I've used, and am still using, Julia for my PhD research. It's perfect for parallel/distributed computing, and the neural network primitives are more than enough for my purposes. Anything I write in pure julia runs really, really fast, and has great profiling tools to improve performance further.
Julia also integrates with python, with stuff like PythonCall.jl. I've gotten everything to work so far, but it hasn't been smooth. The python code is always the major bottleneck though, so I try to avoid it.
Overall, julia is a significantly better language in every single aspect except for ecosystem and the occassional environment issue, which you'll get with conda often anyways. It's really a shame that practically nobody actually cares about it compared to python. It supports multi-dimensional arrays as a first-class citizen, which means that each package doesn't have it's own array like torch, numpy, etc, and you don't have to constantly convert between the types.
I agree on all points. I have used Python for 15 years, Julia for 3 and reach for Julia most of the time for personal projects. I was really stoked when at work the only FOSS solver for our problem was in Julia so we wrote the rest in it for easy integration. The only thing I dread is having to look for a new package since the ecosystem can be quite fragmented.
I actually find that ecosystem for Julia is not that big of an issue for me. I guess that my specific use-case (data analysis, numerical simulations) is probably the most developed part of the ecosystem, but regarding that I find that the ecosystem is much more homogeneous than for example python - most things work with most other things (eg units or measurement uncertainties libraries work automatically with a piloting library).
I actually prefer the smaller community of the jusia ecosystem. For one thing, when searching for issues online youre much less likely to hit spam “tutorials” or eternal september stuff.
And other peoples code is actually a pleasure to read
As someone working with it day to day, coming from around 18 years of mostly python, I wish I could say my experience has been great. I find myself constantly battling with the JIT and compilation and recompilation and waiting around all the time (sometimes 10 to 15 minutes for some large projects). Widespread macro usage makes stack traces much harder to read. Lack of formal interfaces means a lot of static checking is not practical. Pkg.jl is also not great, version compatibility is kind of tacked on and has odd behavior.
Obviously there are real bright spots too, with speed, multiple dispatch, a relatively flourishing ecosystem, but overall I wouldn't pick it up for something new if given the choice. I'd use Jax or C++ extensions for performance and settle on python for high level, despite its obvious warts.
Yeah, Jax with Equinox, jaxtyping, and leaning hard on python’s static typing modules + typeguard lets you pretend that you have a nice little language embedded in python. I swore off Julia a few years ago.
It would be much more useful to see metrics that aren't cumulative if we're interested in growth.
Cumulative measurements, by definition, will never decrease, even if Julia were to fall in popularity.
I am a MATLAB and Python user who has flirted with julia as a replacement. I don't love the business model of JuliaHub, which feels very similar to Mathworks in that all the cool toolboxes are gated behind a 'contact sales' or high priced license. The free 20 hours of cloud usage is a non-starter. Also it seems that by default, all JuliaHub usage is default cloud-based? on-prem and airgapped (something I need) is implied to be $$$.
Open sourcing and maintaining some components of things like JuliaSim or JuliaSim Control might expand adoption of Julia for people like me. I will never be able to convince my company to pay for JuliaHub if their pricing is similar to Mathworks.
I don't think this is really a good comparison. Matlab is a $150 for a personal license for the language itself ($1000/year for commercial), and if you want any packages, all the packages are extra on top of that. Julia is fully open source, and has a strong open source package ecosystem (I think we're up to 10k packages by now). Juliahub provides some enterprise systems like JuliaSim, but the language itself is totally free.
With some serious repositioning, I think there is still an opportunity for Julia to displace Python tools like polars/pandas/numpy, airflow, and pytorch -- with a unified ecosystem that makes it easy to transition to GPU and lead a differentiable programming revolution. They have the brain power to do it.
The future of Python's main open source data science ecosystem, numfocus, does not seem bright. Despite performance improvements, Python will always be a glue language. Python succeeds because the language and its tools are *EASY TO USE*. It has nothing to do with computer science sophistication or academic prowess - it humbly gets the job done and responds to feedback.
In comparison to mojo/max/modular, the julia community doesn't seem to be concerned with capturing share from python or picking off its use cases. That's the real problem. There is room for more than one winner here. However, have the people that wanted to give julia a shot already done so? I hope not because there is so much richness to their community under the hood.
Julia has really lost the differentiable programming mindshare to JAX. I’ve spent weeks or months getting tricky gradients to work in Julia, only to have everything “just work” in JAX. The quality of the autograd is night and day, and goes down to the basic design decisions of the respective “languages” (in the sense that JAX jit compiles a subset of Python) and their intermediate representations.
