UVA Research Computing

Research Computing

Creating innovative solutions for researchers

/tag/machine-learning

  • Machine Learning on Rivanna

    Overview Many machine learning packages can utilize general purpose graphics processing units (GPGPUs). If supported by the respective machine learning framework or application, code execution can be manyfold, often orders of magnitude, faster on GPU nodes compared to nodes without GPU devices. Rivanna has several nodes that are equipped with GPU devices. These nodes are available in the GPU partition. Access to a GPU node and its GPU device(s) requires specific SLURM directives or command line options as described in the Jobs using a GPU Node section. Applications Several machine learning software packages are installed on Rivanna. The most commonly used ones are:
  • Center for Diabetes Technology PriMed

    In their research around constant glucose monitoring and the automated maintenance of insulin for patients, the CDT is exploring data drawn from external data sources such as DexCom and FitBit. RC has assisted the CDT by designing a secure computing footprint in Amazon Web Services to pull in these data, parse and process them, in order to perform deeper analytics through machine learning. In January 2018, CDT sponsored a ski camp at Wintergreen Resort for a group of youth diagnosed with Type I diabetes with the goal of importing glucose, insulin, and exercise metrics at the end of each day through remote web APIs.
  • Predicting ER Triage Levels with Machine Learning

    Before patients are admitted to the emergency room, they are assigned a triage level based on the severity of their health problems. This is accomplished using the Emergency Severity Index (ESI), an emergency department triage algorithm that classifies patient cases into five different levels of urgency. Researchers are interested in using machine learning to develop a model to predict patient triage level. This model would not only analyze the typical vital signs that are used in the ESI, but also demographic data and patients’ history of health. Demographic and health data have been collected. RC is helping to prepare and normalize the data for use in a machine learning model.