Canonical to Represent Minimal Ubuntu

New version of Ubuntu is created for servers, isolated containers based on Docker and cloud systems
12 July 2018   247

Ubuntu team presented a simplified version of the base image - Minimal Ubuntu. It is designed for servers, isolated containers based on Docker and cloud systems. The release features high performance, minimal load time and automation of applications in the cloud.

The small footprint of Minimal Ubuntu, when deployed with fast VM provisioning from GCE, helps deliver drastically improved boot times, making them a great choice for developers looking to build their applications on Google Cloud Platform.
 

Paul Nash

Group Product Manager, Google Cloud

The authors of the project emphasize the size of the distribution kit, which "weighs" 157 MB, and also supports the main cloud systems like Amazon EC2, Google Compute Engine (GCE), LXD and KVM / OpenStack, each of which has its own optimized version of the package. In addition, the OS-based image for operating with containers based on the Docker platform, compatible with the Kubernetes.

Minimal Ubuntu is designed for automated execution, so it includes only a minimal set of tools. The distribution can be upgraded to a set of Ubuntu Server packages using the special utility "unminimize", which returns components that are convenient for interactive management.

According to Canonical representatives, the deletion of the manual control functions resulted in the acceleration of the load time by 40% and the reduction of the occupied disk space by 50%. At the same time, this release remained completely compatible with all the packages from standard Ubuntu repositories. Required packages can be installed using the standard package manager apt or using snapd, which are included in the distribution by default.

Two assemblies are available for download, based on Ubuntu 16.04 LTS and 18.04 LTS. You can download them on the official website.

Students to Beat Google’s Machine-Learning Code

Student programmers' image classification algorithm successfully identifies the object in 93% of cases
13 August 2018   365

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.