Cray Turbocharges AI Model Development With New Algorithms and Frameworks

Urika Software Suite Features Unique Hyperparameter Optimization; Empowers Customers with Added AI Tools

SEATTLE, Nov. 12, 2018 (GLOBE NEWSWIRE) -- Global supercomputer leader Cray Inc. (Nasdaq:CRAY) 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.

More Accurate Training of Models with Intelligent Hyperparameter Optimization

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.

“Developing AI models can be a complex, time-consuming process. By offering intelligent hyperparameter optimization support within our Urika-CS and Urika-XC suites, we’re giving data scientists pre-set algorithms to quickly identify the most favorable machine learning model designs,” said Per Nyberg, vice president market development, AI and cloud at Cray. “Offering data scientists this support has several benefits, including increased productivity and workflow efficiencies.”

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:

• Genetics-based: to find more optimal model architectures
• Population-based: to find the best way to train your model faster
• Random: a baseline algorithm to guide behavior
• Grid Search: a traditional approach, guided by performance metrics

Newly Added Deep Learning Frameworks Give Researchers and Data Scientists A Choice

Cray has added four popular analytics and deep learning frameworks to its Urika AI and Analytics suites: PyTorch, Keras and Horovod for model development and training, as well as the highly-scalable Programming Big Data in R (pbdR) package. The upgraded software suites provide researchers and data science teams the right tools and more choice for how they perform their analytics, machine learning and deep learning workflows.

The new versions of the Urika-CS AI and Analytics software suite and Urika-XC software suite are expected to be available within 90 days.

To learn more and to see a live demo of these new capabilities, stop by our booth #2413 at .

About Cray Inc.

Cray Inc. (Nasdaq:CRAY) combines computation and creativity so visionaries can keep asking questions that challenge the limits of possibility. Drawing on more than 45 years of experience, Cray develops the world’s most advanced supercomputers, pushing the boundaries of performance, efficiency and scalability. Cray continues to innovate today at the convergence of data and discovery, offering a comprehensive portfolio of supercomputers, high-performance storage, data analytics and artificial intelligence solutions.  Go to for more information.

Safe Harbor Statement

This press release contains forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934 and Section 27A of the Securities Act of 1933, including, but not limited to, statements related to the availability and performance of the enhancements to its Urika AI and Analytics software suites and the features and functionality of the Urika AI and Analytics software suites. These statements involve current expectations, forecasts of future events and other statements that are not historical facts. Inaccurate assumptions and known and unknown risks and uncertainties can affect the accuracy of forward-looking statements and cause actual results to differ materially from those anticipated by these forward-looking statements. Factors that could affect actual future events or results include, but are not limited to, the risk that Cray is not able to successfully complete its planned product development efforts in a timely fashion or at all, the risk that the enhancements to its Urika AI and Analytics software suites are not generally available when expected or at all, the risk that its Urika AI and Analytics software suites do not have the features and functionality expected or do not perform as expected and such other risks as identified in the Company’s quarterly report on Form 10-Q for the quarter ended September 30, 2018, and from time to time in other reports filed by Cray with the U.S. Securities and Exchange Commission. You should not rely unduly on these forward-looking statements, which apply only as of the date of this release. Cray undertakes no duty to publicly announce or report revisions to these statements as new information becomes available that may change the Company’s expectations.

CRAY and Urika are registered trademarks of Cray Inc. in the United States and other countries. Other product and service names mentioned herein are the trademarks of their respective owners.