Cray Turbocharges AI Model Development with New Algorithms and Frameworks
Urika Software Suite Features Unique Hyperparameter Optimization; Empowers Customers with Added AI Tools
Global supercomputer leader Cray Inc. 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.