The State of Julia for Scientific Machine Learning
This is an incremental analysis for researchers and practitioners in scientific computing, focusing on ecosystem evaluation rather than new methods.
The paper assesses Julia's current features and ecosystem to evaluate its viability as a replacement for Python in scientific machine learning, highlighting language-level issues that hinder adoption.
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.