Python Weekly (Issue 636 February 1 2024)

Python Weekly - Issue 636

February 01, 2024

Python Weekly

Welcome to issue 636 of Python Weekly. Let's get straight to the links this week.

Articles, Tutorials and Talks

In this tutorial, Jeremy Howard demystifies CUDA programming for NVIDIA GPUs, particularly focusing on its accessibility when combined with PyTorch. The hands-on tutorial, conducted in a Colab notebook, guides viewers through practical examples, starting with simple tasks like converting an RGB image to grayscale, and progressing to more advanced topics like matrix multiplication. The emphasis is on simplifying the CUDA programming process for Python programmers, promoting debugging ease and development efficiency.

A closer look at the repository design pattern in Python. This is a very useful pattern that allows you to keep your data storage separate from your data operations.

Read how Polars executes your query from planning, optimizing to execution.

Learn how to use the Profila profiler to find performance bottlenecks in your Numba code.

The article explores various challenges and inconsistencies in the Python datetime library, as well as how different third-party libraries address these issues. 

All you need to know to understand the GPT-4 with Vision API with examples for processing Images and Videos.

The video outlines an effective workflow for combining Python and Rust - use PyO3 to call Python from Rust CLIs, leverage Rust's safety and Python's libraries, add testing to validate logic, automate processes, and deliver binaries to users. 

Unleashing AI Power on Your Laptop.

In this article, we will learn how to implement parsers in Python using Pylasu and ANTLR. 

We'll build a simple Django app that uses AI to color black and white photos. I'll also guide you through the easiest method to use webhooks in Django. Webhooks are a neat way of external functions (e.g., AI functions) sending data to your server.

The post discusses a Python tutorial on patching Pydantic settings in Pytest. It covers techniques for modifying Pydantic settings during testing scenarios in a Pytest environment, providing practical insights for effective testing and development workflows.

Interesting Projects, Tools and Libraries

An innovative autonomous agent designed to create personalized newspapers tailored to user preferences. GPT Newspaper revolutionizes the way we consume news by leveraging the power of AI to curate, write, design, and edit content based on individual tastes and interests.

16-bit CPU for Excel, and related files.

FastCRUD is a Python package for FastAPI, offering robust async CRUD operations and flexible endpoint creation utilities.

A lightweight, minimalistic, and synchronous Python package for quickly sending emails via Gmail.

Video Search and Streaming Agent.

fabric is an open-source framework for augmenting humans using AI.

Emerging Architectures for LLM Applications.

Upcoming Events and Webinars

There will be a talk, Battle-Tested Tips for Maximizing Learning from Tech Incidents.

There will be following talks

  • Build a Feature Store with Hamilton

  • Scaling Machine Learning Training and Inference @Vedrai

There will be following talks

  • Transform Your Business: A Roadmap to Being Data-Driven with Data Ops

  • Data Versioning with LakeFS and LakeFS-Spec

  • My Data Stays Here! Implementing AI Assistants in a Data-Saving Way

There will be following talks

  • Toolbox of a not-so Data Scientist

  • Boosting Similarity Search With Real-time Stream Processing

  • Open-Source Science (OSSci) 

  • (Maybe) faster Pandas with CuDF on the GPU (perhaps)

There will be following talks

  • Optimizing Ad Conversions with DS

  • Diversifying the feed with color vector selection

  • Using LLMs to convert freetext to structure

There will be following talks

  • What’s for dinner? Building an ML Platform to enhance online-grocery shopping at Picnic

  • Automated Data Quality at Scale using Cleanlab

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