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fork of Zach's original code to use globus-url-copy

The project aims to develop deterministic and stochastic accelerations to speed-up and better scale the training of deep learning models on large-scale computational environments.

Python code for converting between point clouds and rasterized data representation.

Tutorials and scripts to use Summit in order to scale up your Python deep learning projects. It covers basics of data parallelism and goes over what to add in your code to parallelize your training process.

This repository includes public input and output files for TRITON/NEWT, TRITON/KENO and Polaris sequences used in the assessment

Development of reduced order models for Computational Neuroscience models