What can we do to make Julia grow fast?

I’m a big fan of Julia and extremely interested in seeing it succeed and become a widely used language. What are some things that the community could still do to hasten or increase the probability of mass adoption of Julia?

As someone involved in machine learning, I think it is crucial for Julia to dominate in this area since ML is the new bread and butter and that is in large part why Python is growing so fast. So as I start to move my ML projects from Python to Julia, I’ll see what remaining low-hanging fruit is available to contribute to, but any other ideas?


ML people may comment about what’s needed in that area, but in general I think it’s very useful to check what features you miss, and contribute them to the appropriate packages (ML packages, JuliaStats packages, etc.).

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I feel the same way you describe. I would really like to see the best tool be the standard. It is common for many people to take for granted that the best tool is the one that’s used most …that seems reasonable but is often not the case. That’s why I spend a lot of my time searching out the great tools …and that’s exactly how I discovered julia !

Anyway, the thought I was going to share is this: Machine Learning is important but don’t forget about Human Learning!!! The guy (as in man, woman, child) that you help learn some basic (perhaps simple to you) concept today may be the guy that writes the big new module or invents the next important algorithm to make a fast language even faster. Everyone, without exception, has large gaps in their/our knowledge and understanding but many are afraid to ask for help. Having worked as a teacher it is easy to see why. Most any time a student asks a question he is criticized by the other students for being stupid. As in: “Hay! …que ******* tonta! ¡¿¡No entiendes!?!”. Some (not all) of the criticized questions were very good ones but those who did the criticizing didn’t even understand what was really being asked. And yet they were trying to play king of the mountain.

I work in the research dept. of a large university with campuses all over the state I’m in. I work with researchers in many fields, promoting research through cooperation/teamwork, giving the researchers new ideas for research and helping them to focus on work that can produce real-world results. I really enjoy my work. Some of the ideas I have recommended have even been awarded government grants. Although, my job entails more than just that, I can clearly see that no matter how intelligent and highly educated a researcher is, every single one of them (and myself) can use help in understanding what the best tools are to do his job even better …I mean software, math, programming languages and most anything else.

I think that the essence of the idea I’m trying to communicate/reinforce can be derived from one of the main bragging points of julia. It’s said to solve the two language problem. Maybe we could broaden this issue out even more and say every field tends to have it’s own language and tools. So I would say that in keeping with the spirit of julia we could make a habit of releasing the code we write, spend a lot of time on documentation and collaboration with others. It’s important to try to explain things in a way that someone coming from some other field can still understand it(or even a young person who just wants to learn). Sometimes the best explanation is a small code example that can be copy-pasted and run.

…just a thought but maybe you could make lot’s of code examples as blogs. Pick out your very favorite blogs about other languages and try to capture what it is you like about them in your blogs. I think that will help a lot of curious learners to take the dive into julia. Here… I’ll get the ball rolling:

I really like this guy’s style!

Let me explain why I like it so much…

  1. Clean, uncluttered, easy to read, good contrast: If I can read it easily I can focus better and learn without impediment. Some blogs have light blue and light grey and white, etc which, after 10 hours programming and 5 cups of coffee tend to strain the eyes :wink:. Not the case with this guy’s page. The font used there is also very readable and not too small.
  2. Incremental/systematic: If you notice, he throws down just a basic code example with explanation and then after the reader has gotten the hang of that, he goes ahead and makes a similar example but with more features. It’s like taking the person by the hand and saying: “come on, I’ll help you!” I have seen other authors do this very well. Sometimes it’s surprising how you can start stripping out extra code and get a very basic example in just a few lines that works. Especially in julia since it is very concise.
  3. Try it out fast: In the case of JavaScript you have the advantage that it will work in the browser. You can test the code for this blog out right there in the page. That’s a pretty great advantage! I’m not sure what the best way of applying this in julia would be. I think that it would be really cool if you could have the style of this blog + the functionality of a Jupyter notebook (IE, Ijulia) in the form of a web page. I guess I’ll leave it to you to determine what that will be.

I agree with what you say. The blog entry you pointed to however is not quite perfect. Look how messed up the 2nd listing is:

Presenting code in webpages is not quite trivial I guess.

@PetrKryslUCSD I agree that is a bit of a mess there and further improvements could still be made. It would be good to look at many more examples.

Beyond just “Presenting code in webpages” -which is important- I am particularly interested in letting people actually run the code from a web page. It’s one thing to say “look at this code!” and another to say “click here and see what it does” …Any ideas on this?

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Here’s another one I really like: https://bl.ocks.org/mbostock

This is not a blog per se and has its own limitations but it is really helpful and impressive to be able to just scroll over so many visual representations of graphical/mathematical concepts and get to an example in just one click.

Admittedly, not all concepts can easily be represented by an image… but generally speaking, this could be a very good way to present lots of blogs/tutorials.

