Deep Learning framework in Python under RaPyDLI
NSF Grant Number
NSF Grant URL
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. We aim to deploy Caffe and Tensorflow, state of the art deep learning framework on FutureSystems and look for any further algorithmic improvement in the framework
Use of FutureSystems
We will use CUDA(GPU Software) to test and compare performances of different deep learning algorithms from the framework such as Caffe or TensorFlow or our own implementation.
Scale of Use
We will need to run GPU compatible platform to measure several performance gain for several deep learning algorithms