Fundamentally, when you keep a tight, purely functional core representation of your language (e.g. jaxpr’s) and decompose your autograd into two steps (forward mode and a compiler-level transpose operation) you get a system that is substantially easier to guarantee correct gradients, is much more composable, and even makes it easier to define custom gradients.
Unfortunately, Julia didn’t actually have any proper PLT or compilers people involved in the outset. This is the original sin I see as someone with an interest in autograd. I’m sure someone more focused on type theory has a more cogent criticism of their design decisions in that domain and would identify a different “original sin”.
In the end, I think they’ve made a nice MatLab alternative but there’s a hard upper bound on what they can reach.
> The future of Python's main open source data science ecosystem, numfocus, does not seem bright. Despite performance improvements, Python will always be a glue language.
Your first sentence is a scorching hot take, but I don't see how it's justified by your second sentence.
The community always understood that python is a glue language, which is why the bottleneck interfaces (with IO or between array types) are implemented in lower-level languages or ABIs. The former was originally C but often is now Rust, and Apache Arrow is a great example of the latter.
The strength of using Python is when you want to do anything beyond pure computation (e.g. networking) the rest of the world already built a package for that.
My experience with Julia was good, and the language is convenient, however two major factors made me not use it after test projects:
1. Very scarce packages ecosystem. Like there's dataframes.jl file with poor mans implementation of Pandas.
2. Recompiling everything every time. It meant that a Julia program in some script would take ~40 seconds compiling with dataframes & some other important packages.
I think if a language is to replace Python in science, it would need to either be very fast (recompilation on every run breaks this, and running Julia in a notebook/shell is interesting, but outside of pure scientific code, it should be easier to re-run it), or it should offer ergonomics. Pandas has very rough corners, especially when you need grouping with nontrivial operations, or grouped window functions. Joins aren't easy either. Any system that makes this more ergonomic, could bite a bit off Python. But I don't see such.
I think the Tidier.jl has made data in julia quite nice. TidierData leverages Dataframes.jl on the backend but with tidyverse syntax, and TidierDB.jl recreates python's ibis and Rs dbplyr. TidierDB (DuckDB is the main backend, but supports 11 other DB backends) also enables grouped window functions quite smoothly as well as joins and non equijoins.
I like the language but I can't help but feel it missed the train and that the ergonomics improvements it offers are too small to switch over from Python.
It does feel like julia will not make the leap to displace python, but for a long time python offered too few improvements over perl, so its not completely out of the question.
That's mostly because Julia questions get answered on its Discourse or Slack. The sharp decline is due to an automatic cross-post bot that stopped working.
No one bothered fixing it, in great part due to Discourse being the main place of discussion, as far as I know.
Even languages like Python and Javascript who are huge show a decline after 2022 which suggests ChatGPT is probably responsible. It would be better to have some other measure imo.
I love to see Julia growth, if nothing else by being another Dylan like take on Lisp ideas, with a JIT compiler in the box, and the community keeping the effort to overcome tooling issues despite critics.
Yeah it's interesting to see how it's getting on! I wrote my PhD simulation code in it from the ground up as it had nice fundamental abstractions for parallizable code. Of course now it's just Python and Scala/Java but Julia was great for my purpose.
I do scientific computing and a lisp was one of my first languages, so i feel like i ought to be the target audience, but it just never quite catches me.
It’s almost statically compilable which has almost gotten me to pick it up a few times, but apparently it still can’t compile a lot of the most important ecosystem packages yet.
The metaprogramming has almost gotten me to pick it up a few times, but apparently there aren’t mature static anti-footgun tools, even to the degree of mypy’s pseudo-static analysis, so I wouldn’t really want to use those in prod or even complex toy stuff.
It’s so damned interesting though. I hope it gets some of this eventually.
We do statistical modeling in Python in our company. When a statistician asked for R, I said "no, but you can have Julia". He's quite happy with it, and we're planning to move some stuff over.
Chapel folk did a really nice benchmark last year including Julia, where it landed pretty much right on the Pareto frontier of code size vs performance
jarbus|1 year ago
Julia also integrates with python, with stuff like PythonCall.jl. I've gotten everything to work so far, but it hasn't been smooth. The python code is always the major bottleneck though, so I try to avoid it.
