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Julia Computing raises $24M Series A

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Re: Julia Computing raises $24M Series A

#151

Post by jakobnissen »

f6v wrote: post #111
mr_overalls wrote: post #60

Julia seems like such a superior language compared to R. What would be required for it to supplant R for statistical work (or some subset of it)?

The majority of researchers don’t care about the language superiority. They’re concerned with different issues and software tends to suffer from “publish and forget” attitude. Convenience matters, and R ecosystem is quite good.

As a scientist programmer, that has not been my experience. In my experience, science programming is characterized by having to implement a lot of stuff from the ground up yourself, because unlike web dev or containerization, it's unlikely there is any existing library for metagenomic analysis of modified RNA.

And here Julia is a complete Godsend, since it makes it a joy to implement things from the bottom up.

Sure, you also need a language that already has dataframe libraries, plotting, editor support et cetera, and Julia is lacking behind Python and R in these areas. But Julia's getting there, and at the end of the day, it's a relatively low number of packages that are must-haves.

Re: Julia Computing raises $24M Series A

#152

Post by leephillips »

jakobnissen wrote: post #151
f6v wrote: post #111

Earlier quoted context omitted.

The majority of researchers don’t care about the language superiority. They’re concerned with different issues and software tends to suffer from “publish and forget” attitude. Convenience matters, and R ecosystem is quite good.

As a scientist programmer, that has not been my experience. In my experience, science programming is characterized by having to implement a lot of stuff from the ground up yourself, because unlike web dev or containerization, it's unlikely there is any existing library for metagenomic analysis of modified RNA. And here Julia is a complete Godsend, since it makes it a joy to implement things from the bottom up. Sure,…

Exactly. Almost all of it is bespoke implementations, sometimes of an algorithm that has just been invented and not yet applied to a real problem.

Re: Julia Computing raises $24M Series A

#153

Post by enriquto »

oscardssmith wrote: post #12
djhaskin987 wrote: post #2

Can someone please explain to me, a mere mortal, what is the big deal with Julia. Why use it, when there are so many other good languages out there with more community/support? Honest question.

Short answer is that it is (imo) by far the best language for writing generic and fast math. Multiple dispatch allows you to write math using normal notation and not have to jump through hoops to do so.

can you show a simple example of that? I tend to see multiple dispatch as a mental burden, (e.g.: when I see a function call, where will it be dispatched? the answer dependa on the types that I'm juggling, that may not even be visible at that point...)

Re: Julia Computing raises $24M Series A

#154

Post by amkkma »

awaythrowact wrote: post #149
systemvoltage wrote: post #136

Earlier quoted context omitted.

There is too much to talk about and I’d want to give an objective impression with examples in a blog post, but one of the major grips I have is how little information Julia provides you with stack traces. Debugging production problems with absolutely zero clue of what/where the problem might be is one of the most frustrating aspects. I’ve spent so many hours debugging Julia using print statements. Debugger is janky,…

I second most of this. 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 a…

How is "model.fit(X,Y)" better than "fit!(model,X,Y)"?

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

Re: Julia Computing raises $24M Series A

#155

Post by chrispeel »

awaythrowact wrote: post #149
systemvoltage wrote: post #136

Earlier quoted context omitted.

There is too much to talk about and I’d want to give an objective impression with examples in a blog post, but one of the major grips I have is how little information Julia provides you with stack traces. Debugging production problems with absolutely zero clue of what/where the problem might be is one of the most frustrating aspects. I’ve spent so many hours debugging Julia using print statements. Debugger is janky,…

I second most of this. 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 a…

> One thing I really missed in Julia is Object Oriented Programming as a first class paradigm.

Julia peeps would tell you that the multiple dispatch used by Julia is a generalization of OOP. And that they like multiple dispatch

Re: Julia Computing raises $24M Series A

#156

Post by amkkma »

systemvoltage wrote: post #136
amkkma wrote: post #86

Earlier quoted context omitted.

> As soon as you take Julia out of notebooks and try to build moderately complex apps with it, you realize how much you miss Python Why's that? What features or lack thereof of Julia contribute to that experience?

There is too much to talk about and I’d want to give an objective impression with examples in a blog post, but one of the major grips I have is how little information Julia provides you with stack traces. Debugging production problems with absolutely zero clue of what/where the problem might be is one of the most frustrating aspects. I’ve spent so many hours debugging Julia using print statements. Debugger is janky,…

I really don't understand your point about package management.

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

Re: Julia Computing raises $24M Series A

#157

Post by systemvoltage »

leephillips wrote: post #147
systemvoltage wrote: post #136

Earlier quoted context omitted.

There is too much to talk about and I’d want to give an objective impression with examples in a blog post, but one of the major grips I have is how little information Julia provides you with stack traces. Debugging production problems with absolutely zero clue of what/where the problem might be is one of the most frustrating aspects. I’ve spent so many hours debugging Julia using print statements. Debugger is janky,…

This is the first time I’ve heard somebody say that Julia’s package management is worse than Python’s! For most people who have spent years grappling with the succession of unofficial attempts to supply Python with something like package management, including virtual environments, etc., and the resulting dependency hell, Julia’s integrated and sophisticated package management (even with its own special REPL mode) is…

I’m a tortured soul, my opinions might be biased and I hope things improve with Julia.

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.

Re: Julia Computing raises $24M Series A

#158

Post by mint2 »

spywaregorilla wrote: post #123

I played with Julia a bit in grad school. Although I didn't end up using it much after that I thought it was a lovely language. Forget python, I hope Julia manages to kill off Matlab and its weird stranglehold on various pockets of academia. Congrats to the team here.

Matlab, the SAS of academia and certain engineering fields.

Re: Julia Computing raises $24M Series A

#159

Post by amkkma »

systemvoltage wrote: post #136
amkkma wrote: post #86

Earlier quoted context omitted.

> As soon as you take Julia out of notebooks and try to build moderately complex apps with it, you realize how much you miss Python Why's that? What features or lack thereof of Julia contribute to that experience?

There is too much to talk about and I’d want to give an objective impression with examples in a blog post, but one of the major grips I have is how little information Julia provides you with stack traces. Debugging production problems with absolutely zero clue of what/where the problem might be is one of the most frustrating aspects. I’ve spent so many hours debugging Julia using print statements. Debugger is janky,…

Thanks for sharing! I think much of it has to do with the version you're stuck on. (ie startup and compile times are way way better with 1.6)

Python having a broader ecosystem is a very good point, but I've found pycall to be very helpful

Re: Julia Computing raises $24M Series A

#160

Post by StefanKarpinski »

tylermw wrote: post #117
amkkma wrote: post #104

Earlier quoted context omitted.

In addition to the comment about df.jl catching up, they aren't comparable at all. Julia's DF library is generic and allows user defined ops and types. You can put in GPU vectors, distributed vectors, custom number types etc. Julia optimizes all this stuff. data.frame is just a giant chunk of c (c++) code that one must interact with in very specific ways

> Julia's DF library is generic and allows user defined ops and types. You can put in GPU vectors, distributed vectors, custom number types etc. Julia optimizes all this stuff. These features aren't of interest to practicing statisticians, which the parent comment was talking about. > data.frame is just a giant chunk of c (c++) code that one must interact with in very specific ways I don't understand this criticism:…

Many practicing statisticians do actually care about easily using GPUs and doing distributed computations on distributed data sets with the same code they use for a local data set, which is what those Julia capabilities give you.
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