typer
Poetry
typer | Poetry | |
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88 | 377 | |
14,398 | 29,552 | |
- | 1.3% | |
8.7 | 9.7 | |
7 days ago | 6 days ago | |
Python | Python | |
MIT License | MIT License |
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typer
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Github Sponsor Sebastián RamÃrez Python programmer
He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.
- Typer: Python library for building CLI applications
- Copilot for your GitHub stars
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Things I've learned about building CLI tools in Python
I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.
https://typer.tiangolo.com/
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Things to do with standalone script
Adding CLI capabilities. My preferred library here is typer.
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Where to start for managing a Python code base for public distribution
I just heard about this but it seems to be pretty much the type of thing you want and want fast.
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Help on Docstrings
Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.
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Which best practices do you follow to build robust & extensible ETL jobs?
Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).
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Devs on teams that deploy anytime you want, what does your SDLC workflow look like?
So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.
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The different uses of Python type hints
Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.
[1] https://typer.tiangolo.com/
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?
click - Python composable command line interface toolkit
Pipenv - Python Development Workflow for Humans.
Python Fire - Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
PDM - A modern Python package and dependency manager supporting the latest PEP standards
Gooey - Turn (almost) any Python command line program into a full GUI application with one line
hatch - Modern, extensible Python project management
rich - Rich is a Python library for rich text and beautiful formatting in the terminal.
pyenv - Simple Python version management
python-prompt-toolkit - Library for building powerful interactive command line applications in Python
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
cement - Application Framework for Python
virtualenv - Virtual Python Environment builder