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pants | ideas | |
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35 | 81 | |
3,100 | 1,647 | |
2.4% | 1.0% | |
9.8 | 7.3 | |
about 13 hours ago | about 2 months ago | |
Python | ||
Apache License 2.0 | - |
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.
pants
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The xz attack shell script
> C/C++'s header system with conditional inclusion
Wouldn't it be more accurate to say something like "older build systems"? I don't think any of the things you listed are "modern". Which isn't a criticism of their legacy! They have been very useful for a long time, and that's to be applauded. But they have huge problems, which is a big part of why newer systems have been created.
FWIW, I have been using pants[0] (v2) for a little under a year. We chose it after also evaluating it and bazel (but not nix, for better or worse). I think it's really really great! Also painful in some ways (as is inevitably the case with any software). And of course it's nearly impossible to entirely stomp out "genrules" use cases. But it's much easier to get much closer to true hermeticity, and I'm a big fan of that.
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Monorepo + Microservices + Dependency Managment + Build system HELL
Does pants/bazel can help me?
- Pants 2: The ergonomic build system
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Go Dependency management in large company projects - How do you do it?
Hyper-large tech companies managing hyper-large monorepos using Bazel (google), buck (Facebook), please (thought machine), pants (Twitter, Foursquare & Square) enjoy them but also have a lot of resources devoted to running and maintaining it.
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Reason to use other Build Tool than Make?
Yeah there's definitely some alternatives out there. Pants is another one that has a lot of traction.
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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.
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Sanity check of my decision for "Iterative AI" (DVC, MLEM, CML) pipeline over Azure ML
We don't have the CD yet, but I think what I put in place counts as simple CI (even if incomplete)? Every push & PR trigger an azure pipeline, which runs pants. This install the dependencies from the lockfile, run some linters, uses DVC to pull the data necessary for tests, and run unit tests (mypy check is deactivated until I solve a weird error). Basically the same script runs on laptops cross-platform (one of us uses Max, one Ubuntu with GPU, one Ubuntu with CPU, the scripts runs on every platform). The only difference with CI is the installation of Pants and the gestion of Cache (needs to be downloaded in CI so it takes ~3min in CI versus 20 seconds on my laptop).
- Pants 2: fast, scalable, user-friendly build system for codebases of all sizes
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Maintain a Clean Architecture in Python with Dependency Rules
This has also been recently integrated in pants.
- Blazing fast CI with MicroVMs
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?
Bazel - a fast, scalable, multi-language and extensible build system
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.
megalinter - 🦙 MegaLinter analyzes 50 languages, 22 formats, 21 tooling formats, excessive copy-pastes, spelling mistakes and security issues in your repository sources with a GitHub Action, other CI tools or locally.
faster-cpython - How to make CPython faster.
please - High-performance extensible build system for reproducible multi-language builds.
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
pyflow - An installation and dependency system for Python
pyenv-virtualenv - a pyenv plugin to manage virtualenv (a.k.a. python-virtualenv)
pyupgrade - A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language.
nogil - Multithreaded Python without the GIL
Buck - A fast build system that encourages the creation of small, reusable modules over a variety of platforms and languages.
jnumpy - Writing Python C extensions in Julia within 5 minutes.