cupy VS textual

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/

The Textual project has a lot of screenshots in its documentation. These screenshots are built with the docs, so they are always up to date.

https://textual.textualize.io/

Textual[0] does this for CLI apps. That’s not for full GUI apps, but it’s very DOM-like, uses CSS selectors, etc. and a cool option when it meets your needs.

[0] https://github.com/Textualize/textual

For future projects that need a TUI beyond normal printing to a terminal, I'd recommend taking a look at Textual.

https://jupyterbook.org/en/stable/content/code-outputs.html#...

`less -R` is not the default.

FWIW, textual (and urwid) does ANSII escape codes well: https://github.com/Textualize/textual

  touch file$'\n'name

After installing logmerger, you can run a self-contained demo by running logmerger --demo, to view two log files before and after they are merged, and to play with the user-interface features provided by textual.

I think it just survived naturally, filling in the cracks left by Java / C++.

And not the era of Textual (https://textual.textualize.io/) is here, python may get the spotlight even more.