python-dotenv
Poetry
python-dotenv | Poetry | |
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27 | 377 | |
7,151 | 29,631 | |
- | 1.6% | |
5.7 | 9.7 | |
1 day ago | 1 day ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
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python-dotenv
- What are the best ways to prevent writing secrets in the code.
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Chat with GPT-4 Web App in only 80 lines of Python
I personally just use .env to keep api keys
- I create a library for managing configurations as mappings, supports .env by default
- Error - UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
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Elegantly Handle Environment Variables in Python with Pydantic
similar to dotenv, I like this object oriented approach though.
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pdm-dotenv: Simplify Your Project's Environment Variable Management
Are you working on a Python project that uses pdm for dependency management and dotenv for local environment variable and secrets management? Do you find it frustrating when CLI tools like pgcli don't automatically pick up your .env file, forcing you to resort to npm install -g dotenv-cli? I've got a more convenient solution for you!
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Don't know how to work with .gitignore, first time actively working on a public repo
Similarly, I use python-dotenv which reads environment variables, or variables set in a .env file. Then in your settings.py file you can do things like:
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Writing unit tests, constants, and source control (GIT) question!?
Locally you may want to use a .env file that is also in .gitignore and python-dotenv to auto activate them. Also having an .env.template is also a good idea to help others working on the project know what they need to set in order for things to work.
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Need help running Django at a local machine after deploying it
But there's no web UI to set them for your local dev version. But there are various Python modules that will read environment variables from a file named .env. I like https://github.com/theskumar/python-dotenv myself. So try using that - create a .env file for your local dev site and use dotenv instead of os.getenv() to read the environment variables.
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Build Simple CLI-Based Voice Assistant with PyAudio, Speech Recognition, pyttsx3 and SerpApi
python-dotenv
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?
python-decouple - Strict separation of config from code.
Pipenv - Python Development Workflow for Humans.
django-environ - Django-environ allows you to utilize 12factor inspired environment variables to configure your Django application.
PDM - A modern Python package and dependency manager supporting the latest PEP standards
ConfigParser
hatch - Modern, extensible Python project management
django-dotenv - Loads environment variables from .env
pyenv - Simple Python version management
dynaconf - Configuration Management for Python ⚙
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
hydra - Hydra is a framework for elegantly configuring complex applications
virtualenv - Virtual Python Environment builder