today announced enhancements to its Urika® AI and Analytics software suites, adding tools that enable data scientists to train artificial intelligence (AI) models more accurately and in less time. New features in the Cray® Urika®-CS and Urika®-XC AI and Analytics suites include Cray-developed libraries to intelligently optimize machine learning model settings as well as additional AI tools and frameworks commonly used by data scientists. One of the most challenging tasks for a data scientist is optimizing their choice of model hyperparameters, the knobs they can tune to pick the best model within a model class. This optimization can be resource- and labor-intensive, and often relies on time-consuming hand-crafted or brute-force approaches. Cray is adding hyperparameter optimization (HPO) algorithms, capable of running in a distributed fashion, to help data scientists find the optimal model for production AI deployments.”

The release continues, “The new Urika suites are augmented with four HPO strategies – two commonly used strategies and two strategies developed by Cray to take advantage of the parallelism available in a distributed system. Taken together, these strategies simplify the task of finding and tuning the optimal model for a given application. The four strategies are: (1) Genetics-based: to find more optimal model architectures. (2) Population-based: to find the best way to train your model faster. (3) Random: a baseline algorithm to guide behavior. (4) Grid Search: a traditional approach, guided by performance metrics.”

Read more at Globe Newswire.

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