cupy VS typer

I did not expect anything interesting, but this is actually cool.

> A full implementation of the NumPy API. Not something "NumPy-like" — just literally the NumPy API, with the same functions and the same arguments.

I suppose it's like https://cupy.dev/

What was the difference in runtime performance, and did you try CuPy?

https://github.com/cupy/cupy :

> CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.

Projects using CuPy:

It's more effort though. You will likely have to format your data in specific ways for the GPU to efficiently process it. I've done this kind of thing with PyTorch tensors, but there are also math-specific libraries like CuPy. If you only have millions, Numpy should be fine.

I'd also recommend checking out CuPy which aims to fully re-implement the Numpy api for CUDA GPUs, while taking advantage of Nvidia's specialized libraries like cuBLAS, cuRAND, cuSOLVER etc. The tradeoff being that it only works with Nvidia GPUs.

u/Spataner's answer is great. If you WANT GPU-enabled numpy functions, I would check out CuPy: https://cupy.dev/

Not sure how VS Code is relevant here as it's just you IDE, shouldn't have any influence on this. Now, seeing as you're using numpy (which has no gpu support), you could try and use something like CuPy in place of numpy. I'm not sure about the interoperability because I've never used this myself, but if you're lucky it could be as simple as just replacing all numpy calls with the same CuPy calls (or replacing all import numpy as np with import cupy as np ).

Cupy replicates the numpy and scipy APIs but runs on the GPU.

For that, you can use cupy[0], PyTorch[1] or Tensorflow[2]. They all mimic the numpy's API with the possibility to use your GPU.

[0] https://cupy.dev/

He is probably most well know for creating FastAPI that I taught to some of my clients and Typer that I've never used.

I have been using Typer on every one of my CLI projects which uses Click under the hood. The documentation is fantastic, the CLI app it produces looks great and lets you create things quickly. I high recommend it.

https://typer.tiangolo.com/

Adding CLI capabilities. My preferred library here is typer.

I just heard about this but it seems to be pretty much the type of thing you want and want fast.

Docstrings are for documenting how a function/ class/ method/ module works. Often you don't need to add a docstring to your main function because no one will be importing it to use elsewhere. And if you want it to run as a CLI, then there are better ways to document the available options. For example, typer does most of it for you, or in click you add the help text to the decorator.

Most computing tasks in airflow DAGs are KubernetesPodOperator containing a CLI (Python Typer). It allows us to pass arguments easily to run DAG manually if needed (the new UI to pass arguments to DAG in airflow 2.6 is really nice). Arguments allow us to replay DAG easily (change start / end dates for instance).

So it's basically the main .gitlab-ci.yml file plus a separate Python CI app using Typer for the AWS instrumentation.

Similarly for Typer, which is literally "the FastAPI of CLIs"[1]. Handy to type your `main` parameters and have CLI argument parsing. For more complicated cases, it's a wrapper around Click.

[1] https://typer.tiangolo.com/