Ludwig: a type-based declarative deep learning toolbox

TL;DR

Ludwig is a user-friendly, flexible deep learning toolbox that simplifies model building through data types and declarative configurations, promoting broader adoption of deep learning.

Contribution

It introduces a novel data type abstraction and declarative configuration system, along with a modular Encoder-Combiner-Decoder architecture, enhancing accessibility and extensibility.

Findings

Enables non-programmers to train deep learning models effectively.

Facilitates code reuse and extension through data type abstraction.

Supports a wide range of machine learning tasks with a unified architecture.

Abstract

In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code. Ludwig implements a novel approach to deep learning model building based on two main abstractions: data types and declarative configuration files. The data type abstraction allows for easier code and sub-model reuse, and the standardized interfaces imposed by this abstraction allow for encapsulation and make the code easy to extend. Declarative model definition configuration files enable inexperienced users to obtain effective models and increase the productivity of expert users. Alongside these two innovations, Ludwig introduces a general modularized deep learning architecture called Encoder-Combiner-Decoder that can be instantiated to perform a vast amount of machine learning tasks. These innovations make it possible for engineers, scientists from other fields and, in general, a much broader audience to adopt deep learning models for their tasks, concretely helping in its democratization.