SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis

TL;DR

SINGA-Easy is a user-friendly, adaptable deep learning framework designed for multi-modal data analysis, offering distributed hyper-parameter tuning, dynamic inference cost control, and intuitive multimedia interactions.

Contribution

It introduces a novel framework that enhances usability and adaptability for multimedia applications through distributed tuning, cost control, and model explanation features.

Findings

Framework is usable for multimedia applications.

Supports dynamic workload adaptation.

Effective in training and deployment of multi-modal data analysis.

Abstract

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.