Update 12 of the Big Data Tools plugin for IntelliJ IDEA Ultimate, PyCharm Professional Edition, and DataGrip has been released. You can install it from the JetBrains Plugin Repository or from inside your IDE. The plugin allows you to edit Zeppelin notebooks, upload files to cloud filesystems, and monitor Hadoop and Spark clusters.

In this release, we've added experimental Python support and global search inside Zeppelin notebooks. We’ve also addressed a variety of bugs. Let's talk about the details.

Intro

So you are writing some CPU-intensive code in Python and really trying to find ways out of its single-threaded prison. You might be looking towards Numba's "nopython parallel" mode, you might be using forked processes with multiprocessing, you might be writing microservices with database-like coordinators, or even writing your own multithreaded programs in C/C++ just like creators of TensorFlow did.

In this article I'm describing a rationale for my pet project where I try to implement facilities for general purpose multitasking to be used in a form of simple python code, employing a database-like approach for interpreters communication, while keeping the GIL (Global Interpreter Lock) and trying to be as pythonic as possible.

It could also become handy in the light of upcoming multiple interpreters support in CPython.

As far as I know, nobody came that far in trying to provide Python program with native shareable storage. The last closest attempt was Python Object Sharing which is pretty much dead by now. I hope my project won't meet the same fate.

What is full-stack development?

What qualities does the ideal full-stack developer possess?

• Mastery over front-end technologies
• Knowledge of at least one server-side programming language
• Concrete understanding of DBMS technology
• UI/UX design skills
• Experience in handling servers
• Knowledge of Git and version control systems
• In-depth understanding of web services or API
• Awareness of security concerns and frameworks
• Understanding of algorithms and data structure

With ML projects still on the rise we are yet to see integrated solutions in almost every device around us. The need for processing power, memory and experimentation has led to machine learning and DL frameworks targeting desktop computers first. However once trained, a model may be executed in a more constrained environment on a smartphone or on an IoT device. A particularly interesting environment to run the model on is browser. Browser-based solutions may be used on a wide range of devices, desktop and mobile, online and offline. The topic of this post is how to prepare a model for the in-browser usage.

This post presents an end-to-end implementations of a model creation in Python and Node.js. The end goal is to create a model and to use it in a browser. I'll use TensorFlow and TensorFlow.js as main frameworks. One could train a model in Python and convert it to JS. Alternative is to train a model directly in javascript, hence omitting the conversion step.

I have more experience in Python and use it in my everyday work. I occasionally use javascript, but have very little experience in the contemporary front-end development. My hope from this post that python developers with little JS experience could use it to kick start their JS usage.

We have all been there  —  having a nice idea for a hackathon, hobby or a side project and having a burning desire to start coding as soon as possible. And how many possibilities (Heroku, Glitch and others) are there now to bootstrap your app and deploy it immediately.

In the modern world, here and there ideas are arising about using data science for an extra benefit. For instance, Google can use a history of watched videos for providing recommendations about new ones. Online shops are using a recommendation system for increasing your receipt. However… if companies use the data for their benefit, could we do the same for own needs such as looking an online English teacher?

In February 2020, when the disease came to Europe, it became apparent to me that our timid hopes that the epidemics would subside and be finally buried in the China's soil were ruined. It was already evident from the Chinese statistics that the virus is lethal enough to scare and mild enough to pass unnoticed in many cases and, thus, to guarantee its effective dissemination. The question was when it reaches each next country.

Another question was the individual risks, especially the risk of lethal outcome if one contracts the virus. The average figure of around 5% was circulated by late January and early February. It was known that males were more susceptible to fatal outcomes. By February, it was also evident that the virus doesn't lead to death only in the elderly — the middle age was significantly affected, as well.

About a week I wonder around the idea of this article, mainly, because of missing the content here and tidiness of the coronavirus-themes. But, when robotic-harvester, hacking zip with bitcoins and other cool articles released — I decided not to publish crude material.

However, unexpectedly today the maintainer of this review hero showed up from the lockdown and, several hours ago reapy v0.6.0 has been published on PyPi. Under the cut — the last change log, which contains (to my pleasure) no line where I have not been involved one way, or another.

Finally: why reapy is needed and how Python works inside REAPER.

Recently I had to add python3.8 for our Python linter (the strictest one in existence): wemake-python-styleguide. And during this straight-forward (at first look) task, I have found several problems with test coverage that were not solved in Python community at all.

Let's dig into it.

> celery worker --app=demo_app.core --pool=solo --loglevel=INFO