Machine Learning on Encrypted Data Without Decrypting It

Re:Machine Learning on Encrypted Data Without Decrypting It by orbifold on Tuesday November 26, 2019 @12:39PM (#21640560)

Julia always seemed great on paper and definitely is a strong candidate for replacing Matlab. But whenever I tried using it, the user experience seemed much more broken than python or c++. It just seems way easier to structure and work on a python + c++ project than it is to structure and work on a Julia project. A moderately sized sane c++ code base compiles and runs faster than whatever gymnastics Julia performs to create a single 2d plot (literally freezes my laptop for seconds). It also installs gigabytes worth of libraries into global directories by default. The compiler itself is also of questionable quality compared to clang, Swift or Ocaml.

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Re:Machine Learning on Encrypted Data Without Decrypting It by ViralBShah on Wednesday November 27, 2019 @12:38AM (#21645552)

The one thing Julia does not do is install gigabytes of libraries in global directories by default. The default install does not require root, and installs the packages and libraries in ~/.julia.

How did you end up with libraries in global directories?

Also, while we are working on reducing the time to first plot latency (which has to do with compiling large amounts of code - the time to second plot is 0.2sec), it would be good to learn more about the compiler quality issues you mention. We always welcome constructive comments on the Julia discourse.

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Re:Machine Learning on Encrypted Data Without Decrypting It by eigenspace on Tuesday November 26, 2019 @12:58PM (#21640771)

Compiler latency is being actively worked on and a PR was just pushed to Plots.jl which halved the time to first plot for many users.

Note that even before this though, it's just the compilation phase for the first plot that's expensive. Plotting itself is quite fast in julia, and if you start up julia with the command line argument `--compile=min` you'll see very very fast first plots. Obviously, this will come with a runtime performance cost though.

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Re:Machine Learning on Encrypted Data Without Decrypting It by fspeech on Tuesday November 26, 2019 @04:36PM (#21642997)

The particular attraction to me that Julia advertises is that it is much easier to read other people's Julia code. I recently tried Julia and found this to be quite true. It still lacks some critical features I need in the ML library but I can definitely see its attraction to users who want to understand the libraries they use.

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Re:Machine Learning on Encrypted Data Without Decrypting It by nslocum on Tuesday November 26, 2019 @12:43PM (#21640605)

Julia is compiled using LLVM.

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Re:Machine Learning on Encrypted Data Without Decrypting It by mirekrusin on Tuesday November 26, 2019 @02:24PM (#21641678)

clang, swift and julia are all based on llvm compiler toolchain. Ocaml has it's own compiler.

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Re:Machine Learning on Encrypted Data Without Decrypting It by svnpenn on Tuesday November 26, 2019 @01:56PM (#21641411)

> literally freezes my laptop for seconds

Yep. This has been commented on again and again, but many refuse to show willingness to fix or even acknowledge the problem:

https://github.com/JuliaLang/julia/issues/28092

https://github.com/JuliaLang/julia/issues/17285

https://github.com/JuliaLang/julia/issues/4452

https://github.com/JuliaLang/julia/issues/1064

https://github.com/JuliaLang/julia/issues/260

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Re:Machine Learning on Encrypted Data Without Decrypting It by pkofod on Tuesday November 26, 2019 @02:18PM (#21641616)

What do you mean by "refuse to show willingness to fix or even acknowledge the problem"? Do you realize that two of those issues were started by core contributors?

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