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I’ve said this elsewhere, but perhaps this is a good place to reiterate that it seems absolutely crucial that the functionality for calling Julia from other languages, such as R and particularly Python, be robust and reliable. This doesn’t mean that I think Julia can immediately become a “C replacement” for people working on low-level parts of Python and R code, but it’ll simply be impossible for a large fraction of data scientists working in private industry to use Julia unless it cooperates with the tools that everyone else is using. I’m lucky to be in a place where things are flexible enough that I can use Julia, but that may not always be the case, and isn’t the case for a lot of people. It is much easier to convince colleagues that it is ok for me to be using Julia if they can be confident that they can call whatever I wrote from whatever they’re comfortable with. I was a Python skeptic from the moment I encountered it, so Julia was a pretty easy sell for me, but I think there are a lot of people out there who won’t even look at it until they find that they are already using it because somebody else wrote some Julia code that they are calling from Python.

Support-wise this is a tricky issue because most of us here are very happy using Julia and have no desire whatsoever to write Python code for calling it, but at some point many of us are going to have to buckle down and do it or watch the language we love disappear.


In order for any system to grow, you need to convince strangers to adopt it. An important part of persuading strangers is to show them the system you are champion works for what they want to apply it to. So, examples of real world use and success is important. An honest discussion of benefits and shortfalls is necessary as well. I have been using Julia in professional environments for at least a year. Some people in my region have been using it professionally for at least 2 years. We need to share our success stories from industry, academia, consulting, etc. There are a lot of examples of success with Python and R, which is they are widespread. As a community, we need to spread the word on our own successes and what part of that success that Julia uniquely supplied.


More broad package ecosystem and a good way of dealing with data. Both are nicely under way, but things take time :slight_smile:


Persuading people to adopt Julia is difficult because the package ecosystem is less mature compared to other languages. For many users, the strengths of the language alone are not enough to convince them to switch. I think the first step will involve attracting more people to develop the package ecosystem, which will help attract a larger user base. Attracting more developers should be easier as the core language stabilizes.


I think having more Julia beginner videos on Youtube covering more than just beginner stuff is a great way to highlight how easy Julia is to learn and in fact be good at, compared to other languages. This should accelerate the discovery and eyebrow raising process for potential users. For example one may not have time or be motivated enough to read a big part of the documentation to “discover” Julia, but watching a few short videos while having a little snack sounds a lot more lighthearted. And once a motivated person truly discovers Julia, it’s pretty much a one-way ticket to Julia land.


Here are my $0.02 worth:

  1. Plotting must be unobtrusive. It is unacceptable to have to wait for many seconds or minutes before DataFrames or Plots load and processing transitions to your own code.

  2. Debugger must be available.

  3. It must be easy to call Julia from Python or from R.

  4. Discovery of packages must be easy.

  5. There must be a solid selection of success stories and inspirational projects in Julia.


For me, defining “success” as fast growth seems ill-conceived. Even having Julia as a “C replacement” for Python or R misses the point. Julia can only survive as its own thing IMO, and like @mkborregaard wrote, those things take time.

I’ll throw out my plug for Julia Bloggers here, as its a great way to keep building community, getting examples out there for the broader public. Or write the library you wish existed, contribute to other libraries…


@randyzwitch I know that taste varies greatly so this is only an opinion. I think that the blogs such as the one you linked to leave much to be desired as far as aesthetics are concerned. Actually, I think if I could just pick out one detail to change I would say the code blocks. My recommendation, for whatever it’s worth, is that there be proper text highlighting at bare minimum. Beyond that I would recommend a dark theme for a couple reasons, (1) as far as I can tell there are a greater number of programmers who use an editor with a dark theme and it would feel more comfortable to them, (2) The contrast between the dark-theme code and the lighter colored blog will be pleasing and helpful in directing the eyes easily to what the reader is looking for.

Some of these ideas could perhaps even be applied to the main julia homepage too.

Of course, these are just personal opinions.

Yes. Using a language for scientific computing is all about packages. What can Julia do to show off packages? Here are some things.

  1. Complex number support. Simply put, a lot of physicists (especially quantum physicists) and electrical engineers need complex numbers. Package ecosystems which don’t work with them require a lot of extra work since then the user needs to split complex numbers into their real and imaginary parts. MATLAB handles complex numbers quite well across the board, but its “package ecosystem” is ehh… while in R/Python it’s really hard to performantly support both real and complex numbers in the same routines. Julia can do this easily through generic typing and adding appropriate dispatches. If NLsolve.jl, ForwardDiff.jl, Calculus.jl, and Optim.jl were made to fully work with complex numbers, this would be an area where Julia would be uniquely good and thus there would be specific fields where Julia could easily get a foothold.

