I would really love to use Julia, and for my team to as well. But we are too locked into R to even begin. If I were these folks, I would focus on the flow, not stock, of data analysts. Get the next generation locked in to Julia. Turn R into SPSS
Julia Computing raises $24M Series A
141–150 of 246 posts
Re: Julia Computing raises $24M Series A
#142Congrats to the Julia team. I am a python developer who has dabbled with Julia but it never stuck for me. I think Julia was built by academics for other academics running innovative high performance computing tasks. It excels at the intersection of 1) big data, so speed is important, and 2) innovative code, so you can't just use someone else's C package. Indeed, Julia's biggest successful applications outside academi…
> The next PyTorch needs to be written in Julia.
If a major company would pick up Flux.jl and fill out the ecosystem that would be AMAZING.
PyTorch and Tensorflow feel like duct tape and chewing gum all day, every day.
Re: Julia Computing raises $24M Series A
#143I would really love to use Julia, and for my team to as well. But we are too locked into R to even begin. If I were these folks, I would focus on the flow, not stock, of data analysts. Get the next generation locked in to Julia. Turn R into SPSS
They have to offer a plotting library that’s at least half as decent as ggplot if they want people to jump ship. Gotta be able to visualize your results easily and they’ve been stuck with a half-baked interface to python plotting libraries for years
Re: Julia Computing raises $24M Series A
#144I would really love to use Julia, and for my team to as well. But we are too locked into R to even begin. If I were these folks, I would focus on the flow, not stock, of data analysts. Get the next generation locked in to Julia. Turn R into SPSS
They have to offer a plotting library that’s at least half as decent as ggplot if they want people to jump ship. Gotta be able to visualize your results easily and they’ve been stuck with a half-baked interface to python plotting libraries for years
http://makie.juliaplots.org/stable/
hopefully will soon be a dominant force in this direction
Re: Julia Computing raises $24M Series A
#145Earlier quoted context omitted.
Interoperability between libraries that expect your code to be pure R / pure Python. If you use RCpp or Cython or CPython then you lose much of the magic behind the language that enables the cool (but frequently slow) features. My biggest pain point in this situation: you can not use SciPy or Pillow or Cython code with Jax/Pytorch/Tensorflow (except in very limited fashion). Differential equation solvers that need to…
Once your Rcpp code is compiled, it's almost indistinguishable from base R (when you're calling it). All R functions eventually end up calling R primitives written in C, and Rcpp just simplifies the process of writing and linking C/C++ code into the R interpreter. The only difficulty with Rcpp-based R packages is you have to ensure the target system can compile the code, which means having a suitable compiler availab…
> it's almost indistinguishable from base R (when you're calling it).
I am very surprised by this. Given how R is extremely dynamic. and has things like lazy-evaluation, that you can rewrite before it is called with substitute. Which I am sure some packages are using in scary and beautiful ways.
Re: Julia Computing raises $24M Series A
#146Earlier 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,…
> We are stuck with Julia 1.2
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.
Re: Julia Computing raises $24M Series A
#147Earlier 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,…
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 refreshing. I don’t doubt your experiences are real, but suspect you have just had really bad luck.
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.
Re: Julia Computing raises $24M Series A
#148Earlier 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,…
This matches with my experience with an old version of Julia too. But lets not focus on the negative aspects here. People have their different sets of priorities.
Re: Julia Computing raises $24M Series A
#149Earlier 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 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 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.
Re: Julia Computing raises $24M Series A
#150Are A-rounds now well into the $20M range? I remember when they hit $10M and assumed they had continued to grow somewhat. But I didn't realized we'd blown well past $20M — when did that happen?
I think a lot of it is that this is a series A for a 6 year old company that is already making money. Most other startups would be on series B, so comparing this number to a 10 person startup for a company that doesn't have a product yet doesn't make a lot of sense.
I don't have recent data but I'd guess most startups would have been dead for several years, not on series B.