pex
ideas
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pex
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Our Plan for Python 3.13
We get (very) close to cross-environment reproducible builds for Python with https://github.com/pantsbuild/pex (via Pants). For instance, we build Linux x86-64 artifacts that run on AWS Lambda, and can build them natively on ARM macOS.
This is not raw requirements.txt, but isn’t too far off: Pants/PEX can consume one to produce a hash-pinned lock file.
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Is it possible pickle a function with its dependencies?
You should look into pex, or it’s parent build system pants. A PEX (Python EXecutable) file can package up all your code including dependencies and run on another machine of similar OS with just an available compatible interpreter.
- Pex: Python EXecutable
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security risks in python libs
For well-supported libraries, pip-audit might do the trick. Where I've worked, we have used a central build system with library version enforcement. The build system produces a deployable archive, like PEX or similar. Rock-solid tests and sandbox validation environments provide good paths for version upgrades. Restricting libraries to a small set, making sure those repos remain actively developed, performing audits and centralizing builds has helped organizations I've worked in keep on top of potential security issues.
- My latest blogpost, python packaging has moved forward, but we're still missing a crucial part - what do you think?
- PyBake: Create single file standalone Python scripts with builtin frozen file system
- I am frustrated with packaging python, please educate me.
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A function decorator that rewrites the bytecode to enable goto in Python
Don't know if I agree about the goto thing, but there are actually a number of options now for delivering varying degrees of self-contained Python executable.
When I evaluated the landscape a few years ago, I settled on PEX [1] as the solution that happened to fit my use-case the best— it uses a system-provided Python + stdlib, but otherwise brings everything (including compiled modules) with it in a self-extracting executable. Other popular options include pyinstaller and cx_freeze, which have different tradeoffs as far as size, speed, convenience, etc.
[1]: https://github.com/pantsbuild/pex
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Mypyc: Compile type-annotated Python to C
Somewhat related, I had a devil of a time a little bit ago trying to ship a small Python app as a fully standalone environment runnable on "any Linux" (but for practical purposes, Ubuntu 16.04, 18.04, and 20.04). It turns out that if you don't want to use pip, and you don't want to build separate bundles for different OSes and Python versions, it can be surprisingly tricky to get this right. Just bundling the whole interpreter doesn't work either because it's tied to a particular stdlib which is then linked to specific versions of a bunch of system dependencies, so if you go that route, you basically end up taking an entire rootfs/container with you.
After evaluating a number of different solutions, I ended up being quite happy with pex: https://github.com/pantsbuild/pex
It basically bundles up the wheels for whatever your workspace needs, and then ships them in an archive with a bootstrap script that can recreate that environment on your target. But critically, it natively supports the idea of targeting multiple OS and Python versions, you just explicitly tell it which ones to include, eg:
--platform=manylinux2014_x86_64-cp-38-cp38 # 16.04
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?
mypyc - Compile type annotated Python to fast C extensions
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.
setup.py - 📦 A Human's Ultimate Guide to setup.py.
faster-cpython - How to make CPython faster.
python-goto - A function decorator, that rewrites the bytecode, to enable goto in Python
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
pyBake - Create single file standalone Python scripts with builtin frozen file system
pyenv-virtualenv - a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)
plusplus - Enables increment operators in Python using a bytecode hack
jnumpy - Writing Python C extensions in Julia within 5 minutes.
typed_python - An llvm-based framework for generating and calling into high-performance native code from Python.
nogil - Multithreaded Python without the GIL