fast-ruby
PyCall.jl
| fast-ruby | PyCall.jl | |
|---|---|---|
| 4 | 28 | |
| 5,643 | 1,438 | |
| 0.0% | 0.3% | |
| 4.6 | 6.1 | |
| 5 months ago | 2 months ago | |
| Ruby | Julia | |
| - | MIT License |
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fast-ruby
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Sorry for this noobest question
In this perspective, maybe the compilation of approaches by Fast Ruby provides a similar point of entry.
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Understading why attr_accessor in Ruby is faster than a regular method
Great article! I'm one of the maintainers of the `fast-ruby` project and I was glad to see that we have a benchmark for those idioms over here: https://github.com/fastruby/fast-ruby/blob/master/code/general/attr-accessor-vs-getter-and-setter.rb 🤓
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Super readable String operations with `delete_prefix` and `delete_suffix`
Stylistically, chomp has a bit more of the whimsy that you might expect from Ruby, while delete_suffix is a nice mirroring of delete_prefix and more explicitly named. Both have similar performance benchmarks and are faster than sub.
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Ruby 3.0.0 Released
If you’re interested in speed, check this repo: even among Ruby idioms there are big speed differences.
https://github.com/JuanitoFatas/fast-ruby
PyCall.jl
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I just started into Julia for ML
For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).
- The Mojo Programming Language: A Python Superset Drawing from Rust's Strengths
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Calling Chapel, Carbon, and zig code in Julia
PyCall.jl is really handy. Are there any similar projects for calling Chapel code, or Carbon/zig?
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Am I dumb in thinking I can use Rust as a Fast Python and leave it at that?
Julia and Python interop should not be a problem at all. Actually Julia has one of the best interops I’ve ever seen, so much that swift copied it. https://github.com/JuliaPy/PyCall.jl
- Which tools do you use for python + Data Science?
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I don't want to abandon Rust for Julia
One small note, julia also has great python interop via PyCall.jl
- Faster Python calculations with Numba: 2 lines of code, 13× speed-up
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Interoperability in Julia
It is possible to call Python from Julia using PyCall. Then to install PyCall, run the command in the Julia REPL.
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Why is Python so used in the machine learning?
That said, you can run python modules in Julia. So you can just export your code as a module and then use it in Julia via the PyCall package. short description here github here <— you’d just add the pacakge via the really nice package manager built into julia, but for link for more detailed documentation
- Use rust code in Python with pyo3
What are some alternatives?
Rails style guide - A community-driven Ruby on Rails style guide
py2many - Transpiler of Python to many other languages
Ruby style guide - A community-driven Ruby coding style guide
Revise.jl - Automatically update function definitions in a running Julia session
RSpec style guide - RSpec Best Practices
julia - The Julia Programming Language
Best-Ruby - Ruby Tricks, Idiomatic Ruby, Refactoring and Best Practices
Genie.jl - 🧞The highly productive Julia web framework
Fundamental Ruby - :books: Fundamental programming with ruby examples and references. It covers threads, SOLID principles, design patterns, data structures, algorithms. Books for reading. Repo for website https://github.com/khusnetdinov/betterdocs
are-we-fast-yet - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
contracts.ruby - Contracts for Ruby.
libffi - A portable foreign-function interface library.