  2. Arbitrary precision arithmetic. Quite simply, when packages have to be written in C/Fortran, arbitrary precision is pretty much impossible to support. This is why most of MATLAB/Python/R packages can’t do it. You need to look for specific instances where arbitrary precision matters, and show that Julia packages support it. My example is always differential equations, and in that area people always talk about floating point errors and whether things have converged sufficiently. With arbitrary precision, you can (if you have the time) solve the problem with 1e-20 error or something ridiculous, and put all questions about accuracy to rest. This is a great advantage in this field. @dpsanders’s work highlights another use. Also @lbenet’s TaylorIntegration.jl highlights high-precision arithmetic for safe integration of chaotic systems, something which is always a difficult problem to convince people (and yourself) when you have enough accuracy. Find fields where this matters in say optimization, and highlight it using Optim.jl (does this support arbitrary precision? We should check) in a blogpost.

  3. Iterator-based APIs with callbacks. This is more detailed, but in Julia you can build iterative-algorithms in a way that actually uses the iterator interface, and then give (at least in a separate lower-level API) full control over step-by-step behavior, along with allowing users to easily inject any code in the middle since it’s just using the iterator interface. This is a huge amount of control, and so this can definitely be a really nice way to generalize the functionality of iterative linear solvers and optimizers. I think Optim.jl has started to make use of this? The native DifferentialEquations.jl stuff does, and I think IterativeSolvers.jl had a work-in-progress PR here? Then you just have to think up interesting stuff to show with this. You can make an updating animation on what the local landscape and convergence looks like if you have this ability with an optimizer for instance.

  4. Domain-specific libraries. Because speed of package development is accelerated by Julia (at least if you care about performance), it’s much easier to build something very specific to your field. Do it.

  5. Abstract array typing for parallelism. Abstract array types seem almost silly, but if you make your optimization package only use broadcast and then someone throws a GPUArray in there, then if the user passes an f that is also some GPU call (either only uses broadcast or is a GPU kernel written in CUDANative.jl), congrats you have an optimization algorithm which rests entirely on the GPU without transferring the arrays back to the CPU. This can be taken advantage of all over, and really help Julia ride the GPU hype-train better than other languages.

  6. Metapackages. I think Julia’s final underused strength is the fact that all functions compile and will inline small calls. This means that you can easily make functions like (x,y)->f(y,x) which flip arguments, and Julia will inline this. By doing so, you can make something performantly sit on top of a bunch of other packages and slap a single API and documentation together, and really unify things in a way that say “this thing does every variation of X”. JuMP, Plots (well, Plots has some performance issues due to something unrelated here which can be fixed), Bio, and DifferentialEquations do this. I think this is important. People don’t like Python, they like SciPy/NumPy. We need to build these bigger “premiere” packages that are easy to point to. The issue with Julia’s current development is that its so fractured and everything is a single method in some dude’s repo. Users who aren’t developers don’t want to search for these edge packages and vet them themselves. In some sense, packages which pull functionality together, give it a community with some vetting process and community-knowledge is essential for adoption. Julia tends to have a lot more of “single method from recent paper” than “headliners” (SciPy, Pandas, etc.) and most people (for better or worse) just don’t trust those kinds of small projects, especially in terms of long-term support.


Maybe I’m misunderstanding the point of the original post. I linked to JuliaBloggers just to highlight that the community of readers is beginning to emerge, and that anyone who wants to write beginner articles has an instant audience.

How CSS fits into that and growing the Julia community? I mean, I could change the CSS, but I’m thinking that’s not really keeping people from adopting the language…

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Sorry @randyzwitch …by my comment it probably looks like I’ve gone off on a tangent. Also, I would definitely recommend getting others’ opinions before changing the CSS because I could well be the one with bad taste. Please continue to make blogs and tutorials. They are important and much needed. For that reason I appreciate your effort and the point you made.

My first comment was a bit lengthy but more clearly explains my main point. The main point being that one thing that could help Julia grow is to produce good teaching tools. If we want people to enjoy learning and also to make that process easier for them then it would be good to take aesthetics into consideration.

I’m not a graphic designer but I am willing to help out where I can …please let me know!

A lot of great suggestions have been made. Obviously there are a lot of things we can work on to help julia to continue to grow and mature.

We need a cuddly animal. Seriously.
Perl has the Camel. Python has a python (which are VERY cuddly snakes). Linux has Tux the penguin.
I think the Julia triangle logo is excellent, and we have seen how it is adapted to the various packages like Julia Physics.

There was a discussion on having perhaps a stand at the FOSDEM conference. We need something to be visible at an event. Also something which is a bit of fun.


I used Julia for the first time with my students last year, and the course foundered badly on one simple issue: Students like cute visuals. To make headway with young learner programmers, Julia must offer a single, standalone, easily usable and imageable graphics package for a wide range of purposes like graphing and 3D visualisation. The current stitched-together bag of dependencies on Python is just too complex for beginners to want to be bothered.

I guess another issue is the ability to just start and use Julia (without an IDE) in an arbitrary folder without having to reset any environment variables. Unless I’m missing something, that’s still a little clunky.

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Could you be more explicit what you mean? Post an example of some graphics you haven’t been able to do easily in Julia?

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