Python Weekly (Issue 623 October 26 2023)

Python Weekly - Issue 623

October 26, 2023

Python Weekly

Welcome to issue 623 of Python Weekly. Let's get straight to the links this week.

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News

PyCon US 2024’s Call for Proposals is officially open for Talks, Tutorials, Posters, and Charlas! Submit your proposals by December 18, 2023.

Articles, Tutorials and Talks

Learn how to use pytest, the powerful testing framework for Python. Throughout this course you'll gain a deep understanding of pytest's features, best practices, and the nuances of writing effective tests. And at the end, you'll learn how you can use ChatGPT to help you write tests quicker.

To investigate the potential of voice analysis as a prescreening or monitoring tool for type 2 diabetes mellitus (T2DM) by examining the differences in voice recordings between nondiabetic and T2DM individuals.

In this article, Simon Willison explores the concept of embeddings in data analysis and machine learning, highlighting their role in representing complex data for efficient processing and providing practical insights on their use.

Miguel Grinberg writes that Flask maintainers often introduce trivial backwards incompatible changes into new releases of Flask and Werkzeug, causing extensions and tutorials to break until they are updated. There is now

as well. 

Learn what lambda expressions are and how they are used in Python.

SIMD is a CPU feature that lets you speed up numeric processing; learn how to use it with Cython.

Kernels are a simple but powerful abstraction in the Jupyter architecture. They encapsulate language interpreters and make them accessible through a standardized interface. This is the key to Jupyter’s remarkable versatility, with over 100 supported languages. Embedding a kernel in your custom application can seamlessly expose it to the Jupyter ecosystem. Not only can you attach JupyterLab to your program for e.g. state inspection, as you would do with a debugger, but you can truly extend your application with all the power of the Jupyter ecosystem.

Learn how to get your conda-based docker images down to a reasonable size using three things.

Tutorial on how to implement infinite scroll using django and htmx.

In under 4 minutes, we'll add Google sheets as your Django database. This is ideal for faster prototyping and small products.

Interesting Projects, Tools and Libraries

GPU environment management and cluster orchestration

Fast, barebones pip implementation in Rust.

Use GitHub Copilot locally on your Macbook with one-click!

Build browser agents for real world tasks.

Platform for General Robot Intelligence Development.

A Python application that creates a CV in PDF from a YAML/JSON input file.

A JAX-based simulator for autonomous driving research.

Sparsity-aware deep learning inference runtime for CPUs.

Generating Robotic Simulation Tasks via Large Language Models.

Voyager is a library for performing fast approximate nearest-neighbor searches on an in-memory collection of vectors.

Python pipe command line tool.

data load tool (dlt) — the open-source Python library for data loading.

Open source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS.

Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters

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