Why is Julia’s Flux Catching Fire for ML?

If you’re doing machine learning today, you’ve probably noticed that Julia is climbing language popularity charts like a rocket. Language adoption is sometimes tricky, but in this case, I believe the answer is clear. In the next series of articles, I am going to show how Julia ties together a hungry community and a few critical features that feed into one critical library: Zygote.

In this article, we’ll begin to set the tone. We’ll look at the forces at play that led to the seed community that gave Julia it’s initial foothold. Next time, we’ll look at the rocket fuel that is accelerating Julia’s growth.

This story starts with a hungry community watching a pendulum swinging back and forth across the chasm of performance and productivity.

Julia Taps a Hungry Community

The programming language graveyard is full of cool languages that don’t solve compelling problems. Technology might ensure success, but without community, technology alone is not enough. For Julia, the initial vision was the same as the current one: support science with a language that’s both fast and productive. The founding Julia team has mentioned a third hidden goal time and time again: transparency. In other words, most of Julia is written in Julia, so it’s easy to understand. You can think of these boxes as language features. They have also traditionally been compromises, so that optimizing one impacts the others: