Python Weekly (Issue 676 November 14 2024)

Python Weekly - Issue 676

November 14, 2024

Python Weekly

Welcome to issue 676 of Python Weekly. Let's get straight to the links this week.

Articles, Tutorials and Talks

This article is all about fire safety techniques and tools. It's about how you should think about dependency management, which tools you should consider for different scenarios, and what trade offs you'll have to make. Finally, it exposes the complexity and lingering problems in the ecosystem.

NanoDjango is a cool package that lets you build small scripts using all the power of Django, and also supports django-ninja for APIs. We'll dive into NanoDjango in this video, and will use uv and inline script metadata for dependency management.

A post about running large language models (LLMs) locally on a computer. It discusses what LLMs are and how to set them up to run on your own machine. The article also covers some of the limitations of LLMs, but highlights their potential for tasks like proofreading and creative writing.

Django's longevity is attributed to its stable core, time-based releases, and API stability policy. While there's enthusiasm for expanding Django's features, the author argues that the core should remain focused and prioritize stability. Instead, the community should embrace third-party packages as a way to innovate and extend Django's capabilities without compromising its core.

An introduction to the main techniques and latest models.

A closer look at non-elementary group-by aggregations.

Most Django scaling guides focus on theoretical maximums. But real scaling isn’t about handling hypothetical millions of users - it’s about systematically eliminating bottlenecks as you grow. Here’s how to do it right, based on patterns that work in production.

The author's take on what could be a project template for Django advanced usage, with modern tooling (for Python and UI dependencies, as well as configuration/environment management), but not too opinionated.

This video provides an in-depth, step-by-step explanation of Flash Attention, covering its derivation, implementation, and underlying concepts. The presenter explains CUDA, Triton, and autograd from scratch, then derives and codes both the forward and backward passes of Flash Attention.

Interesting Projects, Tools and Libraries

The no-nonsense RAG chunking library that's lightweight, lightning-fast, and ready to CHONK your texts.

Muon optimizer for neural networks: >30% extra sample efficiency, <3% wallclock overhead.

A trainable PyTorch reproduction of AlphaFold 3.

A suite of image and video neural tokenizers.

AlphaFold 3 inference pipeline.

LLM-powered multiagent persona simulation for imagination enhancement and business insights.

Elixir's pipe operator in Python.

A vim-like terminal reader to chat with your books.

A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper and Ada GPUs, to provide better performance with lower memory utilization in both training and inference.

PyQt-based application to create Beamer-LaTeX Presentations.

New Releases

Upcoming Events and Webinars

There will be following talks

  • Running Python data transformations at scale with dbt and Astronomer Cosmos

  • Anomaly Detection in Track Scenes

There will be following talks

  • Beginner Friendly IoT in Python – Object Oriented design for writing IoT servers

  • Transforming Jupyter Notebooks into Web Apps: A Python-Powered Approach to Heat Planning

There will be a talk, HTMX with Python.

There will be following talks

  • From stringly typed to strongly typed: Insights from re-designing a library

  • Foundational Models for Time Series Forecasting: Are We There Yet?

There will be following talks

  • Robust and Efficient Coupling of Perception to Actuation with Metric and Non-Metric Scene Representations

  • Understanding Context in the Wild - AI Testing Automated Driving Systems

  • Strategies Towards Reliable Scene Understanding for Autonomous Driving and Beyond

  • How to Unlock More Value from Self-Driving Datasets

There will be following talks

  • Developing High-Resolution Models for Future UK Climate Extremes

  • From Data Science to Data Engineering

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