Python Weekly (Issue 550 May 19 2022)

Python Weekly - Issue 550

May 19, 2022

Python Weekly

Welcome to issue 550 of Python Weekly. Let's get straight to the links this week.

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News

In collaboration with the Metal engineering team at Apple, we are excited to announce support for GPU-accelerated PyTorch training on Mac. Until now, PyTorch training on Mac only leveraged the CPU, but with the upcoming PyTorch v1.12 release, developers and researchers can take advantage of Apple silicon GPUs for significantly faster model training. This unlocks the ability to perform machine learning workflows like prototyping and fine-tuning locally, right on Mac.

Articles, Tutorials and Talks

Learn about web scraping in Python with this step-by-step tutorial. We will cover almost all of the tools Python offers to scrape the web. From Requests to BeautifulSoup, Scrapy, Selenium and more.

In this video you will go through a Natural Language Processing Python Project creating a Sentiment Analysis classifier with NLTK's VADER and Huggingface Roberta Transformers. The project is to classify the sentiment of amazon customer reviews. 

msgspec is a schema-based JSON encoder/decoder, which allows you to process large files with lower memory and CPU usage. If you’re parsing JSON files on a regular basis, and you’re hitting performance or memory issues, or you just want built-in schemas, consider giving it a try.

Learn how you can use Python’s Kubernetes Client library to automate all the boring Kubernetes tasks and operations.

This notebook illustrates how to build an image search engine from scratch using Milvus. Milvus is the most advanced open-source vector database built for AI applications and supports nearest neighbor embedding search across tens of millions of entries. We'll go through image search procedures and evaluate the performance. Moreover, we managed to make the core functionality as simple as 15 lines of code, with which you can start hacking your own image search engine.

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SPONSORTips that may save you from the hell of PyYAMLYAML is a data-serialization language that is widely used. As a developer, I’m always dealing with YAML from time to time. But processing YAML, especially using PyYAML in Python is painful and full of traps. This post shares some tips and snippets that can make your life with PyYAML easier.Search PDFs with AI and PythonOr the joys and headaches of trying to process Turing-complete file formats.A Tutorial on Structural OptimizationStructural optimization lets us design trusses, bridges, and buildings starting from the physics of elastic materials. Let's code it up, from scratch, in 180 lines.The overengineered Solution to my Pigeon ProblemHow the author built a wifi-equipped water gun to shoot the pigeons on the balcony, controlled over the internet by a python script running openCV reading the camera image of an old iPhone.Getting Started with NLTK in PythonExploring some of the most common functions and techniques we can use to develop basic NLP pipelines.Speed Up Web Scraping with Concurrency in PythonScraping websites can be time-consuming because you have to deal with waiting for responses from the server and rate-limiting. That's why we will show you how to speed up your web scraping projects by using concurrency in Python.BooksMathematical Logic through PythonThe book's approach captures the essence of the mathematical analysis of Logic using a sequence of carefully designed programming projects in the Python programming language. Each chapter in the book provides the background for, explanation, implications, and mathematical treatment of an associated programming project. Interesting Projects, Tools and Librariesmitmproxy2swaggerAutomagically reverse-engineer REST APIs via capturing traffic.reloadiumAdvanced hot reloading & profiling for Python.django-rest-cliScaffold your Django Rest(DRF) projects faster with DR-CLI.NannyMLNannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases.behaveaslbehave-asl is a Python tool for running unit-style behavioral tests against an AWS Step Function. It is built on top of behave.pipe-interceptIntercept Windows Named Pipes communication using Burp or similar HTTP proxy tools.PiPPyPipeline Parallelism for PyTorch.ipyvizzuipyvizzu is the Jupyter Notebook integration of Vizzu. ipyvizzu enables data scientists and analysts to utilize animation for storytelling with data using Python.ColossalAIAn integrated large-scale model training system with efficient parallelization techniques.EurybiaEurybia monitor model drift over time and securize model deployment with data validation.New ReleasesPython 3.9.13 is now availableDjango 4.1 alpha 1 releasedTensorFlow 2.9.0  

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