This blog uses a simple web service as an example to show you how to setup your first GoCD continuous delivery pipeline and manage your build version in it. Check out the blog - https://goo.gl/U4bnKk
Last year we discovered an extensive dataset on the subject of traffic on German roads provided by the BASt. It holds detailed numbers of cars, trucks and other vehicle groups passing more than 1,500 automatic counting stations. The amazing thing about this dataset is that the records for each counting station are provided on an hourly basis and they reach back to the year 2003. As an attempt to get to know the structure and to find a good way for dealing with the massive size of the dataset, we set up some Jupyter (formerly IPython) Notebooks.
I’m working on a project called BadgeYay. It is a badge generator with a simple web UI to add data and generate printable badges in PDF. BadgeYay's back-end is now shifted to REST-APIs and to test functions used in REST-APIs, we need some testing technology that will test each and every function used in the API. For our purposes, we chose the popular unit tests Python test suite. In this blog post, I’ll be discussing how I have written unit tests to test BadgeYay's REST-API.
I want to write and deploy the simplest function possible on AWS Lambda, written in Python, using Terraform.
You already know the longer it takes to detect a problem, the more expensive it is to resolve. Your testing needs to happen earlier in the development pipeline while taking into account all aspects of privacy, security and monitoring. Read the 4-part eBook to learn how to detect problems earlier in your DevOps testing processes by:
In this video we'll cover how to create a bot for Discord. This bot will be able to join a server and show up in the user list. It will be able to interact in chat rooms and private messages and respond to custom commands.
Learn how to use Redis and Python to build location-aware applications.
This Monday, February the 12th, we launched a public beta of Datalore - an intelligent web application for data analysis and visualization in Python, brought to you by JetBrains. This tool turns the data science workflow into a delightful experience with the help of smart coding assistance, incremental computations, and built-in tools for machine learning.
In this article, we will see how KNN can be implemented with Python's Scikit-Learn library. But before that let's first explore the theory behind KNN and see what are some of the pros and cons of the algorithm.
This is the eleventh installment of the Flask Mega-Tutorial series, in which I'm going to tell you how to replace the basic HTML templates with a new set that is based on the Bootstrap user interface framework.
So have you ever needed a reliable External scheduler for your distributed systems? Apache Airflow (by Airbnb) has a good stable scheduler.
So how can we use Airflow for this purpose, here’s how we did.
A native Linux Chromecast GUI that supports transcoding and subtitles.
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"
Python wrapper of win32 for creating Windows notifications.
The reusable Django application for Telegram authorization (also known as Telegram login).
Django REST framework/React quickstart.
A Django middleware implementing the Referrer-Policy header.
A deep neural network for finding text-independent speaker embedding written in tensorflow and tensorpack.
Mixins for Django Rest Framework Serializer.
Generate puns from English phrases.
Add Disqus to your Jupyter notebook.