erlang-port-with-python
PyCall.jl
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erlang-port-with-python | PyCall.jl | |
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1 | 28 | |
0 | 1,438 | |
- | 1.2% | |
10.0 | 6.1 | |
almost 7 years ago | about 2 months ago | |
Python | Julia | |
- | MIT License |
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erlang-port-with-python
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OCaml is getting multicore support in the next major version (v5)
I think Elixir would be interesting for your usecase.
It's a dynamic, garbage collected language. It's easy to pick up and get going with. As a functional programming language there isn't a lot to learn in the way of language constructs, and you don't even have to do the 'wrestling with the type system' thing that you have to do in compiled functional languages like OCaml.
Its processing 'horsepower' is probably comparable to Python, but it's much better for building low latency things if you want to run something in a bit more of a production use case. This is also improving due to the recent addition of a JIT.
The addition of NX is making Elixir an increasingly interesting place to do ML - write Elixir, have it run on GPU etc. See https://dashbit.co/blog/nx-numerical-elixir-is-now-publicly-...
Python integration is probably best done using the Erlang 'port' system - running Python as a managed process and communicating with it using messages over stdin/stdout. I use it for C interop and it works well (and fits well with the Elixir/Erlang process model). It's not difficult to roll your own in Python e.g. https://github.com/fujimisakari/erlang-port-with-python/blob... or look at something like http://erlport.org/
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?
awesome-ocaml - A curated collection of awesome OCaml tools, frameworks, libraries and articles.
py2many - Transpiler of Python to many other languages
pyml - OCaml bindings for Python
Revise.jl - Automatically update function definitions in a running Julia session
libpython-clj - Python bindings for Clojure
julia - The Julia Programming Language
Genie.jl - 🧞The highly productive Julia web framework
are-we-fast-yet - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
libffi - A portable foreign-function interface library.
RCall.jl - Call R from Julia
typeshed - Collection of library stubs for Python, with static types