bunch
Numba
| bunch | Numba | |
|---|---|---|
| 1 | 127 | |
| 482 | 10,017 | |
| - | 1.0% | |
| 4.6 | 9.8 | |
| about 2 months ago | 13 days ago | |
| Python | Python | |
| MIT License | BSD 3-clause "New" or "Revised" License |
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bunch
Numba
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CuPy: NumPy and SciPy for GPU
I'm surprised to see pytorch and Jax mentioned as alternatives but not numba : https://github.com/numba/numba
I've recently had to implement a few kernels to lower the memory footprint and runtime of some pytorch function : it's been really nice because numba kernels have type hints support (as opposed to raw cupy kernels).
- I Use Nim Instead of Python for Data Processing
- Nvidia Warp: A Python framework for high performance GPU simulation and graphics
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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
What are some alternatives?
python-builddsl - A superset of the Python programming language with support for closures and multi-line lambdas
NetworkX - Network Analysis in Python
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Box - Python dictionaries with advanced dot notation access
Dask - Parallel computing with task scheduling
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
cupy - NumPy & SciPy for GPU
SymPy - A computer algebra system written in pure Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python
julia - The Julia Programming Language