Robyn
Numba
Robyn | Numba | |
---|---|---|
61 | 124 | |
3,580 | 9,452 | |
7.2% | 1.1% | |
9.2 | 9.9 | |
6 days ago | 7 days ago | |
Python | Python | |
BSD 2-clause "Simplified" License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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.
Robyn
- Robyn – Innovator Friendly, and Community Driven Python Web Framework
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Introducing Dependency Injections in Robyn with a Twist!
For those who might not be familiar, Robyn is a fast, asynchronous Python backend web framework that operates with a Rust runtime, combining the best of both worlds for efficient and robust web development.
- Robyn: A Fast, Innovator Friendly, and Community Driven Python Web Framework
- Robyn – Web Framework in Rust
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FastHttp for Python (64k requests/s)
If you're comparing web frameworks you might also like to look at robyn https://robyn.tech/, which claims impressive performance. It's always tricky tho' to go from benchmarks to a particular use case.
- Robyn: High-Performance and Community-Driven Python Web Framework
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Robyn passes 1M installs on PyPi.
Robyn's Link - https://github.com/sparckles/robyn
- Robyn v0.38.0 - An improved CLI for create-robyn-app
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Robyn Finds a New Nest: Joining the Sparckles Open-Source Organization
For the unaware, Robyn , is a High-Performance, Community-Driven, and Innovator Friendly Python Web Framework with a Rust runtime.
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
NetworkX - Network Analysis in Python
uvicorn - An ASGI web server, for Python. 🦄
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
MMM-BurnIn
Dask - Parallel computing with task scheduling
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
cupy - NumPy & SciPy for GPU
Python-Regex - A port of the Rust regex library to python for super speed linear matching.
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
strawberry - A GraphQL library for Python that leverages type annotations 🍓
SymPy - A computer algebra system written in pure Python