Deep learning API for Python

Small review of Gluon API -  clear, concise, simple yet powerful and efficient API for deep learning
16 October 2017   964

The Gluon API specification is an effort to improve speed, flexibility, and accessibility of deep learning technology for all developers, regardless of their deep learning framework of choice. The Gluon API offers a flexible interface that simplifies the process of prototyping, building, and training deep learning models without sacrificing training speed. 

Check this official screencast tutorials on Gluon on different IDEs.

 

Main advantages:

  • Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
  • Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
  • Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.
  • High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides.

Learn more at GitHub

NGINX to Release Unit 1.3 Beta

Developers expanded the ability to run web applications in Python, PHP, Perl, Ruby and Go
16 July 2018   104

In open access, a beta version of the NGINX Unit 1.3 application server was released. Developers continued to expand the ability to run web applications in Python, PHP, Perl, Ruby and Go. The project code is written in C and is distributed under the Apache 2.0 license.

Features

Version 1.3 eliminates the problems with handling errors when installing HTTP connections.

Among other changes:

  • parameter max_body_size to limit the size of the body of the request;
  • new parameters for setting timeouts when setting up an HTTP connection:
         "settings": {
              "http": {
                  "header_read_timeout": 30,
                  "body_read_timeout": 30,
                  "send_timeout": 30,
                  "idle_timeout": 180,
                  "max_body_size": 8388608
              }
          },
  • automatic use of the Bundler where possible in the Ruby module;
  • http.Flusher interface in the module for the Go language;
  • The possibility of using characters in the UTF-8 encoding in the request headers.

The first version of the NGINX 1.1 application server was released in mid-April 2018. Under the control of NGINX Unit, several applications can be executed simultaneously in different programming languages, the startup parameters of which can be changed dynamically without the need to edit the configuration files and restart.