Pyjion
hpy
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Pyjion | hpy | |
---|---|---|
23 | 20 | |
1,398 | 1,001 | |
- | 1.9% | |
6.4 | 8.2 | |
about 2 months ago | 22 days ago | |
C++ | Python | |
MIT License | MIT 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.
Pyjion
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Python 3.13 Gets a JIT
It exists, was created by microsoft employees, and is referenced in the article: https://www.trypyjion.com/
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Is anyone using PyPy for real work?
I've actually come across and started using Pyjion recently (https://github.com/tonybaloney/pyjion); how does Pypy compare, both in terms of performance and purpose? There seems to be a lot of overlap...
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funAndEasyToUse
Python is capable of doing things at runtime that are really hard to statically compile around, such as monkeypatching methods onto existing objects. You can compile it, but it's complicated. One strategy is to use a JIT that can observe application state at runtime and then invalidate code as it becomes obsoleted by changes, but it's complicated. See pyjion for an example.
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How is Golang websocket better than FastAPI websocket?
and if you need more speed you can try https://www.pypy.org/ or https://github.com/tonybaloney/Pyjion or https://www.pyston.org/
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CPython vs PyPy
Finally, there is also Pyjion which based on its website is βA drop-in JIT Compiler for Python 3.10β (https://www.trypyjion.com/). We will be covering it on a separate writeup. See you next time ;-).
- Accelerate Python code 100x by import taichi as ti
- Create CPython extensions in .NET?
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You think python is slow ?
Pyjion Easy to use, small compiler. Increase performance of our π CPython.
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Why doesn't CPython use a JIT?
Pyjion (and in particular its README) are worth a read for existing options.
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High Performance subset of python
https://github.com/tonybaloney/Pyjion Seems pretty cool
hpy
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RustPython
There is a merge request up to add autogen rust bindings to hpy
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Ruby 3.2βs YJIT is Production-Ready
Are you referencing https://github.com/hpyproject/hpy?
I do hope it takes off.
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Codon: A high-performance Python compiler
The HPy project [0] seems like a promising way out of this.
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New record breaking for Python in TechEmPower
socketify.py breaks the record for Python no other Python WebFramework/Server as able to reach 6.2 mi requests per second before in TechEmPower Benchmarks, this puts Python at the same level of performance that Golang, Rust and C++ for web development, in fact Golang got 5.2 mi req/s in this same round. Almost every server or web framework tries to use JIT to boost the performance, but only socketify.py deliveries this level of performance, and even without JIT socketify.py is twice as fast any other web framework/server in active development, and still can be much more optimized using HPy (https://hpyproject.org/). Python will get even faster and faster in future!
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Is it time to leave Python behind? (My personal rant)
I think Propose a better messaging for Python is the option and a lot of languages will learn it from Rust, because rust erros are the best described errors I see in my life lol. Cargo is amazing and I think we will need a better poetry/pip for sure, HPy project will modernize extensions and packages π¦ too https://hpyproject.org/
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A Look on Python Web Performance at the end of 2022
It also show that PyPy3 will not magically boost your performance, you need to integrate in a manner that PyPy3 can optimize and delivery CPU performance, with a more complex example maybe it can help more. But why socketify is so much faster using PyPy3? The answer is CFFI, socketify did not use Cython for integration and cannot delivery the full performance on Python3, this will be solved with HPy.
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socketify.py - Bringing WebSockets, Http/Https High Peformance servers for PyPy3 and Python3
HPy integration to better support CPython, PyPy and GraalPython
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Your Data Fits in RAM
Absolutely everything in CPython is a PyObject, and that canβt be changed without breaking the C API. A PyObject contains (among other things) a type pointer, a reference count, and a data field; none of these things can be changed without (again) breaking the C API.
There have definitely been attempts to modernize; the HPy project (https://hpyproject.org/), for instance, moves towards a handle-oriented API that keeps implementation details private and thus enables certain optimizations.
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Python 3.11 is up to 10-60% faster than Python 3.10
NumPy is a sore point (works, but slow) and the missing spark to ignite PyPy adoption for a subset of users. The current hope seems to be HPy. If PyPy acquires good NumPy performance, a lot of people would migrate. Also of note is that conda-forge builds hundreds of packages for PyPy already (I think they started doing that in 2020).
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The future of Python build systems and Gentoo
I've battled with Python's packaging systems for more than a decade. Every couple of years some new thing comes along promising to fix package management, but it always fails spectacularly on some important, mainstream package or use case. I suspect a lot of this complexity comes down to the significant degree in which the Python ecosystem depends on C: not only do we need to package Python programs but C programs as well, and some people think we should ship C source code and build on the target machine and others think we should ship pre-built C binaries and dynamically link against them on the target machine thus Python supports both mechanisms; however, both are perilous in the general case.
And interestingly, Python depends so hard on C because CPython is very slow, and CPython is very slow because the C-extension API is virtually the whole of the CPython interpreter (many interesting optimizations to the interpreter would break compatibility in the ecosystem). So it certainly feels like all of Python's major problems come down to this decision to lean hard on C and specifically to make so much of the interpreter available for the C-extensions.
The way out as far as I can tell is to define some narrower API surface (e.g., https://hpyproject.org), get the ecosystem to consolidate around that, deprecate the old API surface, and then make the requisite breaking optimizations such that the ecosystem can feasibly do more in native Python. This requires leadership; however, and this has not historically seemed to be Python's strong suite--the Python maintainers seem unable to drive out big, necessary changes like this (which is certainly not to say that leadership is easy, particularly when Python is so established in many respects).
Personally, I've come to use Go for 99% of my Python use cases and it's been great. There are dramatically fewer C bindings, so the build/packaging tooling and performance are orders of magnitude better than in Python. Static typing works well out of the box, real static binaries are not only feasible but trivial (as opposed to Python where you try to build a zip file with your dependencies and the result is hundreds of megabytes and it's still missing the runtime, std libs, and various .so files). Further still, builds, tests, and every kind of tooling are far faster than with Python, and far simpler to install and manage. Unless you're doing data science, I don't think you'll regret the transition.
What are some alternatives?
Numba - NumPy aware dynamic Python compiler using LLVM
Nuitka - Nuitka 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. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
cinder - Cinder is Meta's internal performance-oriented production version of CPython.
graalpython - A Python 3 implementation built on GraalVM
nogil - Multithreaded Python without the GIL
py2js
Cython - The most widely used Python to C compiler
pgcopy - fast data loading with binary copy
falcon - The no-magic web data plane API and microservices framework for Python developers, with a focus on reliability, correctness, and performance at scale.
psycopg2cffi - Port to cffi with some speed improvements