docker-django-example VS pip-tools

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.

- https://github.com/nickjj/docker-django-example

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.

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.

If anyone is interested I updated my Django / Docker starter project for 4.2: https://github.com/nickjj/docker-django-example

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.

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.

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.

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.

> Why is the "requirements.txt" file a stupid flat listing of all transitive dependencies with pinned versions? It makes it harder to change library versions even if there are no true conflicts.

My friend, here is what you seek: https://github.com/jazzband/pip-tools

requirements.txt is flat because it's really the output of `pip freeze`. It's supposed to completely and exactly rebuild the environment. Unfortunately it's far too flexible and people abuse it by putting in only direct dependencies etc.

If you're writing packages, you don't need a requirements.txt at all, by the way. Package dependencies (only direct dependencies) live in pyproject.toml with the rest of the package config. requirements.txt (and pip tools) are only for when you want to freeze the whole environment, like for a server deployment.

For all my projects I found myself regenerating manual lock files using complex shell commands with pip-compile to get a reproducible environments across devices using a custom pre-install-command. I finally decided that instead of hacking together the same solution on all my projects I would build a plugin that handles this complexity for me.

Instead of venv, we are using pip-tools in this starter kit. pip-tools take things further in dependency management. Check out what pip-tools does in their official GitHub repo. In short, it helps your project find the best match for the dependent packages. For example, you might need two packages A and B in your project that requires same package C under the hood. But A requires any version of C from 1.0.1 to 1.0.10 and B requires any version of C from 1.0.7 to 1.0.15. Pip tools will automatically compile the version of 'C' that suits for both of your packages.

I've created a small project called just-pip-tools that combines pip-tools and just to manage Python dependencies in a layered approach. This isn't a magic bullet; it's a set of files you can adapt to your needs.

For small projects I recommend pip-tools. Just write packet list in requirements.in and pip-compile compile a requirements.txt with comments.

Take a look at pip-tools, great package. https://github.com/jazzband/pip-tools

pip-tools

I recommend generating a lockfile to document this information, as you might do with pip-tools.

Check out pip-tools [1] which does exactly that, albeit in a slightly more polished way.

[1]: https://github.com/jazzband/pip-tools