docker-django-example
Poetry
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docker-django-example | Poetry | |
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44 | 377 | |
1,097 | 29,483 | |
- | 2.6% | |
7.8 | 9.7 | |
10 days ago | 1 day ago | |
Python | Python | |
MIT License | MIT License |
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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.
docker-django-example
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Django 5.0 Is Released
Congrats on the release to the Django community!
If anyone is curious, I updated my Django / Docker starter kit app to use Django 5.0 at: https://github.com/nickjj/docker-django-example
It pulls together gunicorn, Celery, Redis, Postgres, esbuild and Tailwind with Docker Compose. It's set up to run in both development and production.
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Working with Docker Containers Made Easy with the Dexec Bash Script
- https://github.com/nickjj/docker-django-example
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What's the correct way to install or version up packages when using Docker and Poetry?
For example I edit the regular non-lock file and then run ./run pip3:install from my host which handles the above. A repo with an example Django project in Docker can be found here https://github.com/nickjj/docker-django-example. There's a pip3-install script in the bin/ directory, you can replace that with Poetry commands instead.
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Docker advantages for a single developer?
Currently, I'm using a modified version of this Docker setup (https://github.com/nickjj/docker-django-example) to work locally and build/deploy a production image. However, using PyCharm as my IDE, the development process is incredibly slow, especially when adding or removing Python packages. It takes at least 3 minutes to rebuild the Docker image after adding a package, and PyCharm has to update its index. Additionally, PyCharm's inspector sometimes gets confused about which packages are already installed based on the requirements.txt.
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Django 4.2 released
If anyone is interested I updated my Django / Docker starter project for 4.2: https://github.com/nickjj/docker-django-example
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Django Local to Production - FTP or what ?
Lots of handy stuff in this Django and Docker example project https://github.com/nickjj/docker-django-example He does a good course about Docker too.
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psycopg2 in production
If you're using Docker with a Debian based image you only need to apt install libpq-dev and you're good to go, it only needs to exist in your Docker image not your VPS directly. I've been using it for years. Here's a working example if you want to poke around https://github.com/nickjj/docker-django-example.
- Looking to use Docker & Docker Compose in production and need advice.
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How to Dockerize any Django Application: A Step-by-Step Tutorial
On a positive note, I would recommend perhaps looking at https://github.com/nickjj/docker-django-example for a good, somewhat beginner guide for django + docker work.
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What is your development cycle when using docker and containers? What's the general flow between developing locally and running the containers to test.
I put together https://github.com/nickjj/docker-django-example which pulls together a typical Django set up using Gunicorn, Celery, Postgres, Redis, esbuild and Tailwind.
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?
Tailwind CSS - A utility-first CSS framework for rapid UI development.
Pipenv - Python Development Workflow for Humans.
django-async-orm - Bringing Async Capabilities to django ORM
PDM - A modern Python package and dependency manager supporting the latest PEP standards
headwind - An opinionated Tailwind CSS class sorter built for Visual Studio Code
hatch - Modern, extensible Python project management
launchr - Launchr is an open source SaaS starter kit, based on Django.
pyenv - Simple Python version management
full-stack-fastapi-template - Full stack, modern web application template. Using FastAPI, React, SQLModel, PostgreSQL, Docker, GitHub Actions, automatic HTTPS and more.
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
direnv - unclutter your .profile
virtualenv - Virtual Python Environment builder