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Python Weekly

Welcome to issue 606 of Python Weekly. Let's get straight to the links this week.

Articles, Tutorials and Talks

Geospatial Data in your Graph

In this stream we explore some techniques for working with geospatial data in Neo4j. We will cover some basic spatial Cypher functions, spatial search, routing algorithms, and different methods of importing geospatial data into Neo4j.

When NumPy is too slow
What do you do when your NumPy code isn’t fast enough? We’ll discuss the options, from Numba to JAX to manual optimizations.

Accelerating PyTorch Model Training
Using Mixed-Precision and Fully Sharded Data Parallelism.

Caching in Django with Redis
A step-by-step guide on implementing caching with Redis in Django.

Problems faced when downstream testing Python packages
The article explores the importance of downstream testing in Python package development, focusing on the challenges and strategies for ensuring reliable and compatible software dependencies. It provides insights into various testing techniques and tools to enhance the quality and compatibility of Python packages in real-world scenarios.

Documenting Native Python Extensions Made With Rust and PyO3
The post discusses the process of documenting native Python extensions created with Rust and PyO3, highlighting the benefits and challenges of documenting such projects. It provides practical tips and examples for effectively documenting Python extensions to improve their usability and maintainability for developers.

Automating Python code quality
The article emphasizes the importance of code quality in Python software development, discussing various aspects such as style consistency, code readability, testing, and documentation. It provides practical tips and best practices to improve code quality and maintainability, ultimately enhancing the overall software development process.

Building Real-time Machine Learning Foundations at Lyft
The article highlights Lyft's efforts in developing real-time machine learning foundations to enhance their platform's performance and user experience. It explores the challenges faced and the strategies employed to build scalable and reliable machine learning systems within the context of a ride-sharing company.

A Tale of Debugging: The Competitive Programmer Approach
Have the computer find the bugs for you.

Python dependency management redux

Django Model Fields With Attributes

Interesting Projects, Tools and Libraries

embedchain
Framework to easily create LLM powered bots over any dataset.

FastSAM
Fast Segment Anything.

generative-models
Generative Models by Stability AI.

vllm
A high-throughput and memory-efficient inference and serving engine for LLMs.

Django LiveView
Framework for creating a complete HTML over the Wire site or LiveView.

wanda
A simple and effective LLM pruning approach.

Automata
The Future is Self-Written.

Upcoming Events and Webinars

PyLadies London Meetup July 2023
There will be following talks

Maximizing GPU Utilization in Kubernetes: Strategies for High-Performance Python Applications
Telemetry with monkey patching & auto-instrumentation
The coding conventions that make our lives easier

PyData London Meetup July 2023
There will be following talks

Evaluating Distributional Forecasts
How To Build Your Own Private ChatGPT Using Streamlit, LangChain & Vicuna
Open Source is Technically Illegal
London Data Week

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basic spatial Cypher functions Fully Sharded Data Parallelism PyTorch Model Training compatible software dependencies Python package development Python software development Native Python Extensions browser Python Weekly Python code quality various testing techniques

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