Python Weekly (Issue 495 April 15 2021)

Python Weekly - Issue 495

April 15, 2021

Python Weekly

Welcome to issue 495 of Python Weekly. We have a packed issue this week. Enjoy it!

From Our Sponsor 

Build professional REST APIs with Python, Flask, Flask-RESTful, and Flask-SQLAlchemy

News

Thanks to a two-year grant commitment from Bloomberg, our second 2021 Visionary Sponsor and a long term committed supporter of the Python ecosystem, The Python Software Foundation (PSF) is hiring a full-time project and community manager for the Python Packaging ecosystem, with a specific focus on the Python Package Index (PyPI).

To date, access to CUDA and NVIDIA GPUs through Python could only be accomplished by means of third-party software such as Numba, CuPy, Scikit-CUDA, RAPIDS, PyCUDA, PyTorch, or TensorFlow, just to name a few. Each wrote its own interoperability layer between the CUDA API and Python. By releasing CUDA Python, NVIDIA is enabling these platform providers to focus on their own value-added products and services. NVIDIA also hopes to lower the barrier to entry for other Python developers to use NVIDIA GPUs. 

Articles, Tutorials and Talks

A great post breaking down the setup run a SaaS, from load balancing to cron job monitoring to payments and subscriptions. 

How to plan, buy, build, and store your 2-10 GPU machine learning servers and PCs.

Originally VaccinateCA was powered by a heavily customized Airtable instance, accompanied by a custom JavaScript app for the callers that communicated with the Airtable API via some Netlify functions. Learn how it was ported to a new custom Django backend, running on top of PostgreSQL.

Data lakes provide a myriad of benefits. They are data agnostic and don’t require you to define a schema upfront. However, without a proper structure, it may be challenging to find the data that you need. In this article, we’ll address this problem by creating a FastAPI abstraction allowing us to query the AWS Glue metadata catalog.

In this video we learn about dataclasses and how to use them, as well as the related attrs library that dataclasses were based on.

Progressive Type Checking on a Large Code Base.

This paper explores how developers use these type annotations, the type system semantics provided by type checking and inference tools, and the performance of these tools. We evaluate the types and tools on a corpus of public GitHub repositories. We review MyPy and PyType, two canonical static type checking and inference tools, and their distinct approaches to type analysis. We then address three research questions: (i) How often and in what ways do developers use Python 3 types? (ii) Which type errors do developers make? (iii) How do type errors from different tools compare?

This post details the specific mechanics of integrating Rust with Python to increase performance, with very little effort.

A bug that plagues thousands of open-source ML projects.

This post covers model inference optimization or compression in breadth and hopefully depth as of March 2021. This includes engineering topics like model quantization and binarization, more research-oriented topics like knowledge distillation, as well as well-known-hacks.

Leverage your Python Skills to Create Beautiful Mathematical Animations.

The series will cover: Writing a Python script that pulls the current day's weather from an API, Packaging the script into a Docker container, Automating using CI/CD to push the Docker container to AWS to run on AWS Lambda, Scheduling the AWS Lambda function to run automatically and Writing infrastructure YAML file to create components automatically.

In this post, you will learn what is a pipeline and How to use scikit-learn pipeline? How to do grid search and feature selection with pipelines? How to use column transformer with pipeline?

The Pi Foundation's first microcontroller is here! Learn how to program it using MicroPython.

Have some ingredients in your fridge but don’t know what to make? In the mood for a new, fun cocktail? We’ll build all of that as a telegram bot in this step-by-step python tutorial. 

Interesting Projects, Tools and Libraries

gProfiler combines multiple sampling profilers to produce unified visualization of what your CPU is spending time on, displaying stack traces of your processes across native programs, Java and Python runtimes, and kernel routines.

A toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction.

Remove tattoos using Deep Learning.

Logica is a logic programming language that compiles to StandardSQL and runs on Google BigQuery.

Quickly construct, mock & deploy PKI test configurations using simple declarative configuration. Includes CRL, OCSP and time stamping service provisioning.

Rotten Scripts contains amazing and awesome scripts written in Python, JavaScript, Bash, Powershell, and more.

OPAL is an administration layer for Open Policy Agent (OPA), detecting changes to both policy and policy data in realtime and pushing live updates to your agents.

Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats.

A library that provides several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning.

A Python Library for Document Layout Understanding.

Runtime type annotations for the shape, dtype etc. of PyTorch Tensors.

Divide full port scan results and use it for targeted Nmap runs.

A very lightweight monitoring system for Raspberry Pi clusters running Kubernetes.

Hot reloading for Python.

New Releases

Upcoming Events and Webinars

There will a talk, Retro Gamedev: Making your own compiled language for NES in Python

There will be following talks

  • Bias in NLP: using linguistics and computer science to improve AI/ML fairness

  • Automating social media content with Python and Mako

There will be following talks

  • How we task satellites to take pictures

  • How to detect silent failures in ML models

There will be following talks

  • Text Extensions for Pandas

  • Introducing Elyra: Extending JupyterLab for AI

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