Python

Streamlit is well-loved by many people, especially among data folks due to the fact that it does not require prior web programming knowledge to get started. Popular use cases for streamlit can be anything from a quick machine learning application poc or internal dashboards. But what if you want to create a production deployment? Would streamlit still be a viable option? Experiment setup For a production deployment, let’s assume there are 500 concurrent users. For a single-page streamlit with a chat box taking 3 seconds to return a single-paragraph message: ...

Python package management is not straightforward, seeing default package manager (pip) does not behave like node’s npm, in a sense that it doesn’t track dependencies versions. This is why you should use poetry to manage python packages, since it creates a lock file, so you can be sure that on every re-install, the versions would be the same. However, this poses a challenge when you want to create a docker image with poetry, because you need to do an extra pip install poetry (unless you bake this into your base python image). Additionally, turns out using poetry to install packages results in larger docker image size. ...

Updated 2023-09-09: I now revert back to plain requirements.txt, since pipenv is very poor at resolving large dependencies list. Poetry still remains funky so that’s off the table as well. Updated 2024-01-20: I’ve been using poetry in production for a few months now and so far it works great! Looks like they ironed out a lot of rough edges. Although I would still recommend people to use pre-commit to generate plain requirements.txt, this is so that in ci or dockerfile you don’t have to install poetry and set in-project venv every time. ...