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Wt (pronounced as witty) is a C++ library for developing web applications. The API is widget-centric and uses well-tested patterns of desktop GUI development tailored to the web. To the developer, it offers abstraction of web-specific implementation details, including client-server protocols, event handling, graphics support, graceful degradation (or progressive enhancement), and URL handling.
The library comes with an application server that acts as a stand-alone Http(s)/WebSocket server or integrates through FastCGI with other web servers.
Client-side and client/server AJAX applets, games, web widgets, plugins based on the Google Web Toolkit for use in your websites, blogs, CMS systems, and for display in Internet Explorer, Mozilla, Firefox, Safari, Opera.
Secure Socket Funneling (SSF) is a network tool and toolkit. It provides simple and efficient ways to forward data from multiple sockets (TCP or UDP) through a single secure TLS link to a remote computer. SSF also allows dynamic port forwarding with a SOCKS server from the client to the server and from the server to the client.
Gizmo Microservice Toolkit toolkit provides packages to put together server and pubsub daemons with the following features like Standardized configuration and logging, Health check endpoints with configurable strategies, Configuration for managing pprof endpoints and log levels, Structured logging containing basic request information, Useful metrics for endpoints, Graceful shutdowns, Basic interfaces to define our expectations and vocabulary and lot more.
A toolkit for centralize the usage of WP7 push notification from any type of applications... it includes server project with implementation in php, mysql, mssql, soap and restful webservices and also a client lib for using it out of the box with the desired server :)
The Validator Toolkit provides a set of validators for the ASP.NET MVC framework to validate HTML forms on the client and server-side using validation sets. By defining a validation set, e.g. a LoginValidationSet class, the toolkit will generate code.
XUP (Extensible User Interface Protocol) is a SOAP-based protocol for delivering events and incremental UI updates. This project builds a web application development framework on top of XUP, consisting of a client, a server toolkit, and a template viewer.
Deploying machine learning models can often be tricky due to their numerous dependencies, deep learning models often even more so. One of the ways to overcome this is to use Docker containers. Unfortunately, it is rarely straight-forward. In this tutorial, we will demonstrate how to deploy a pre-trained deep learning model using Azure Container Services, which allows us to orchestrate a number of containers using DC/OS. By using Azure Container Services, we can ensure that it is performant, scalable and flexible enough to accommodate any deep learning framework. The Docker image we will be deploying can be found here. It contains a simple Flask web application with Nginx web server. The deep learning framework we will use is the Microsoft Cognitive Toolkit (CNTK) and we will be using a pre-trained model; specifically the ResNet 152 model.Azure Container Services enables you to configure, construct and manage a cluster of virtual machines pre-configured to run containerized applications. Once the cluster is set up you can use a number of open-source scheduling and orchestration tools, such as Kubernetes and DC/OS. This is ideal for machine learning application since we can use Docker containers which enable us to have ultimate flexibility in the libraries we use and allows us to easily scale up based on demand. While always ensuring that our application remains performant. You can create an ACS through the Azure portal but in this tutorial we will be constructing it using the Azure CLI.