Python-docker VS NumPy

This defines which image to inherit from, In this case we are using a Python image with the Python 3.11 version running on a slim version of the Bookwork version of Debian linux. Image definitions can be viewed from the DockerHub TAG link such as the python:3.11-slim-bookworm and official images like this one typically have pretty complicated definitions in order to get them highly optimized.

> You are going to eventually run into the same issue most people trying to use Alpine Linux just because of simplicity and being lightweight run: musl is not a completely ABI-compatible seamless replacement to glibc and might cause issue with statically linked binaries, and other annoying issues you won't foresee.

Well, if all you need is the server to run Docker/Podman, SSH and some other limited amount of packages, it shouldn't be too bad. Of course, there are also horror stories of Alpine resulting in way worse performance in select use cases: https://github.com/docker-library/python/issues/509 and there's the fact that Alpine might be popular inside of containers, but way less so outside.

Also, because of the short EOL cycle, I personally ditched Debian on servers (and Alpine in containers) myself for Ubuntu everywhere: https://blog.kronis.dev/articles/using-ubuntu-as-the-base-fo... A bit of a polarizing move (though RPM distros aren't much better at the moment), but it seems to have worked out for me in the end.

Doesn't mean that someone can't try, though, maybe their use case is suitable for Alpine.

This is the actual raw Dockerfile: https://github.com/docker-library/python/blob/master/3.9/slim-bullseye/Dockerfile

I usually cannibalize Docker Community's examples: Here

Official Docker image of Python 3.10.5-slim

If you want Python v3.7, perhaps try FROM python:3.7 (link)

Navigate into the Python directory and configure and ensure enable-optimization option is added as shown in the command below.

It looks like you are running into this bug: https://github.com/docker-library/python/issues/674

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]