Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment

TL;DR

The paper introduces Model Asset Exchange (MAX), a system that simplifies access to diverse state-of-the-art deep learning models via a unified API, facilitating easier deployment for non-experts across various frameworks.

Contribution

MAX provides an open-source Python library with RESTful APIs that wrap and unify multiple DL models from different frameworks, enabling seamless inference without deep framework knowledge.

Findings

Wrapped and open-sourced over 30 DL models across fields

Developed web applications demonstrating MAX's utility

Simplified deployment process for non-expert developers

Abstract

A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL). In many different research fields, DL models have been evolving rapidly and become ubiquitous. Despite researchers' excitement, unfortunately, most software developers are not DL experts and oftentimes have a difficult time following the booming DL research outputs. As a result, it usually takes a significant amount of time for the latest superior DL models to prevail in industry. This issue is further exacerbated by the common use of sundry incompatible DL programming frameworks, such as Tensorflow, PyTorch, Theano, etc. To address this issue, we propose a system, called Model Asset Exchange (MAX), that avails developers of easy access to state-of-the-art DL models. Regardless of the underlying DL programming frameworks, it provides an open source Python library (called the MAX framework) that wraps DL models and unifies programming interfaces with our standardized RESTful APIs. These RESTful APIs enable developers to exploit the wrapped DL models for inference tasks without the need to fully understand different DL programming frameworks. Using MAX, we have wrapped and open-sourced more than 30 state-of-the-art DL models from various research fields, including computer vision, natural language processing and signal processing, etc. In the end, we selectively demonstrate two web applications that are built on top of MAX, as well as the process of adding a DL model to MAX.