1140
News & links about Python programming. https://pythonhub.dev/ Administrator: @rukeba
FastSAM
Fast Segment Anything.
https://github.com/CASIA-IVA-Lab/FastSAM
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.
https://mgorny.pl/articles/downstream-testing-python-packages.html
Django LiveView
Framework for creating a complete HTML over the Wire site or LiveView.
https://github.com/Django-LiveView/starter-template
vllm
A high-throughput and memory-efficient inference and serving engine for LLMs.
https://github.com/vllm-project/vllm
Django security releases issued: 4.2.3, 4.1.10, and 3.2.20
In accordance with our security release policy, the Django team
is issuing
Django 4.2.3,
Django ...
https://www.djangoproject.com/weblog/2023/jul/03/security-releases/
Django Model Fields With Attributes
https://jacobian.org/til/django-model-fields-with-attributes/
What are your favorite extensions for VSCODE that make coding in Python easier?
https://www.reddit.com/r/learnpython/comments/14lafpb/what_are_your_favorite_extensions_for_vscode_that/
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.
https://blog.fidelramos.net/software/python-code-quality
Caching in Django with Redis
A step-by-step guide on implementing caching with Redis in Django.
https://fly.io/django-beats/caching-in-django-with-redis/
PromtEngineer / localGPT
Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
https://github.com/PromtEngineer/localGPT
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.
https://pythonspeed.com/articles/numpy-is-slow/
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.
https://eng.lyft.com/building-real-time-machine-learning-foundations-at-lyft-6dd99b385a4e
ChristianLempa / videos
This is my video documentation. Here you'll find code-snippets, technical documentation, templates, command reference, and whatever is needed for all my YouTube Videos.
https://github.com/ChristianLempa/videos
Why Mac for Python dev?
https://www.reddit.com/r/learnpython/comments/14faxsa/why_mac_for_python_dev/
The Annotated S4
This post provides an overview of the Structured State Space for Sequence Modeling (S4) architecture which is a new approach to very long-range sequence modeling tasks for vision, language, and audio, showing a capacity to capture dependencies over tens of thousands of steps. It also includes code implementations that allow readers to experiment with the S4 architecture.
https://srush.github.io/annotated-s4
Lightning-AI / lit-gpt
Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
https://github.com/Lightning-AI/lit-gpt
Python dependency management redux
https://rednafi.com/python/dependency_management_redux/
wanda
A simple and effective LLM pruning approach.
https://github.com/locuslab/wanda
Introduction to Causality in Machine Learning
Home
Table of Contents
Introduction to Causality in Machine Learning
Correlation ...
https://pyimagesearch.com/2023/05/08/introduction-to-causality-in-machine-learning/
OAuth Authentication with Flask in 2023
A long time ago I wrote a tutorial on how to add logins with a social network to your Flask ...
http://blog.miguelgrinberg.com/post/oauth-authentication-with-flask-in-2023
Building a Toy Programming Language in Python
I thought it would be fun to go outside of my comfort zone of web development topics and write ...
http://blog.miguelgrinberg.com/post/building-a-toy-programming-language-in-python
generative-models
Generative Models by Stability AI.
https://github.com/Stability-AI/generative-models
Automata
The Future is Self-Written.
https://github.com/emrgnt-cmplxty/Automata
A Tale of Debugging: The Competitive Programmer Approach
Have the computer find the bugs for you.
https://albexl.substack.com/p/a-tale-of-debugging-the-competitive
XingangPan / DragGAN
Official Code for DragGAN (SIGGRAPH 2023)
https://github.com/XingangPan/DragGAN
embedchain
Framework to easily create LLM powered bots over any dataset.
https://github.com/embedchain/embedchain
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.
https://www.youtube.com/watch?v=djMsdSxvd2E
Python Hub Weekly Digest for 2023-07-02
https://pythonhub.dev/digest/2023-07-02/
Designing Pythonic library APIs
The article discusses some principles for designing good Python library APIs, including structure, naming, error handling, type annotations, and more. The author argues that Python's flexibility can be a double-edged sword, and that it's important to design APIs that are easy to use and understand.
https://benhoyt.com/writings/python-api-design/
arguably
arguably turns functions into command line interfaces (CLIs). arguably has a tiny API and is extremely easy to integrate.
https://github.com/treykeown/arguably