Tag Archives: servers

Learn Node.js, Unit 3: A tour of Node.js | Linux.com


Node is often described as “JavaScript on the server”, but that doesn’t quite do it justice. In fact, any description of Node.js I can offer will be unfairly reductionist, so let me start with the one provided by the Node team:

“Node.js is a JavaScript runtime built on Chrome’s V8 JavaScript engine.” (Source)

That’s a fine description, but it kinda needs a picture, doesn’t it? If you look on the Node.js website, you’ll notice there are no high-level diagrams of the Node.js architecture. Yet, if you search for “Node.js architecture diagram” there are approximately 178 billion different diagrams that attempt to paint an overall picture of Node (I’ll refer to Node.js as Node from now on). After looking at a few of them, I just didn’t see one that fit with the way I’ve structured the material in this course, so I came up with this:

Node Architecture

Figure 1. The Node.js architecture stack

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Acumos Project’s 1st Software, Athena, Helps Ease AI Deployment | Software


By Jack M. Germain

Nov 16, 2018 5:00 AM PT

The
LF Deep Learning Foundation on Wednesday announced the availability of the first software from the
Acumos AI Project. Dubbed “Athena,” it supports open source innovation in artificial intelligence, machine learning and deep learning.

This is the first software release from the Acumos AI Project since its launch earlier this year. The goal is to make critical new technologies available to developers and data scientists everywhere.

Acumos is part of a Linux Foundation umbrella organization, the LF Deep Learning Foundation, that supports and sustains open source innovation in artificial intelligence, machine learning and deep learning. Acumos is based in Shanghai.

Acumos AI is a platform and open source framework that makes it easy to build, share and deploy AI apps. Acumos standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment, freeing data scientists and model trainers to focus on their core competencies, and accelerating innovation.

“The Acumos Athena release represents a significant step forward in making AI models more accessible for builders of AI applications and models, along with users and trainers of those models and applications,” said Scott Nicholas, senior director of strategic planning at The Linux Foundation. “This furthers the goal of LF Deep Learning and the Acumos project of accelerating overall AI innovation.”

The challenge with AI is that there are very few apps to use it, noted Jay Srivatsa, CEO of
Future Wealth.

“Acumos was launched to create an AI marketplace, and the release of Athena is a first step in that direction,” he told LinuxInsider.

The Acumos AI Platform

Acumos packages toolkits such as TensorFlow and SciKit Learn, along with models that have a common API that allows them to connect seamlessly. The AI platform allows for easy onboarding and training of models and tools.

The platform supports a variety of popular software languages, including Java, Python, and R. The R language is a free software environment for statistical computing and graphics.

The Acumos AI Platform leverages modern microservices and containers to package and export production-ready AI applications as Docker files. It includes a federated AI Model Marketplace, which is a catalog of community-distributed AI models that can be shared securely.

LF Deep Learning members contribute to the evolution of the platform to ease the onboarding and the deployment of AI models, according to LF Deep Learning Outreach Committee Chair Jamil Chawki. The Acumos AI Marketplace is open and accessible to anyone who wants to download or contribute models and applications.

“Acumos Athena is a significant release because it enables the interoperability of AI, DL and ML models and prevents the lock-in that usually occurs whenever projects are built using disparate configurations, systems and deployment techniques,” explained Rishi Bhargava, cofounder of
Demisto.

It will ease restrictions on AI, DL and ML developers by removing silos and allowing them to build standardized models, chain each other’s models together, and refine them through an out-of-the-box general AI environment, he told LinuxInsider.

“The efficiency of learning models is hugely contingent on the quality and uniqueness of data, the depth and repeatability of feature engineering, and selecting the best model for the task at hand,” Bhargava said. “Athena will free developers of extraneous burdens so they can focus on these core tasks, learn from each other, and eventually deliver better models to businesses and customers.”

Athena Release Highlights

Athena’s design is packed with features to make the software quick and easy to deploy, and to make it easy to share Acumos AI applications.

