pants
Poetry
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pants | Poetry | |
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35 | 377 | |
3,098 | 29,483 | |
2.5% | 2.6% | |
9.8 | 9.7 | |
1 day ago | 3 days ago | |
Python | Python | |
Apache License 2.0 | 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.
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.
0: https://www.pantsbuild.org/
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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.
https://github.com/pantsbuild/pants/issues/13393
- Blazing fast CI with MicroVMs
Poetry
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Understanding Dependencies in Programming
You can manage dependencies in Python with the package manager pip, which comes pre-installed with Python. Pip allows you to install and uninstall Python packages, and it uses a requirements.txt file to keep track of which packages your project depends on. However, pip does not have robust dependency resolution features or isolate dependencies for different projects; this is where tools like pipenv and poetry come in. These tools create a virtual environment for each project, separating the project's dependencies from the system-wide Python environment and other projects.
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Implementing semantic image search with Amazon Titan and Supabase Vector
Poetry provides packaging and dependency management for Python. If you haven't already, install poetry via pip:
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From Kotlin Scripting to Python
Poetry
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How to Enhance Content with Semantify
The Semantify repository provides an example Astro.js project. Ensure you have poetry installed, then build the project from the root of the repository:
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Uv: Python Packaging in Rust
Has anyone else been paying attention to how hilariously hard it is to package PyTorch in poetry?
https://github.com/python-poetry/poetry/issues/6409
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Boring Python: dependency management (2022)
Based on this comment 5 days ago[0], it's working? I'm not sure didn't dig in too far but based on that comment it seems fair to say that it's not fully Poetry's fault because torch removed hashes (which poetry needs to be effective) for a while only recently adding it back in.
Not sure where I would stand if I fully investigated it tho.
[0] https://github.com/python-poetry/poetry/issues/6409#issuecom...
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Fun with Avatars: Crafting the core engine | Part. 1
We will be running this project in Python 3.10 on Mac/Linux, and we will use Poetry to manage our dependencies. Later, we will bundle our app into a container using docker for deployment.
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Python Packaging, One Year Later: A Look Back at 2023 in Python Packaging
Here are the two main packaging issues I run into, specifically when using Poetry:
1) Lack of support for building extension modules (as mentioned by the article). There is a workaround using an undocumented feature [0], which I've tried, but ultimately decided it was not the right approach. I still use Poetry, but build the extension as a separate step in CI, rather than kludging it into Poetry.
2) Lack of support for offline installs [1], e.g. being able to download the dependencies, copy them to another machine, and perform the install from the downloaded dependencies (similar to using "pip --no-index --find-links=."). Again, you can work around this (by using "poetry export --with-credentials" and "pip download" for fetching the dependencies, then firing up pypiserver [2] to run a local PyPI server on the offline machine), but ideally this would all be a first class feature of Poetry, similar to how it is in pip.
I don't have the capacity to create Pull Requests for addressing these issues with Poetry, and I'm very grateful for the maintainers and those who do contribute. Instead, on the linked issues I share my notes on the matter, in the hope that it may at least help others and potentially get us closer to a solution.
Regardless, I'm sticking with Poetry for now. Though to be fair, the only other Python packaging tools I've used extensively are Pipenv and pip/setuptools. It's time consuming to thoroughly try out these other packaging tools, and is generally lower priority than developing features/fixing bugs, so it's helpful to read about the author's experience with these other tools, such as PDM and Hatch.
[0] https://github.com/python-poetry/poetry/issues/2740
[1] https://github.com/python-poetry/poetry/issues/2184
[2] https://pypi.org/project/pypiserver/
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Introducing Flama for Robust Machine Learning APIs
We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.
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How do you resolve dependency conflicts?
I started using poetry. The problem is poetry will not install if there is dependency conflict and there is no way to ignore: github
What are some alternatives?
Bazel - a fast, scalable, multi-language and extensible build system
Pipenv - Python Development Workflow for Humans.
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.
PDM - A modern Python package and dependency manager supporting the latest PEP standards
please - High-performance extensible build system for reproducible multi-language builds.
hatch - Modern, extensible Python project management
pyflow - An installation and dependency system for Python
pyenv - Simple Python version management
pyupgrade - A tool (and pre-commit hook) to automatically upgrade syntax for newer versions of the language.
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
Buck - A fast build system that encourages the creation of small, reusable modules over a variety of platforms and languages.
virtualenv - Virtual Python Environment builder