Convert Generalized Neural Network bundle (GNN) to native Tensorflow 2.0

This project was completed by a student accepted on to the 2023 HPCC Systems Intern Program.

Project Description

Neural Networks have become a key mechanism for the analysis of many types of data.  In particular they have been found to be very effective for the analysis of complex datasets such as images, video, and time-series, where classical methods have proven inadequate. The Generalized Neural Network Bundle (GNN) allows the ECL programmer to combine the parallel processing power of HPCC Systems with the powerful Neural Network capabilities of Keras and Tensorflow. The GNN bundle attaches each node in the HPCC Systems cluster to an independent Keras/Tensorflow environment and coordinates among those environments to provide a distributed environment that can parallelize all phases of Keras/Tensorflow usage. Most importantly, this coordination is transparent to the GNN user, who can program as if running on a single node.

Our GNN bundle was originally developed against the Tensorflow 1.5 interface.  We currently support Tensorflow 2.0 using a Tensorflow compatibility mode.  The candidate for this project will adapt GNN to directly use the Tensorflow 2.0 interface in order to maximize  performance, round out any functionality gaps, and fully support all Tensorflow pre-trained models.  In particular, we want to ensure that we provide effective support for GPUs, and full support for recent neural network models.

The successful candidate will have a strong knowledge of neural networks, and experience with Tensorflow.  This project includes coding, testing, and documenting the results.

If you are interested in this project, please contact the mentor shown below.