How Open Data Hub learners become the teachers

Posted on: September 24, 2019

Brian Proffitt

The prospect of true machine learning is a tangible goal for data scientists and researchers. It has been long known that the platform on which such ML apps can run have to be fast and hyper efficient so that learning can be that much faster. This is the motivation for Red Hat engineers in the Office of the CTO who are working to optimize such an open source platform: Open Data Hub.

Open Data Hub is built on Red Hat OpenShift Container Platform, Ceph Object Storage, and Apache Kafka/Strimzi integrated into a collection of open source projects to enable a machine-learning-as-a-service platform. That’s a lot of components to be integrated, and to ensure that their contributions to Open Data Hub perform well, Red Hat engineers have taken the step of creating an Internal Data Hub within Red Hat as a proving ground and learning environment.

Trust No One, Run Everywhere–Introducing Enarx

Posted on: August 16, 2019

Mike Bursell, Red Hat

When you run a workload as a VM, container or in a serverless environment, that workload is vulnerable to interference by any person or software with hypervisor, root or kernel access. Enarx, a new open source project, aims to make it simple to deploy workloads to a variety of trusted execution environments (TEEs) in the public cloud, on your premises or elsewhere, and to ensure that your application workload is as secure as possible.

When you run your workloads in the cloud, there are no technical barriers to prevent the cloud providers–or their employees–from looking into your workloads, peeking into the data, or even changing the running process. That’s because when you run a workload as a VM, container or serverless, the way that these are implemented means that a person or software entity with sufficient access can interfere with any process running on that machine.

Machine Learning with Open Source Infrastructure

Posted on: August 9, 2019

Brian Proffitt

As machine learning becomes more interesting to technology companies, it is hardly surprising that a company like Red Hat is going to approach the challenges of this aspect of artificial intelligence with an open source methodology in mind.

The immediate benefits to open source machine learning tools are plain as day to anyone familiar with how open source works: lower cost, more flexibility, no vendor lock-in… you know, the usual.

But dig a little deeper and it quickly becomes apparent that open source means more for cutting-edge software than just a faster way to get cheaper software.

Exploring Unsupervised Deep Learning

Posted on: August 1, 2019

Brian Proffitt

The concept of artificial intelligence, which seemed so much like science fiction a few decades ago, has made real, practical inroads in producing results that organizations can find useful. What’s making those results happen, though, isn’t esoteric pie-in-the-sky theory: it’s creating statistical models that have been trained to make decisions. And trained a lot.

Artificial intelligence itself is a term that, for now, has had less of a focus than the more results-oriented machine learning, where a computer system is given input and output data and then is directed to infer the mathematical rules that govern the transformation of that data.

“It’s like pointing a program to look at the solar system and then have it figure out the laws of motion that govern a planetary system,” explained Sanjay Arora.

Understanding and Applying Storage Federation Patterns Using KubeFed

Posted on: July 3, 2019July 3, 2019

Erin Boyd

As a cloud user, how do you avoid the pull of data gravity of one provider or another? How can you get the flexibility and tooling to migrate your infrastructure and applications as your needs change? How do you get to the future of storage federation as data agility?

In this blog we cover the primary motivations and considerations that drive the enablement of flexible, scalable, and agile data management. Our subsequent blogs cover practical use cases and concrete solutions for our 6 federated storage patterns.

All of this is grounded in Red Hat’s work to take a lead in multi-cluster enablement and hybrid cloud capabilities. Our work is focused on leading and moving forward projects that lay the groundwork for this vision in OpenShift, driven via the Kubernetes project KubeFed.

Keylime: Using TPM to Secure Your Slice of the Cloud

Posted on: June 25, 2019

Andrew Toth

Luke Hinds

As people move workloads to shared and public cloud environments, what methods are available to attest their environment has not been tampered with? Is there a good way to use a standardized cryptographic module to do remote attestation, trusted system boot, and so on?

In this post we’ll introduce the Keylime project in some detail, and save a technology demo for a following hands-on article.

Keylime is an open source community-based project endeavoring to be the go-to technology for establishing and maintaining trusted infrastructure in distributed system deployments via two technologies: the use of embedded Trusted Platform Module (TPM) hardware (version 2 and later); and the Linux kernel subsystem – Integrity Measurement Architecture (IMA).

Building a Scalable TensorFlow Twitter Bot for Red Hat Summit

Posted on: June 4, 2019

Anand Sanmukhani, Red Hat

Red Hat’s AI Center of Excellence and PerceptiLabs wanted a way to demonstrate a TensorFlow model to the public during the 2019 Red Hat Summit. The plan was for this model to take images as input, and then respond with the likelihood of a Red Hat fedora being in that image. Here’s what we learned during Red Hat Summit.

This application, which we called Fedora Finder Bot, would be featured during Red Hat CTO Chris Wright’s keynote, where PerceptiLabs demoed their AI platform.

Our initial solution for this objective would be a Twitter bot that receives tweets or direct messages and replies with the output from the TensorFlow model. Twitter being a public service, we felt it could make the model available to a large number of users, so that any user could just tweet to the bot with a picture and the bot would respond with the model’s output.

Rook Changes the Kubernetes Storage Landscape

Posted on: April 15, 2019April 15, 2019

Brian Proffitt

It’s no secret that if you want to run containerized applications in a distributed way, then Kubernetes is the platform for you. Kubernetes’ role as an orchestration platform for containers has taken center stage to become a main player for automating deployment, scaling, and management of applications within containers. Red Hat’s own OpenShift Container Platform is a Kubernetes distribution that uses Kubernetes optimized for enterprises.

Storage has been one of the areas of potential optimization. Many containers, by their very nature, are usually small enough to be easily distributed and managed. Containers hold applications, but the data those applications use needs to be held somewhere else, for a number of reasons. Of particular interest in this post, we want to avoid the containers themselves becoming too large and unwieldy to be effectively managed.

Consumption is Fractal: Open Source Sustainability

Posted on: April 12, 2019

Brian Proffitt

One of the more obscure terms one might hear bandied about in the free and open source software ecosystem is the so-called “bus factor.” The somewhat-informal term refers to the state of a given project based on its sustainability.

Specifically, bus factor is shorthand for the question: what would happen to your open source project if one of your community members were hit by a bus? Would the project survive? Or is so much workflow and institutional knowledge wrapped up in that one person that your project would be damaged, possibly to the point of no recovery?

Anomaly Detection on OpenStack Logs Using Machine Learning

Posted on: March 29, 2019

Karsten Wade

(There’s a great new conference in the U.S., DevConf.US, returning in 2019 to Boston University (15 to 17 Aug). This highly-technical conference is interested in drawing a diverse group of speakers and attendees, with a specific emphasis on people who are new to speaking and tech conferences in general. Only in its second year, DevConf.US builds on the successful decade-spanning run of DevConf.CZ in Brno, CZ.

This is a session from DevConf.US 2018. The call for proposals to present at DevConf.US 2019 is now open.)

In this session from the CentOS Dojo held as part of DevConf.US, OpenStack technical support engineers Madhur Gupta and Shatadru Bandyopadhyay talk about how to use machine learning for anomaly detection on OpenStack logs. Once an anomaly is detected in the logs, it can be used to automate further action, while helping in root cause analysis.

The challenge with anomaly detection in OpenStack in the first place is that it generates a significant quantity of logs, even in relatively simple production setups. How do you ingest and detect anomalies in all that data?

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