Poetry
hatch
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Poetry | hatch | |
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377 | 20 | |
29,483 | 5,299 | |
2.6% | 3.9% | |
9.7 | 9.4 | |
about 21 hours ago | 6 days ago | |
Python | 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.
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
hatch
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Uv: Python Packaging in Rust
Exciting stuff! I view Hatch [1] as becoming the Cargo for Python because it's already close and has an existing (and growing) user base but I can definitely see depending on this for resolution and potentially not even using pip after it becomes more stable.
[1]: https://hatch.pypa.io/latest/
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lockfiles for hatch projects
I was inspired enough by the hatch sync idea that I created a PR to add that functionality to hatch: https://github.com/pypa/hatch/pull/1094
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Building and Releasing a Python CLI
Another concept I learned was about build backends, an import step which is used to initialize and install any dependencies of the app you're packaging. Since the tutorial went with using Hatch that is also what I went with, though it didn't provide a lot of useful details especially because it didn't show how to add any dependencies, so I took a look at the docs which were very nice and simple to follow.
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Is there an up-to-date python package template?
Try using hatch: https://hatch.pypa.io/latest/
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How do I install dependencies in Hatch?
I'm trying to learn Hatch, I currently use [Poetry](python-poetry.org/) to manage my dependencies, and while I'm overall happy with it, I really like the features I'm reading about with Hatch. I'm also working on learning CI pipelines & Dockerizing Python applications, and Hatch seems like a really useful tool to learn for this (and just as a general use tool).
- pipenv or virtualenv ?
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Call for questions for Guido van Rossum from Lex Fridman
Poetry 1.2 has been a pain. Which was the dev's fault though. Switching to something new while deprecating a related feature is just plain bad. I've been looking into modern alternatives like PDM and Hatch, but haven't used them (yet).
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So how do you actually deploy code/scripts?
For example, when it comes to Python, one option is to use the same packaging system that a huge number of open-source libraries and tools are published with. You can use setuptools or Hatch to build a "packaged" version of your code, and publish it to either the public PyPi repository or an internal one that you set up. Then your users can use pip to install your package, automatically fetch its dependencies, and keep it up to date, just like any other Python module.
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Scala isn't fun anymore
Don't forget the new PyPa tool on the block: Hatch.
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How to create a Python package in 2022
See also: https://github.com/pypa/hatch
What are some alternatives?
Pipenv - Python Development Workflow for Humans.
setuptools - Official project repository for the Setuptools build system
PDM - A modern Python package and dependency manager supporting the latest PEP standards
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
pyenv - Simple Python version management
poetry-dynamic-versioning - Plugin for Poetry to enable dynamic versioning based on VCS tags
reloadium - Hot Reloading and Profiling for Python
virtualenv - Virtual Python Environment builder
PyNeuraLogic - PyNeuraLogic lets you use Python to create Differentiable Logic Programs
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
pypyr automation task runner - pypyr task-runner cli & api for automation pipelines. Automate anything by combining commands, different scripts in different languages & applications into one pipeline process.