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Julia 1.0

julialang.org

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Re: Julia 1.0

#41

Post by fundamental »

amrrs wrote: post #12

I use both R and Python in my work but when we move our models to production it's not real time, just a batch execution like once in a day. I'd like to hear from anyone who uses Julia in their actual job/work. Is it worth learning Julia, hoping to use it in work some day?

I use it pretty much every work day to explore ML/DSP ideas and to experiment around before formalizing an algorithm. It's fast to run (which is important to be able to go through enough data to make conclusions), the code produced is reasonably readable, there are a decent number of libs, and it's fairly quick to write. I'd say if you're looking for something like a MATLAB/R/Python/etc replacement Julia is a good option, though it has been a bumpy ride at times while the language has been evolving.

I think I started using Julia around 2013 or so after getting fed up with the slower speed of octave, so I'm guessing that things should be a fair bit smoother from this point on if they've officially tagged 1.0.

Re: Julia 1.0

#43

Post by ViralBShah »

rrock wrote: post #30

Congrats to everyone behind this effort! I’m looking forward to helping out with getting the image processing packages in Images.jl packages updated for 0.7/1.0.

Tim Holy did say he is looking forward to catching up with upgrading Images.jl to 1.0.

Re: Julia 1.0

#44

Post by Q6T46nT668w6i3m »

bsdubernerd wrote: post #39

I'm a quite happy Julia user, however I feel there are still some warts in the language that should have warranted a bit more time before banging 1.0 on the badge. Exception handling in julia is poor, which reminds me of how exceptions are (not/poorly) handled in R. Code can trap exceptions, but not directly by type as you _would_ expect. Instead, the user is left to check the type of the exception in the catch block…

Yeah, I agree with your comments about error handling. It’s far from ideal in non-interactive contexts. It’s especially disappointing since you could easily imagine something like Julia replicating Python’s success at transitioning code from interaction (e.g. Jupyter notebook) to production.

I initially defended the choice, but I now agree that 1-based indexing now seems like a poor choice since Julia has become something more than the original mission of a better MATLAB or Octave. It’s a, admittedly, minor tragedy of Julia’s success.

Re: Julia 1.0

#46

Post by dagw »

typon wrote: post #42

Reading through the docs, looks like they have 1-indexed arrays..?

That is correct, mainly due to coming from Fortran and Matlab background. However they also have offset arrays (like in Fortran) if you need arrays that start on a different index.

Re: Julia 1.0

#48

Post by dagw »

_0w8t wrote: post #11
ai_ia wrote: post #4

Earlier quoted context omitted.

I really hope for Julia to become mainstream and maybe replace Python as the defacto lang for data science. Julia is an incredible language. Kudos to the team developing it.

This is my thinking as well. Python is nice to glue things, but doing high-performance math is not its strength. Things like GIL should be addressed long time ago, but it seems it is so fundamental to make things work in Python that I have big doubts that it will ever be addressed.

The GIL has been 'addressed' regularly since python 1.4, just no one has come up with an acceptable solution.

Re: Julia 1.0

#49

Post by taliesinb »

I have high hopes for Julia becoming the defacto open-source scientific language. Despite Python and R both having a massive head start, I'm willing to bet that talented engineers and scientists will be drawn to Julia to implement their next-generation frameworks owing to the powerful features that it offers.

For example, the fact that an array of unions such as Array{Union{Missing,T}} is represented in memory as a much more efficient union of arrays is a perfect example of where a clever compiler can make the logical thing to do also the efficient thing to do!

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