Athena can be deployed with one-click using Docker or Kubernetes. The software also can deploy models into a public or private cloud infrastructure, or into a Kubernetes environment on users’ own hardware, including servers and virtual machines.

It utilizes a design studio graphical interface that enables chaining together multiple models, data translation tools, filters and output adapters into a full end-to-end solution. Also at play is a security token to allow simple onboarding of models from an external toolkit directly to an Acumos AI repository.

Models easily can be repurposed for different environments and hardware. This is done by decoupling microservices generation from the model onboarding process.

An advanced user portal allows personalization of marketplace view by theme and data on model authorship. This portal also allows users to share models privately or publicly.

“The LF Deep Learning Foundation is focused on building an ecosystem of AI, deep learning and machine learning projects, and today’s announcement represents a significant milestone toward achieving this vision,” said LF Deep Learning Technical Advisory Council Chair Ofer Hermoni of Amdocs.

Unifying Factor

The Acumos release is significant for the advancement of AI, DL and ML innovation, according to Edgar Radjabli, managing partner of
Apis Capital Management.

The AI industry is very fragmented, with virtually no standardization.

“Companies building technology are usually required to write most from scratch or pay for expensive licensed cloud AI solutions,” Radjabli told LinuxInsider. “Acumos can help bring a base (protocol) layer standard to the industry, in the same way that HTTP did for the Internet and Linux itself did for application development.”

LF Deep Learning members are inspired and energized by the progress of the Acumos AI Project, noted Mazin Gilbert, vice president of advanced technology and systems at AT&T and the governing board chair of LF Deep Learning.

“Athena is the next step in harmonizing the AI community, furthering adoption and accelerating innovation,” he said.

Open Source More Suitable

Given the challenges of growing new technologies, open source models are better suited to the development process than those of commercial software firms. Open source base layer software is ideal. It allows greater adoption and interoperability between diverse projects from established players and startups, said Radjabli.

“I believe that Acumos will be used both by other open source projects building second-layer applications, as well as commercial applications,” he said.

Today, the same situation exists in other software development. Open source base layer protocols are used across the industry, both by other open source/nonprofit projects and commercial operations, he explained.

“Athena clearly is geared to an open source environment, given that it already has about 70 or more contributors,” said Future Wealth’s Srivats.

Benefits for Business and Consumers

The benefits to be gained from AI, DL and ML are very significant. Companies across the industry have been making progress in the development of unique applications for AI/DL/MO. More growth in this space will result from Acumos, according to Radjabli.

One example involves a company that uses neural networks for predictive healthcare analytics. This system allows it to diagnose breast cancer with zero percent false negatives simply from patient data correlation analysis. This does not involve any invasive testing or imaging, according to Radjabli.

“The correlation is comprised of over 40 variables, which means it would have never been found through traditional medical research data analysis and was only made possible through the use of convolutional and recurrent neural networks working in combination,” he said.

AI, DL and ML are all geared toward businesses understanding and predicting consumer behavior, added Srivatsa.

“Both will benefit,” he said.

What’s Next for Acumos AI

The developer community for Acumos AI already is working on the next release. The company expects it to be available in mid-2019.

The next release will introduce convenient model training, as well as data extraction pipelines to make models more flexible.

Additionally, the next release will include updates to assist closed-source model developers, such as secure and reliable licensing components to provide execution control and performance feedback across the community.


Jack M. Germain has been an ECT News Network reporter since 2003. His main areas of focus are enterprise IT, Linux and open source technologies. He has written numerous reviews of Linux distros and other open source software.
Email Jack.





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Benchmarking Packet.com’s Bare Metal Intel Xeon / AMD EPYC Cloud


With the tests earlier this week of the 16-way AMD EPYC cloud comparison the real standout of those tests across Amazon EC2, Packet, and SkySilk was Packet’s bare metal cloud. For just $1.00 USD per hour it’s possible to have bare metal access to an AMD EPYC 7401P 24-core / 48-thread server that offers incredible value compared to the other public cloud options for on-demand pricing. That led me to running some more benchmarks of Packet.com’s other bare metal cloud options to see how the Intel Xeon and AMD EPYC options compare.

