mongo_orm
PyCall.jl
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| mongo_orm | PyCall.jl | |
|---|---|---|
| 1 | 28 | |
| 32 | 1,438 | |
| - | 1.2% | |
| 0.0 | 6.1 | |
| almost 4 years ago | about 1 month ago | |
| Crystal | Julia | |
| MIT License | MIT License |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mongo_orm
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Ruby 3.0.0 Released
> If you're not, you can port to anything and it'll be roughly equivalent and having a static language in Ruby's clothes doesn't scratch much of an itch.
There are a couple of reports about successful ports, e.g. https://forum.crystal-lang.org/t/experience-porting-a-ruby-w....
> I don't see a great argument for using Crystal in 2020
There is still the Ruby-like syntax appreciated by many developers and Crystal has also some other interesting aspects not present in Ruby or imperative OO languages in general, e.g. union types, powerful macros (see e.g. https://github.com/sam0x17/mongo_orm/blob/master/src/mongo_o...).
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?
gleam - ⭐️ A friendly language for building type-safe, scalable systems!
py2many - Transpiler of Python to many other languages
crystal - The Crystal Programming Language
Revise.jl - Automatically update function definitions in a running Julia session
mosquito - Trading Bot with focus on Evolutionary Algorithms and Machine Learning
julia - The Julia Programming Language
gcf.cr - gcf.cr provides serverless execution and deployment of crystal language code in Google Cloud Functions
Genie.jl - 🧞The highly productive Julia web framework
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
are-we-fast-yet - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
normandy - Channels for CSP style Ruby