Issues in the Reproducibility of Deep Learning Results
Issues in the Reproducibility of Deep Learning Results
复制标题
DOI：
10.1109/spmb47826.2019.9037840
复制
发表时间：
2019-12
期刊：
影响因子：
--
通讯作者：
S. Jean-Paul;T. Elseify;I. Obeid;Joseph Picone
中科院分区：
文献类型：
--
作者：
S. Jean-Paul;T. Elseify;I. Obeid;Joseph Picone
Neuronix高性能计算集群允许我们对大数据进行广泛的机器学习实验[1]。这个异类集群使用创新的调度技术SLurm[2]来管理CPU和图形处理单元(GPU)的网络。图形处理器群由各种处理器组成，从低端消费级设备(如NVIDIA GTX 970)到高端设备(如GeForce RTX 2080)。这些图形处理器对我们的研究至关重要，因为它们允许在海量数据资源上执行极其计算密集型的深度学习任务，例如TUH EEG语料库[2]。我们使用TensorFlow[3]作为我们深度学习系统的核心机器学习库，并经常使用多个GPU来加速训练过程。
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.