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Python Weekly

Welcome to issue 591 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor

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News

DjangoCon US 2023 Call for Proposals
DjangoCon US 2023 call for proposals is open. Submit your talk and tutorial proposals by 2023-05-15.

Articles, Tutorials and Talks

How to Build a Complete Python Package Step-by-Step

Do you want to know how to build a complete Python package? Well, look no further! This video will take you step-by-step through the entire process, from creating your project to publishing it on PyPI.

Django: How to profile and improve startup time
Your Django project’s startup time impacts how smooth it is to work with. Django has to restart your project every time you run a management command and when runserver reloads. This involves importing all your apps, and thus all the modules that they import. As you add more code and dependencies to your project, you may notice its startup time increasing. Development can slow down to a crawl; reports of 10 second startup times are, sadly, not unusual. In this post we’ll cover tools for profiling startup time, and tactics for reducing it.

Python Automation with ChatGPT

In this video we are going to learn how to use Python to automate things for us so we don't have to do them manually. But here's even more interesting thing we will do we will ChatGPT and we will tell Python to use ChatGPT's API or the underlying API that ChatGPT is also using to generate the Python scripts for the two use cases.

How We Deploy 5X Faster with Warm Docker Containers
Using pex, Serverless Dagster Cloud now deploys 4 to 5 times faster by avoiding the overhead of building and launching Docker images.

Building a Bloom filter
In this post, we will explore the Bloom filter — a data structure that is ingenious in its simplicity and elegant in its design

Build A SaaS From Scratch

Learn how to build a SaaS from scratch using Django, Tailwind and Stipe as the payment gateway.

Training Deep Networks with Data Parallelism in Jax
One of the main challenges in training large neural networks, whether they are LLMs or VLMs, is that they are too large to fit on a single GPU. To address this issue, their training can be parallelized across multiple GPUs. This means either parallelizing the data or model to distribute computation across several devices. In this post, we'll cover batch splitting, also known as data parallelism, and show how to use JAX's pmap function to parallelize computations across multiple devices.

Data validation in Python: a look into Pandera and Great Expectations
Data validation is a vital step in any data-oriented workstream. This post investigates and compares two popular Python data validation packages - Pandera and Great Expectations.

Online gradient descent written in SQL
Modern MLOps is complex because it involves too many components. You need a message bus, a stream processing engine, an API, a model store, a feature store, a monitoring service, etc. Sadly, containerisation software and the unbundling trend have encouraged an appetite for complexity. I believe MLOps shouldn’t be this complex. For instance, MLOps can be made simpler by bundling the logic into your database. In this post, I want to push this idea, and actually implement a machine learning algorithm within a relational database, using SQL.

When to use Python's "list" function
When should you use the built-in list(...) function in Python? And when shouldn't you? The list constructor is both underused and overused in Python.

Harvard CS50’s Web Programming with Python and JavaScript

This web programming course from Harvard University picks up where CS50x leaves off. It dives more deeply into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Django, React, and Bootstrap. Topics include database design, scalability, security, and user experience. You will learn how to write and use APIs, create interactive UIs, and leverage cloud services like GitHub and Heroku.

The Speed of Python: It Ain’t That Bad!
I hear all the time that Python is way too slow. Is it?

Testing multiple Python versions with nox and pyenv

Overhead of Python Asyncio tasks

Why Python keeps growing, explained

Interesting Projects, Tools and Libraries

beartype
Unbearably fast near-real-time runtime type-checking in pure Python.

PyBroker
Algorithmic Trading in Python with Machine Learning.

duckargs
Productivity tool for quickly creating python programs that parse command-line arguments. Stop writing argparse boilerplate code!

Mathesar
An intuitive UI for managing data, for users of all technical skill levels. Built on Postgres.

DiffusionFastForward
Course on diffusion generative models in a fast forward mode.

Sushi
Sushi is a library that allows you to run functions from other languages without needing to translate the code.

gptcli
ChatGPT in command line with gpt-3.5-turbo api.

New Releases

Python 3.12.0 alpha 6 released

Upcoming Events and Webinars

Virtual: Python Web Conf 2023
Join Pythonistas from around the world for the 5th annual Python Web Conference (March 13-17). Tickets include 5 days, 65+ live talks, expert-led tutorials, social events, an exclusive pass to all conference recordings for 90 days, cool swag and more. Don’t wait, buy your ticket today!

SF Python Meetup March 2023
There will be following talks

Atri Framework: Open-source web framework for Python developers
Beyond GraphGPT: Large Language Models and Prompt Engineering

Virtual: PyLadies SoFlo Meetup March 2023
Join us for a quick primer on the Plotly graph_objects packages to create meaningful and engaging 3D visualizations.

PyData Berlin Meetup March 2023
There will be following talks

Feature engineering for machine learning with open-source
Fortuna, a Library for Uncertainty Quantification

PyData Cambridge Meetup March 2023
There will be following talks

Dealing with medical images: Common formats, tools, and pitfalls
Is Python Packages Management in GxP Environments Mission impossible?

PyData Bristol Meetup March 2023
There will be following talks

Building a Public-Facing and Low-Maintenance Wordle Solver with Unsupervised Learning
Software to Data: Lessons from a Crash Landing

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Python Weekly - Python Weekly - Issue 591

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