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   263

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.

Students to Beat Google’s Machine-Learning Code

Student programmers' image classification algorithm successfully identifies the object in 93% of cases
13 August 2018   382

Developers-students from Fast.ai which organize free online computer training courses have created an image classification algorithm that successfully identifies the object in 93% of cases and copes with it faster than a similar Google algorithm with a similar configuration. The authors argue that "the creation of breakthrough technologies is not just for big companies". This is reported by MIT Technology Review.

When evaluating performance, the DAWNBench test was used, which calculates the speed and cost of teaching the neural network. During the Fast.ai experiment, the neural network was launched on 16 virtual AWS nodes, each contained 8 NVIDIA V100 graphics cards. This configuration achieved accuracy of 93% in 18 minutes, and the cost of machine time was estimated at $ 40. The result of Fast.ai is faster than the development of Google engineers by 40%, but the corporation uses its own clusters TPU Pod, so the comparison is not entirely objective.

The developers used the PyTorch Python library, as well as their own development - fastai. They were able to achieve this learning speed with the new method of cropping images from the ImageNet dataset: instead of square pictures, they began to use rectangular:

Fast AI
Fast AI

State-of-the-art results are not the exclusive domain of big companies. These are the obvious, dumb things that many researchers wouldn’t even think to do.
 

Jeremy Howard

Founder, Fast.AI

The authors tried to make the project accessible to everyone, so they simplified its infrastructure, refusing to use distributed computing systems and containers. To implement it, developers teamed up with engineers from the innovative division of the Pentagon (DIU) to release software to quickly create and support distributed models on AWS.