The Rapid Python Deep Learning Infrastructure (RaPyDLI) project is based on the objective to combine high level Python, C/C++ and Java environments with carefully designed libraries supporting GPU accelerators and MIC coprocessors. Interactive analysis and visualization will be supported together with scaling from the current terabyte size to Petabyte datasets to enable substantial progress in the complexity and capability of the DL applications. A broad range of storage models will be supported including network file systems, databases and HDFS. The partnership of Indiana University, University of Tennessee-Knoxville, and Stanford University combines leaders in parallel computing algorithms and run times, Big Data, clouds, and deep learning.

From May 31 to June 2, 2015, Prof. Geoffrey Fox attended the NSF XPS Workshop in Arlington, VA. On June 2 he gave a presentation that included a poster detailing individual components of the RaPyDLI project and their current state. Click on the image to the right to view the poster displayed during the workshop.

HIGHLIGHTS

The above image links to an article discussing the RaPyDLI project.

• Indiana University is pleased to host a new Intel Parallel Computing Center as part of their longstanding tradition blending industry and academia. Prof. Judy Qiu heads this new center which also includes Prof. Steven Gottlieb of the IU Physics Dept. In addition a $320,000 award from Intel will be used in funding research contributing to the RaPyDLI project.
• A presentation that documented RaPyDLI's achievements was given at an NSF XPS workshop on Jun 2, 2015.
• The official NSF award page for the RaPyDLI project can be seen here.
• Co-PI Prof. Judy Qiu has received a 2015 Outstanding Junior Faculty award from Indiana University. To learn more, click here.
• Indiana University now offers a Data Science Master's degree, more on which can be found here.