gevent
Numba
gevent | Numba | |
---|---|---|
5 | 124 | |
6,163 | 9,452 | |
0.2% | 1.1% | |
8.7 | 9.9 | |
3 months ago | 7 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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gevent
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Is anyone using PyPy for real work?
A sub-question for the folks here: is anyone using the combination of gevent and PyPy for a production application? Or, more generally, other libraries that do deep monkey-patching across the Python standard library?
Things like https://github.com/gevent/gevent/issues/676 and the fix at https://github.com/gevent/gevent/commit/f466ec51ea74755c5bee... indicate to me that there are subtleties on how PyPy's memory management interacts with low-level tweaks like gevent that have relied on often-implicit historical assumptions about memory management timing.
Not sure if this is limited to gevent, either - other libraries like Sentry, NewRelic, and OpenTelemetry also have low-level monkey-patched hooks, and it's unclear whether they're low-level enough that they might run into similar issues.
For a stack without any monkey-patching I'd be overjoyed to use PyPy - but between gevent and these monitoring tools, practically every project needs at least some monkey-patching, and I think that there's a lack of clarity on how battle-tested PyPy is with tools like these.
- SynchronousOnlyOperation from celery task using gevent execution pool on django orm
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How to Choose the Right Python Concurrency API
I'm not sure how much it replicates the CSP model, but the closest thing I've found to Go-style concurrency in Python is gevent: https://github.com/gevent/gevent
I personally still prefer to use it in all my projects.
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I have a problem with installing Ajenti on a 64bit Ubuntu 21.04 server
Greenlet seems to have some troubles compiling with Python 3.9. https://github.com/gevent/gevent/issues/1627
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?
eventlet - Concurrent networking library for Python
NetworkX - Network Analysis in Python
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Faust - Python Stream Processing
Dask - Parallel computing with task scheduling
Thespian Actor Library - Python Actor concurrency library
cupy - NumPy & SciPy for GPU
kombu - Messaging library for Python.
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
Tomorrow - Magic decorator syntax for asynchronous code in Python
SymPy - A computer algebra system written in pure Python