Issues in the Reproducibility of Deep Learning Results

Abstract

The Neuronix high-performance computing cluster allows us to conduct extensive machine learning experiments on big data [1] . This heterogeneous cluster uses innovative scheduling technology, Slurm [2] , that manages a network of CPUs and graphics processing units (GPUs). The GPU farm consists of a variety of processors ranging from low-end consumer grade devices such as the Nvidia GTX 970 to higher-end devices such as the GeForce RTX 2080. These GPUs are essential to our research since they allow extremely compute-intensive deep learning tasks to be executed on massive data resources such as the TUH EEG Corpus [2] . We use TensorFlow [3] as the core machine learning library for our deep learning systems, and routinely employ multiple GPUs to accelerate the training process.