Deprecated warning: PyTorch 1.0+ uses to Aten as its tensor library. So the article is no longer applicable in PyTorch 1.0+. Please refer to this GitHub repository for more information.
Python is one of the most popular programming languages today for deep learning applications. However, as an interpreted language, it has been considered too slow for high-performance computing. Therefore, I incorporated CUDA into Python to build my own extension that accelerates the computation.
In this post, we described how to use JUnit 5 to test your Spring MVC application with Maven.
Setup Maven Dependencies We can get the required dependencies by declaring the junit-jupiter-api (version 5.x) dependency in our pom.xml file. This dependency provides the public API for writing tests and extensions.
<dependency> <groupId>org.junit.jupiter</groupId> <artifactId>junit-jupiter-engine</artifactId> <version>5.1.0</version> <scope>test</scope> </dependency> If we are using an IDE that is bundled with an older JUnit 5 version, it throws an exception when we try to run our unit tests by using our IDE.
I’m reading source code written with Torch these days. Torch is a well-known deep learning framework written by Lua.
So I summarize the grammar of it and provide a quick tutorial here.
Run As we know, Lua is a C-like language. Therefore, it is case-sensitive.
The following code outputs “Hello World” with Lua. Note that the semicolon at the end of a line is optional, like JavaScript.
print('Hello World') You can use the interrupter of Lua in the command line:
Tornado is one of the most popular web frameworks for Python, which is based on a single thread IO loop (aka event loop). You can handle high concurrency with optimal performance. However, Tornado is single-threaded (in its common usage, although it supports multiple threads in advanced configurations), therefore any “blocking” task will block the whole server. This means that a blocking task will not allow the framework to pick the next task waiting to be processed.
This tutorial introduces how to install QQ/TIM in Linux with Wine, which had been tested on ArchLinux with Wine 2.4.
Prerequisites Before starting, you need to get the latest Wine. I’m not sure whether QQ/TIM can run on a lower version of Wine. In ArchLinux, you can easily get the latest Wine using the following command:
pacman -S wine However, in Debian, you need to install Wine with some more steps.
In this tutorial, you will be setting up a numerical Python development environment for Windows 10. As you might have already realized, Python is rather simple to set up on a Linux/macOS box but as with many open source-based projects getting up and running on Windows is never trivial.
Good solutions for Windows are Enthought Canopy{.reference.external}, Anaconda{.reference.external} (which both provide binary installers for Windows, OS X, and Linux), and Python (x, y).
This tutorial is designed to help a new CAS user to set up the Apereo CAS server and client to their applications. The code of this tutorial is open-sourced on GitLab.
What’s CAS? Enterprise Single Sign-On - CAS provides a friendly open source community that actively supports and contributes to the project. While the project is rooted in higher-ed open source, it has grown to an international audience spanning Fortune 500 companies and small special-purpose installations.
Last week, I took part in the Google Developer Day held in Beijing. The Angular team introduced their new Angular 2. Angular is a development platform for building mobile and desktop web applications.
This tutorial shows how to configure and use Angular 2 web components with the Electron framework for creating native cross-platform applications with web technologies.
As recommended by the Angular team, TypeScript will be used throughout this tutorial. TypeScript is a typed superset of JavaScript that compiles to plain JavaScript.