Overall, julia is a significantly better language in every single aspect except for ecosystem and the occassional environment issue, which you'll get with conda often anyways. It's really a shame that practically nobody actually cares about it compared to python. It supports multi-dimensional arrays as a first-class citizen, which means that each package doesn't have it's own array like torch, numpy, etc, and you don't have to constantly convert between the types.
frakt0x90|1 year ago
SirHumphrey|1 year ago
shwouchk|1 year ago
And other peoples code is actually a pleasure to read
Bostonian|1 year ago
tmvphil|1 year ago
Obviously there are real bright spots too, with speed, multiple dispatch, a relatively flourishing ecosystem, but overall I wouldn't pick it up for something new if given the choice. I'd use Jax or C++ extensions for performance and settle on python for high level, despite its obvious warts.
catgary|1 year ago
nsajko|1 year ago
Huh? I think Pkg is very good as far as package managers go, exceptionally so. What specifically is your issue with it?
jakobnissen|1 year ago
tpoacher|1 year ago
cbruns|1 year ago
Open sourcing and maintaining some components of things like JuliaSim or JuliaSim Control might expand adoption of Julia for people like me. I will never be able to convince my company to pay for JuliaHub if their pricing is similar to Mathworks.
adgjlsfhk1|1 year ago
Kalanos|1 year ago
The future of Python's main open source data science ecosystem, numfocus, does not seem bright. Despite performance improvements, Python will always be a glue language. Python succeeds because the language and its tools are *EASY TO USE*. It has nothing to do with computer science sophistication or academic prowess - it humbly gets the job done and responds to feedback.
In comparison to mojo/max/modular, the julia community doesn't seem to be concerned with capturing share from python or picking off its use cases. That's the real problem. There is room for more than one winner here. However, have the people that wanted to give julia a shot already done so? I hope not because there is so much richness to their community under the hood.
catgary|1 year ago
Fundamentally, when you keep a tight, purely functional core representation of your language (e.g. jaxpr’s) and decompose your autograd into two steps (forward mode and a compiler-level transpose operation) you get a system that is substantially easier to guarantee correct gradients, is much more composable, and even makes it easier to define custom gradients.
Unfortunately, Julia didn’t actually have any proper PLT or compilers people involved in the outset. This is the original sin I see as someone with an interest in autograd. I’m sure someone more focused on type theory has a more cogent criticism of their design decisions in that domain and would identify a different “original sin”.
In the end, I think they’ve made a nice MatLab alternative but there’s a hard upper bound on what they can reach.
tomnicholas1|1 year ago
Your first sentence is a scorching hot take, but I don't see how it's justified by your second sentence.
The community always understood that python is a glue language, which is why the bottleneck interfaces (with IO or between array types) are implemented in lower-level languages or ABIs. The former was originally C but often is now Rust, and Apache Arrow is a great example of the latter.
The strength of using Python is when you want to do anything beyond pure computation (e.g. networking) the rest of the world already built a package for that.
culebron21|1 year ago
1. Very scarce packages ecosystem. Like there's dataframes.jl file with poor mans implementation of Pandas.
2. Recompiling everything every time. It meant that a Julia program in some script would take ~40 seconds compiling with dataframes & some other important packages.
I think if a language is to replace Python in science, it would need to either be very fast (recompilation on every run breaks this, and running Julia in a notebook/shell is interesting, but outside of pure scientific code, it should be easier to re-run it), or it should offer ergonomics. Pandas has very rough corners, especially when you need grouping with nontrivial operations, or grouped window functions. Joins aren't easy either. Any system that makes this more ergonomic, could bite a bit off Python. But I don't see such.
dizk12|1 year ago
https://github.com/TidierOrg/TidierDB.jl https://github.com/TidierOrg/TidierData.jl
of note i am biased as a tidier contributor and author of tidierdb.jl but hopefully you might be willing to give it a try.
xgdgsc|1 year ago
nsajko|1 year ago
> recompilation on every run breaks this
Your comment is exceedingly misleading. Whether and when Julia code gets compiled is up to the user.
NeutralForest|1 year ago
dv_dt|1 year ago
pjmlp|1 year ago
joshlk|1 year ago
https://trends.stackoverflow.co/?tags=julia
amval|1 year ago
No one bothered fixing it, in great part due to Discourse being the main place of discussion, as far as I know.
NeutralForest|1 year ago
eigenspace|1 year ago
mjgant|1 year ago
veqq|1 year ago
pjmlp|1 year ago
tajd|1 year ago
6gvONxR4sf7o|1 year ago
It’s almost statically compilable which has almost gotten me to pick it up a few times, but apparently it still can’t compile a lot of the most important ecosystem packages yet.
The metaprogramming has almost gotten me to pick it up a few times, but apparently there aren’t mature static anti-footgun tools, even to the degree of mypy’s pseudo-static analysis, so I wouldn’t really want to use those in prod or even complex toy stuff.
It’s so damned interesting though. I hope it gets some of this eventually.
toolslive|1 year ago
kayson|1 year ago
affinepplan|1 year ago
I know that's not exactly answering your question, but you might be interested https://chapel-lang.org/ChapelCon/2024/chamberlain-clbg.pdf
unknown|1 year ago
[deleted]
ofrzeta|1 year ago