  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.
