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News & links about Python programming.
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The Jupyter+git problem is now solved

Jupyter notebooks don’t work with git by default. With nbdev2, the Jupyter+git problem has been totally solved. It provides a set of hooks which provide clean git diffs, solve most git conflicts automatically, and ensure that any remaining conflicts can be resolved entirely within the standard Jupyter notebook environment. To

https://www.fast.ai/2022/08/25/jupyter-git/
5 Tips To Achieve Low Coupling In Your Python Code

In this video I share 5 tips to help you write code that has low coupling. I'll show you several examples and also share a story of a technique I used several times in the past that has really helped me reduce coupling and solve more complex software design problems.

https://www.youtube.com/watch?v=qR4-PBLUZNw
Building a backend from scratch using only OpenAI Codex

Developing with Codex is a bit special, and it sometimes takes a few attempts to get it to write exactly what you want it to. But in broad strokes, getting from nothing to something in just 10 prompts is really impressive and encouraging.

https://codeball.ai/blog/codex-todo-mvc
Stable Diffusion with Diffusers

In this post, we want to show how to use Stable Diffusion with the Diffusers library, explain how the model works and finally dive a bit deeper into how diffusers allows one to customize the image generation pipeline.

https://huggingface.co/blog/stable_diffusion
facebookresearch / esm

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

https://github.com/facebookresearch/esm
stable-diffusion

Stable Diffusion is a latent text-to-image diffusion model. Similar to Google's Imagen, this model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and runs on a GPU with at least 10GB VRAM.

https://github.com/CompVis/stable-diffusion
Accelerate Python code 100x by import taichi as ti

There is no universal solution to all optimization problems. That's partially why Python is fascinating. You can always find/create an easy-to-use tool that can precisely solve your problem at hand. In terms of scientific computing, Taichi is an ideal option within Python that can help you achieve performance comparable to C/C++.

https://docs.taichi-lang.org/blog/accelerate-python-code-100x
Multiprocessing Pool in Python

This guide provides a detailed and comprehensive review of the multiprocessing.Pool in Python, including how it works, how to use it, common questions, and best practices.

https://superfastpython.com/multiprocessing-pool-python/
Someone’s Been Messing With My Subnormals!

After noticing an annoying warning, I went on an absurd yak shave, and discovered that because of a tiny handful of Python packages built with an appealing-sounding but dangerous compiler option, more than 2,500 Python packages—some with more than a million downloads per month—could end up causing any program that uses them to compute incorrect numerical results.

https://moyix.blogspot.com/2022/09/someones-been-messing-with-my-subnormals.html