The State of Julia for Scientific Machine Learning

TL;DR

This paper reviews Julia's current ecosystem and features, evaluating its potential as a Python alternative for scientific machine learning, highlighting strengths, challenges, and community needs for wider adoption.

Contribution

It provides a comprehensive assessment of Julia's ecosystem and discusses the key challenges hindering its adoption in scientific machine learning.

Findings

Julia's ecosystem has grown significantly since 2012.

Julia offers performance and ergonomic improvements over Python.

Community efforts are needed to address language-level issues.

Abstract

Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's inception in 2012 and declaration of language goals in 2017, its ecosystem and language-level features have grown tremendously. In this paper, we take a modern look at Julia's features and ecosystem, assess the current state of the language, and discuss its viability and pitfalls as a replacement for Python as the de-facto scientific machine learning language. We call for the community to address Julia's language-level issues that are preventing further adoption.