Algebird
PyCall.jl
| Algebird | PyCall.jl | |
|---|---|---|
| 2 | 28 | |
| 2,287 | 1,438 | |
| 0.1% | 0.3% | |
| 7.3 | 6.1 | |
| 22 days ago | about 2 months ago | |
| Scala | Julia | |
| Apache License 2.0 | MIT License |
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Algebird
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What do you use when you have to store high cardinality metrics?
https://github.com/twitter/algebird (production ready, used at Twitter, but for the JVM)
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Symbolics.jl: A Modern Computer Algebra System for a Modern Language
Hey, I have... I'm a co-author of Algebird[0], which has many ideas that I'd pull over.
I'm hoping to introduce Clojure's "spec" or "schema" libraries so that the types at play can at least be inspectable inside the system. In a fully typed language, I'd implement the extensible generics as typeclasses.
I suspect it would make it quite a bit tougher (at least in the approach I'm imagining) for folks to write new generic functions, due to many type constructors...
On the other hand, the complexity is there, even if you don't write it down!
It would be a big project, and a worthy effort, to write down types for everything in SICM.
[0] https://github.com/twitter/algebird
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?
Breeze - Breeze is a numerical processing library for Scala.
py2many - Transpiler of Python to many other languages
Zeppelin - Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
Revise.jl - Automatically update function definitions in a running Julia session
Saddle
julia - The Julia Programming Language
Spire - Powerful new number types and numeric abstractions for Scala.
Genie.jl - 🧞The highly productive Julia web framework
ND4S - ND4S: N-Dimensional Arrays for Scala. Scientific Computing a la Numpy. Based on ND4J.
are-we-fast-yet - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.