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di
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Python Type Hints Are Turing Complete
Indeed.
The dep injection is fastapi and typer code though, and quite tied to it. So it's worth mentioning that somebody is attempting (quite successfully from the look of it) to create a generic lib of dep injection using annotation named DI, inspired by those libs: https://github.com/adriangb/di
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I built a standalone version of FastAPI's dependency injection system
Also, to clarify because I realized my comment above was confusing: I did not mean that 1 file == 1 function in the def a_function sense, I meant it in the "functionality" sense. So for example di/_scope_validation.py is a piece of functionality (validating scopes) and so it is in it's own file instead of being at the top of di/container.py. I guess the entire project could be a 1000 LOC container.py file, I think I would personally find that a bit hard to digest, but it's for others to read not me so...
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di: pythonic dependency injection
We provide some basic benchmarks comparing to FastAPI.
ideas
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Type information for faster Python C extensions
Lower latency native calls in Python would be extremely useful, thank you for your work! Is the following GitHub issue the right place to monitor progress? https://github.com/faster-cpython/ideas/issues/546
I'm open to doing some benchmarking. Several of my libraries have pure CPython bindings (StringZilla, UCall, SimSIMD), and all perform low-latency SIMD-accelerated ops, so might be a good testing ground :)
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How Many Lines of C It Takes to Execute a and B in Python?
Recent CPython development has been towards optimizations and addressing use cases that benefit from optimizations, some coming from the faster CPython initiative. You might just get your JIT[1].
[1] https://github.com/faster-cpython/ideas/wiki/Workflow-for-3....
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GIL removal and the Faster CPython project
The faster-cpython folks seem to be working towards a JIT (https://github.com/faster-cpython/ideas/tree/main/3.13) and both pyston and cinder have JITs. So I don't think anyone has ruled one out.
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Our Plan for Python 3.13
faster-cpython team has done a lot of work to experiment on it: https://github.com/faster-cpython/ideas/issues/485#issuecomm...
It kind of sounds like migration to register based is a foregone conclusion, but it's not very clear to me.
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Faster CPython at PyCon, part two
lots of big ideas are still remaining to be done. One example is the register based interpreter, see https://github.com/faster-cpython/ideas/issues/485
A previous plan called for the beginning of a JIT in 3.12, seen as "Trace optimized interpreter" here: https://github.com/faster-cpython/ideas/wiki/Workflow-for-3....
- EdgeDB – A graph-relational database built on top of Postgres
- Python 3.12 Nogil Benchmark
What are some alternatives?
xlcalculator - xlcalculator converts MS Excel formulas to Python and evaluates them.
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.
LaTeXML - LaTeXML: a TeX and LaTeX to XML/HTML/ePub/MathML translator.
faster-cpython - How to make CPython faster.
pydantic - Data validation using Python type hints
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
json-parser-in-typescript-very-bad-idea-please-dont-use - JSON Parser written entirely in TypeScript's type system
pyenv-virtualenv - a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)
python-typing-machines - Python type hints are Turing complete.
jnumpy - Writing Python C extensions in Julia within 5 minutes.
nogil - Multithreaded Python without the GIL
cinder - Cinder is Meta's internal performance-oriented production version of CPython.