论文标题
深速优化库的基准评估
Benchmark Assessment for DeepSpeed Optimization Library
论文作者
论文摘要
深度学习（DL）模型由于其性能和处理大型数据集的能力而在产生高准确性和性能指标的同时，广泛用于机器学习。此类数据集的大小以及DL模型的复杂性导致此类模型很复杂，消耗了大量资源和训练时间。引入了许多最近的图书馆和应用程序来处理DL的复杂性和效率问题。在本文中，我们通过分类任务评估了一个示例，即Microsoft DeepSpeed库。 DeepSpeed公共消息来源报告了LENET架构上的分类绩效指标。我们通过评估图书馆在几个现代神经网络架构上进行评估，包括卷积神经网络（CNN）和Vision Transformer（VIT）。结果表明，在其中一些情况下，深速可以改善，但对其他情况没有或负面影响。
Deep Learning (DL) models are widely used in machine learning due to their performance and ability to deal with large datasets while producing high accuracy and performance metrics. The size of such datasets and the complexity of DL models cause such models to be complex, consuming large amount of resources and time to train. Many recent libraries and applications are introduced to deal with DL complexity and efficiency issues. In this paper, we evaluated one example, Microsoft DeepSpeed library through classification tasks. DeepSpeed public sources reported classification performance metrics on the LeNet architecture. We extended this through evaluating the library on several modern neural network architectures, including convolutional neural networks (CNNs) and Vision Transformer (ViT). Results indicated that DeepSpeed, while can make improvements in some of those cases, it has no or negative impact on others.
