This is my favourite comment about julia for like last few years +1 on that.
Ecosystem is extremly poor outside very few niches and most of the Deep Learning stuff isn't even faster than python api (+C ofc.) so swaping is just usless if u dont have time to write your own GPU kernals for every new opertaion.
Julia has some pretty swell cross-GPU packages. I was really hoping that it would catch on in the ML community, but I think we're past that point: the inferior solution has more momentum.
To me, Julia feels clunky as all hell. The other thing is that Julia doesn't help for deep learning, where something like 99.99% of the computation time is actually CUDA kernels.
I don't know; I actually really like the optional typing of Julia in theory, I don't know why I find the language so unpleasant.
I think biggest Julia problem is deployment and focus on Academia/HPC, its nice language and pretty fun to write, but it seems to be not good enough to replace Python and lost tons of momentu from hype train in 2017-2018?.
Julia still have a sucess story and is a lot bigger compared to D.
Whatever happened to Julia? Wasn't it supposed to incorporate all these incredible abstractions at the language level and run quickly on GPUs and everything in-between? Is it just lack of adoption or is has it something else?
Interesting. I'm a member of a Broad Institute lab that recently evaluated Julia. We reached the conclusion that Julia, while promising, lacked the flexibility and performance of our existing tools. I suppose this is a problem that investment could address!
Answering your question in good faith, even though I am unsure it was asked that way-
I am not a Julia programmer, I mostly write in python, but I find their community welcoming and not condescending at all. I think it would have a positive impact on most people’s personality
The language is very interesting too but doesn’t yet have a google, apple or msft behind it so I would understand why lovers of it maybe overstep a little promoting to try to keep it alive
Personally I find the integration with cuda to be really well done and I could see it being easier than python for highly customized deep learning (custom kernels etc)
Your comment above seems kind of unnecessarily mean spirited to me - maybe I’m reading it wrong?
I was surprised - because I remember you responding to the “I made 500k with machine learning guy” and being really impressed with your willingness to try to teach the guy without shitting on him (I’m an ex algo/hft guy and think someone with your knowledge could have gone that route very easily)
Why isn't some big company putting its weight behind Julia considering what it has to offer in the field of data science? I just don't get why it's still playing second fiddle to Python. Tech is full of inertia, paradoxically.
Thanks for the info. What worries me is that you can replace 'Julia' with 'Scala' in that sentence and a data scientist wouldn't know the difference. It's already fast. If Julia wants to win in data science then they need to poach users away from other languages.
People get settled in their toolsets. Besides, Julia does have a lot of syntax similarities to Matlab, but that doesnt mean that switching is straightforward.
It’s still a completely different language with it’s own paradigms, semantics, ecosystem, etc.
Julia is a very interesting language but big data processing is not a matter of the programming language itself, but how your platform and your architecture is put together.
And nearly always your bottleneck is not the language itself..
Also in the ~10 years Julia coders have been arguing Julia is inevitable, I haven’t seen a killer demo that shows something miles better than pandas, pyspark, PyTorch etc. In the same timespan if you look at Rust and Go they were able to capture large market share from compelling OSS projects that made people want to learn the language. Probably the missing piece for the Julia community is to create some library that does something innovative that makes people want to switch just to try it.
True, the D numeric ecosystem cannot be compared to Julia's and I would pick Julia if I was a scientist of course. Notice, I was talking about performance though.
I agree on the promise (perhaps already realized) of Julia. The library ecosystem there is so, so incredible. The language is custom formed to it's intended domain of numerical computation. I've been trying to ween myself off of python for these purposes.
Needless to say, my pushing of Julia also meets with a great deal of pushback. I have also had some setbacks. The 1.0 changes hurt my learning a bit. I don't like how it doesn't play well with brew. I feel like it is forcing me a bit into a Jupyter notebook style, which is not my favorite. All of these are small quibbles ultimately.
That's quite silly. Julia ecosystem is non-existent. If we move nimlang would be the closest . PyPy shaping up nice for cext part and when fully compatible we will just use PyPy.org.
Honest question: did you read the post at all? Even the quick summary addresses this.
Chris' whole point is that the biggest benefits of switching to Julia will be felt by the folks that are developing the packages and libraries for others to use. He's advocating that the best way to get you to want to switch isn't incremental language-level features, but rather it's first-in-class domain-specific packages.
This is something that will take time, but Julia's language-level features are uniquely positioned to enable the development of such packages. Chris is extremely productive (and definitely an outlier), but in less than two years he managed to coordinate and build a first-in-class ecosystem for differential equations.
So he's not advocating for you to switch at all — he's advocating for folks to build the packages (like his) that will get you to want to switch.
Ecosystem is extremly poor outside very few niches and most of the Deep Learning stuff isn't even faster than python api (+C ofc.) so swaping is just usless if u dont have time to write your own GPU kernals for every new opertaion.
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