Python plugin for Visual Studio released

What features Python Tools for Visual Studio plugin brings for a coder? 
01 August 2017   2306

Multi-paradigm programming language with easy-to-use syntax and many features including the support of the object-oriented and structured programming.

Visual Studio is one of the most popular integrated development environment, created by the Microsoft. This product allow you to develop both console applications and GUI applications, including those that support Windows Forms technology, as well as Web sites, web applications, web services in both native and managed codes for all platforms, that are supported by Windows, Windows Mobile, Windows CE,. NET Framework, Xbox, Windows Phone. NET Compact Framework and Silverlight.

Visual Studio supports these programming languages:

  • Basic
  • C#
  • C++
  • F++
  • JScript

But now, Python support also can be included by a simple plugin.

Python Tools for Visual Studio is a free, open source plugin that turns Visual Studio into a Python IDE.


A high-level, free and open source Python Web framework that encourages rapid and clean development with  pragmatic design.


  • CPython,
  • IronPython,
  • editing,
  • browsing,
  • IntelliSense,
  • mixed Python/C++ debugging,
  • remote Linux/MacOS debugging,
  • profiling, IPython, 
  • web development with Django and other frameworks.

From the Visual Studio 2017 installer, select the Python or Data Science workload to add Python support to Visual Studio.

If you will face any issues, don't hesitate to contact developers via GitHub issue. Also, don't forget to check documentation

Designed, developed, and supported by Microsoft and the community.

Nvidia to Open SPADE Source Code

SPADE machine learning system creates realistic landscapes based on rough human sketches
15 April 2019   672

NVIDIA has released the source code for the SPADE machine learning system (GauGAN), which allows for the synthesis of realistic landscapes based on rough sketches, as well as training models associated with the project. The system was demonstrated in March at the GTC 2019 conference, but the code was published only yesterday. The developments are open under the non-free license CC BY-NC-SA 4.0 (Creative Commons Attribution-NonCommercial-ShareAlike 4.0), allowing use only for non-commercial purposes. The code is written in Python using the PyTorch framework.

Sketches are drawn up in the form of a segmented map that determines the placement of exemplary objects on the scene. The nature of the generated objects is set using color labels. For example, a blue fill turns into sky, blue into water, dark green into trees, light green into grass, light brown into stones, dark brown into mountains, gray into snow, a brown line into a road, and a blue line into the river. Additionally, based on the choice of reference images, the overall style of the composition and the time of day are determined. The proposed tool for creating virtual worlds can be useful to a wide range of specialists, from architects and urban planners to game developers and landscape designers.

Objects are synthesized by a generative-adversarial neural network (GAN), which, based on a schematic segmented map, creates realistic images by borrowing parts from a model previously trained on several million photographs. In contrast to the previously developed systems of image synthesis, the proposed method is based on the use of adaptive spatial transformation followed by transformation based on machine learning. Processing a segmented map instead of semantic markup allows you to achieve an exact match of the result and control the style.

To achieve realism, two competing neural networks are used: the generator and the discriminator (Discriminator). The generator generates images based on mixing elements of real photos, and the discriminator identifies possible deviations from real images. As a result, a feedback is formed, on the basis of which the generator begins to assemble more and more qualitative samples, until the discriminator ceases to distinguish them from the real ones.