The Julia approach gives quicker development. Some of these ML libraries such as Flux are so small that almost anyone can read the code and learn how it works and make modifications. It is not for anyone to jump into TensorFlow.
The irony here is that tiny Julia libraries give the same power as much larger Python libraries which require highly specialized and trained developers to evolve and maintain.
With Julia it is much easier to grow the eco system because you can make lots of relatively small packages which can then be combined in almost any way.
The challenge for the Julia community today is really to make people new to the environment aware of this.
I have encountered people who thought Flux couldn't do anything because it was so small. He was so used to the huge monolithic libraries in Python, that it did not occur to him that adding something like an new activation function is literally one line of code. He is used to that requiring adding various C++ nodes. Wrap those up and God knows what more steps you need when you extend something like TensorFlow.
I don't think most people realize how powerful Julia is. They are not used to being able to combine libraries the way you can do in Julia. A big part of the effort for the Julia community will simply be to write more tutorial and introduction which better introduce beginners to these kinds of capabilities. Once you know Julia it is pretty obvious.
But grab people's attention and make them realize Julia could be a solution to their problem, I think one needs a lot of sort of shallow and quick intros to these kinds of things.
> The challenge for the Julia community today is really to make people new to the environment aware of this.
^This. As we're seeing here in the discussion thread Python folks don't really realize what they're missing when we're talking about composability of of libraries in Julia - it's kind of difficult to explain the impact on productivity without people actually trying it out. I think Julia and Flux are getting to the point where they're quite useable and comparable with PyTorch. Also some of the Neural ODE stuff and differentiable programming in SciML seems just a lot easier to implement than it would be in Python.
From the point of view of a DL researcher, you’re able to easily implement your own custom layers. From the point of view of a framework developer, you get much faster development time. From the point of a more normal user, you hopefully get more features faster because the jobs of the framework developer and DL researcher have been made easier.
