1.2 is pretty ancient. Current, or even recent, versions of Julia have a fraction of the startup time (https://lwn.net/Articles/856819/). Package management has been refined further, as well.
We absolutely cannot upgrade Julia version right now, dozens of repos full of complicated scientific code. Management doesn’t care as far as it barely runs. I don’t think it’s fair to blame Julia for it but it just shows how much more it needs to go. That should be looked at as a positive thing.
I have one more complain to Julia community - please don’t be too defensive. Accept and embrace criticisms and turn that into a propellant for improving the language. I’ve seen a cult-like behavior in Julia community that is borderline toxic. Sorry, it’s the truth and needs to be said. Speed isn’t everything and people are magnetized by the benchmarks on the Julia website, especially non-software engineers.
I think all languages have this dynamic...I've seen it with python and R. To some extent it's fed by what we perceive as criticisms from people defending their favorite incumbent language with arguments that aren't at all informed- such as a focus on speed and how numba achieves parity there.
In the same vein, I and many Julia users are enthusiastic precisely because of thing other than speed, such as the type system, abstractions, differentiability and other things that make programming more joyful, fluid and productive.
Agree though, that we could always improve on acceptance of criticism.
Instead of Pip, virtualenv, conda, etc etc there's one package manager that resolves and installs native and binary dependencies, ensures reproducibility with human readable project files, is accessible from the REPL etc.
You can get an entire GPU based DL stack up and running on a windows machine in under 30 min, with a few keystrokes and no special containers or anything like that. Don't even have to install cuda toolkit. It's a dream, and I've heard the same from recent python converts
A lot of problems are fixable with time and money. Maybe the Series A will help!
But some problems might be related to Julia's design choices. One thing I really missed in Julia is Object Oriented Programming as a first class paradigm. (Yes I know you can cobble together an approximation with structs.)
OOP gets a lot of hate these days. Mostly deserved. But in some large complex projects it's absolutely the right abstraction. I've used OOP in several Python projects. Most of the big Python DS/ML packages use OOP.
Maybe you think PyTorch, SciKit, etc are all wrong, and would be better off with a more functional style. I know it's fashionable in some circles to make fun of the "model.fit(X,Y); model.predict(new_data)" style of programming but it works well for us busy professionals just trying to ship features.
I don't think Julia is wrong for enforcing a more functional style. It probably makes it easier for the compiler to generate great performance.
But Python has achieved its success because of its philosophy of being the "second best tool for every job" and that requires a more pragmatic, multiparadigm approach.
Julia is object oriented in a broad sense, it just uses multiple dispatch which is strictly more expressive than single dispatch, so doesn't make sense to have dot notation for calling methods because types don't own methods.
For giving up some facility in function discover, you get speed, composability, generic code...and a net gain in usability because you can have one array abstraction for GPUs, CPUs etc etc, which is just an instance of having common verbs across the ecosystem (enabled by MD). Instead of everyone having their own table type or stats package, you have Tables.jl or Statsbase.jl that packages can plug into and extend without the issues inherent in subclassing, monkeypatching etc.
This is a much better, more powerful and pleasant experience
Closing the gap in Method discovery will simply require a language feature with tooling integration, where you write the types and then tab to get the functions. There's already an open issue/PR for this
Consider if we want to run many different types of models. Logistic regression, gradient boosting, NNs, etc. We want the ability to easily plug in any type of model into our existing code base. That's why model.fit(X,Y) is attractive. I just need to change "model = LogisticRegressionModel" to "model = GradientBoostingModel" and the rest of the code should still Just Work. This is a big part of SciKit's appeal.
But all these different models have very different training loops. So with "fit!(model,X,Y)" I need to make sure I am calling the compatible "fit" function that corresponds to my model type.
You might now say "Ah! Multiple dispatch handles this for you. The 'fit' function can detect the type of its 'model' argument and dispatch execution to the right training loop sub function." And I suppose that's theoretically correct. But in practice I think it's worse.
It should be the responsibility of the developer of "model" to select the "fit" algorithm appropriate for "model." (They don't have to implement it, but they do have to import the right one.) The developer of "fit" should not be responsible for handling every possible "model" type. You could have the developer of "model" override / extend the definition of "fit" but that opens up its own can of worms.
So is it possible to do the same thing with "fit!(model,X,Y)"? Yes of course it is. It's possible to do anything with any turing complete language. The point is, which system provides the best developer ergonomics via the right abstractions? I would argue, in many cases, including this one, it's useful to be able to bundle functions and state, even if that is in theory "less flexible" than pure functions, because sometimes programming is easier with less flexibility.
>It should be the responsibility of the developer of "model" to select the "fit" algorithm appropriate for "model." (They don't have to implement it, but they do have to import the right one.) The developer of "fit" should not be responsible for handling every possible "model" type. You could have the developer of "model" override / extend the definition of "fit" but that opens up its own can of worms.
It's really the same thing as python, just better...I don't see the distinction you are drawing.
In python you have a base class with default behavior. You can subclass that and inherit or override.
Julia has abstract types with interfaces...instead of relying on implementation details like fields, you provide functions so that more types of models can work even if they don't have that one specific field. Otherwise everything is the same where it counts,- you can compose, inherit and override. Even better, you can work with multiple models and types of data, inheriting where you see fit.
I don't see any benefit to python's restrictions here, either in ease of use or in expressiveness.
For all intents and purposes it's a strict superset.
Even better, you can use macros and traits to group different type trees together.
https://www.stochasticlifestyle.com/type-dispatch-design-pos...
These seem to be in contradiction:
>It should be the responsibility of the developer of "model" to select the "fit" algorithm appropriate for "model.
>You could have the developer of "model" override / extend the definition of "fit" but that opens up its own can of worms.
It's the same in python, either you inherit Fit or you can override it. What's the difference with Julia?
Except in julia all types and functions have a multiple dispatch, parametric type and possible trait lattice of things you can override, customize and compose, so that even if the model author has to override fit, they can do it using small composable building blocks.
Each develop will develop their model in their own package. But they just define this fix function. E.g.
``` # in PkgA function fit!(model::ModelTypeA, X, y) # some code ... end ```
``` # in PkgB function fit!(model::ModelTypeB, X, y) # some code ... end ```
Each `fit!` function is distinct. It's the same as `model.fit` where the `model` is controlled by the individual developers.
Julia peeps would tell you that the multiple dispatch used by Julia is a generalization of OOP. And that they like multiple dispatch
as opposed to Python and JVM traces? LOL
Also, python has been around for alot longer. So perhaps that Python has "everything" is a feature of time and user base.
Maybe Julia will grow to have a large enough user-base to have those things.
Gosh, that's really old and it's not even an LTS version. I fear many of your woes stem from that. Julia 1.6 has huge improvements, and the community has rallied around VS Code now that Atom seems to be dying.
It really shouldn't be too bad to update.
Python having a broader ecosystem is a very good point, but I've found pycall to be very helpful
