SINGA-Easy：一个易于使用的MultiModal分析框架
SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis
摘要：
深度学习在图像分类、自然语言处理和多模态数据分析等广泛的多媒体应用中取得了巨大成功。近年来，许多深度学习框架的发展为用户设计模型、进行训练和部署推理提供了高级编程接口。然而，它仍然具有挑战性，建立一个高效的端到端的多媒体应用程序与大多数现有的框架。具体而言，在可用性方面，要求非专家实现深度学习模型，为整个机器学习管道获得正确的设置，管理模型和数据集，并同时利用外部数据源。此外，在适应性方面，由于实际服务工作负载不断波动，因此非常需要弹性计算解决方案，并且缩放硬件资源以处理波动的工作负载通常是不可行的。为了应对这些挑战，我们引入了SINGA-Easy，这是一种新的深度学习框架，它在训练阶段提供分布式超参数调整，在推理阶段提供动态计算成本控制，并通过模型解释促进与多媒体内容的直观用户交互。我们对多模态数据分析应用程序的训练和部署的实验表明，该框架是可用的，并适应动态推理负载。我们在Apache SINGA上实现了SINGA-Easy，并通过整个机器学习生存周期展示了我们的系统。
英文摘要：
Deep learning has achieved great success in a wide spectrum of multimedia applications such as image classification, natural language processing and multimodal data analysis. Recent years have seen the development of many deep learning frameworks that provide a high-level programming interface for users to design models, conduct training and deploy inference. However, it remains challenging to build an efficient end-to-end multimedia application with most existing frameworks. Specifically, in terms of usability, it is demanding for non-experts to implement deep learning models, obtain the right settings for the entire machine learning pipeline, manage models and datasets, and exploit external data sources all together. Further, in terms of adaptability, elastic computation solutions are much needed as the actual serving workload fluctuates constantly, and scaling the hardware resources to handle the fluctuating workload is typically infeasible. To address these challenges, we introduce SINGA-Easy, a new deep learning framework that provides distributed hyper-parameter tuning at the training stage, dynamic computational cost control at the inference stage, and intuitive user interactions with multimedia contents facilitated by model explanation. Our experiments on the training and deployment of multi-modality data analysis applications show that the framework is both usable and adaptable to dynamic inference loads. We implement SINGA-Easy on top of Apache SINGA and demonstrate our system with the entire machine learning life cycle.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