Packet’s on-demand server options for their “bare metal cloud” offerings range from an Intel Atom C2550 quad-core server with 8GB of RAM at just 7 cents per hour up to a dual Xeon Gold 6120 server with 28 cores at two dollars per hour with 384GB of RAM and 3.2TB of NVMe storage. There are also higher-end instances including NVIDIA GPUs but those are on a dynamic spot pricing basis.

The only AMD EPYC option at this time is their “c2.medium.x86” instance type that is the EPYC 7401P 24-cores / 48-threads with 64GB of RAM and 960GB of storage at $1.00 USD per hour. (Packet also advertises Cavium ThunderX ARM servers though currently there is no availability.)

For seeing how these different low-cost, bare metal access on-demand server options compare I benchmarked the c1.small, s1.large, c2.medium, c1.xlarge, m1.xlarge, and m2.xlarge options as all of their key offerings at this point.

All of these instances were tested with Ubuntu 18.04 LTS x86_64 running the Linux 4.15 kernel and GCC 7.3 compiler. These benchmarks were carried out via the Phoronix Test Suite benchmarking software. Beyond looking at the raw performance, the performance-per-dollar / value was also explored based upon Packet’s current on-demand pricing.



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System76 Announces a Line of US-Made PCs » Linux Magazine


System76, one of the few vendors that sells Linux PCs, is launching a series of computers that the company says is “made in the US.” Although some of the elements within the system are imported, System76 says the Thelio desktop series goes beyond mere assembly and that the system is actually manufactured on American soil.  

“We’ve seen it argued that this isn’t US manufacturing because every part isn’t made in the US. If we sourced every part externally, this would be called “assembled in the US.” That’s not what we’re doing here. We’re transforming raw materials into a final product,” System76 said in a blog post.

There are three members of the Thelio family: entry-level Thelio that comes with Ryzen or Core CPUs, up to 32GB of RAM and is priced at $1099. Thelio Major is powered Threadripper or Core-X CPUs, and can pack up to 128GB of Memory; the base price is $2,299. The biggest member of the family is Thelio Massic that’s powered by dual XEON CPUs, you can get up to 768GB of ECC Memory and up to 86TB of Storage; its priced at $2,899.

System76 has created its own Ubuntu-based OS that runs on their hardware, it’s called Pop!_OS. Building their own OS allowed System76 to optimize the performance.

System76 has designed their own chassis controller and hard drive backplane, called Thelio Io, that moves proprietary functionality from the mainboard to the open source Thelio Io ‘daughterboard’.

“Moving chassis and thermal control to Thelio Io enables far more granular performance optimization. Motherboard data, fan speed, and GPU and OS data are used to coordinate optimal airflow,” claimed System76.

Thelio Massive also includes an open source System76 designed SAS backplane for high performance 2.5” PCIe storage.

System76 has released its own parts of the hardware and software work into open source. Thelio hardware is certified by Open Source Hardware Association (OSHWA) and licensed under the GPL v3 and CC-BY-SA.



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17 Fun Linux Commands to Run in the Terminal | Linux.com


The terminal is a very powerful tool, and it’s probably the most interesting part in Unix. Among the plethora of useful commands and scripts you can use, some seem less practical, if not completely useless. Here are some Bash commands that are fun, and some of them are useful as well.

This command adds some spice to your terminal by adding a cat to your screen which will chase after your (mouse) cursor. Install it by running this script:

Type oneko to display the cat.

linux-fun-commands-oneko

Figlet is a command for those who love to write in ASCII art. It greatly simplifies this task as it automatically transforms any given string. It comes with a bunch of fonts by default at “/usr/share/figlet/fonts/,” and you can of course add your own.

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