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IBM/Red Hat/Fedora Leftovers

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Red Hat
  • 10 tips for machine learning experiment tracking and reproducibility: Do it yourself approach without additional tooling – IBM Developer

    As machine learning practitioners, we invest significant time and effort to improve our models. You usually do it iteratively and experimentally by repeatedly changing your model, running an experiment, and examining the results, then deciding whether the recent model change was positive and should be kept or discarded.

    Changes in each iteration might involve, for example, changing a value for a hyperparameter, adding a new input feature, changing the underlying machine learning model (for example, by using gradient boosting classification instead of random forest classification), trying a new heuristic, or trying an entirely new approach.

    Experimentation cycles can cause a great deal of confusion. It’s easy to get lost, forgetting what changes you made in the recent experiments and whether the latest results are indeed better than before. A single experiment can take hours or even longer to complete. So, you try to optimize your time and execute multiple experiments simultaneously. This makes it even less manageable, and the confusion gets even worse.

    In this blog, I share lessons and good practices that I learned in my recent machine learning projects. Although I call it a “Do it yourself” approach, some might call it “The caveman way.” I am fully aware that nowadays there are many experiment tracking and management platforms, but it is not always possible or convenient to use them. Some platforms require that you execute your experiments on their platform. Sometimes you can’t share sensitive information outside of your organization, not just the data sets but also results and code. Many platforms require a paid subscription, which can also be a problem in some cases. Sometimes you just want full control of your experiment management approach and data.

    The following practices are easy to implement and do not require additional tooling. They are mostly suitable for small to medium machine learning projects with a single researcher or a small team. Most of the artifacts are saved locally, and adaptations might be required if you want to use a shared storage. As a seasoned developer of production systems, I’m aware that a few of the tips might be considered ‘code-smells’ or bad practices when it comes to traditional development of such systems. However, I believe that they have their place and are justified for short-term research projects. I would like to emphasize that the tips reflect my personal journey and point of view, and not necessarily any official views or practices.

  • Goodbye, CentOS … and welcome back, CentOS 9 Stream -

    The last couple of years have been messy for the CentOS project and, coinciding with the final stages of this couple of years, there have been announcements that have shaped the new reality of it, whose outcome is fulfilled, following tradition, when we are ending this year. That’s why we say goodbye to CentOS, as we welcome you to CentOS 9 Stream All in a somewhat figurative way, it should be added.

    Recomposing the facts for those who are not up to date, in September 2009 Red Hat announced two releases of its community distribution: CentOS 8, which as always up to that point had been built directly from the source code of Red Hat Enterprise Linux (RHEL); and CentOS Stream, a new variant in format rolling release It was never quite clear how it would fit into the project org chart, given the nature of CentOS, whose pillars have always revolved around stability, long-term support, and a professional solution approach.

  • How we use Linux Test Project to test and improve Linux | Enable Sysadmin

    The Linux Test Project (LTP) is a general-purpose, integrated test suite designed to help organizations using and developing Linux better understand what things work and what still needs work. It is comprised of regression and conformance tests designed to confirm the behavior of the Linux kernel and glibc. Its tools and test suites aim to verify the Linux kernel and related subsystems.

    In short, the Linux Test Project (LTP) is aimed at testing and improving Linux. Its goal is to deliver a suite of automated testing tools for Linux and publish the results of the tests they run. For example, we use LTP tests on Red Hat Enterprise Linux (RHEL) to improve the Linux kernel and system libraries.

  • DevSecOps jobs: 3 ways to get hired | The Enterprisers Project

    In the specialty of DevSecOps, demand for talent has outpaced supply. Many organizations have realized that the traditional siloed development structure is no longer adequate for maintaining application security in light of the ever-increasing pace of software development and delivery. To remedy this problem, many have started shifting security left – having developers run tests and fix security issues in their code.

    As a result, DevSecOps, a function tasked with continuous AppSec testing throughout the DevOps pipeline, has become essential. However, this is a tough field to break into, and figuring out the right path can be challenging. With such a huge demand in the industry for DevSecOps expertise, those who are looking for new opportunities should understand the skills and qualifications needed for this emerging role.

  • Get started with Gradle plugins for Eclipse JKube

    Eclipse JKube is a collection of plugins and libraries to help Java developers containerize and deploy their applications. At the end of the Summer of 2020, Eclipse JKube published its first stable release (see the article, Cloud-native Java applications made easy: Eclipse JKube 1.0.0 now available ). The Eclipse JKube team has just released Eclipse JKube v1.5.1, which includes Gradle plugins for Kubernetes and Red Hat OpenShift.

    This article introduces the new Gradle plugins in Eclipse JKube. You will learn how to build a Java application into a container image and deploy it onto either vanilla Kubernetes or an OpenShift cluster using Gradle.

  • Fedora Community Blog: CPE Weekly Update – Week of December 6th – 10th

    This is a weekly report from the CPE (Community Platform Engineering) Team. If you have any questions or feedback, please respond to this report or contact us on #redhat-cpe channel on (

  • Printf-style debugging using GDB, Part 3

    Welcome back to this series about using the GNU debugger (GDB) to print information in a way that is similar to using print statements in your code. The first article introduced you to using GDB for printf-style debugging, and the second article showed how to save commands and output. This final article demonstrates the power of GDB to interact with C and C++ functions and automate GDB behavior.

  • How Node.js uses the V8 JavaScript engine to run your code | Red Hat Developer

    Ever wondered how your JavaScript code runs seamlessly across different platforms? From your laptop to your smartphone to a server in the cloud, the Node.js runtime ensures that your code is executed flawlessly regardless of the underlying architecture. What’s the magic that makes that possible? It’s the V8 JavaScript engine.

    This article discusses how our team enhanced V8 to handle certain platform differences, notably big-endian versus little-endian byte order.

More in Tux Machines

digiKam 7.7.0 is released

After three months of active maintenance and another bug triage, the digiKam team is proud to present version 7.7.0 of its open source digital photo manager. See below the list of most important features coming with this release. Read more

Dilution and Misuse of the "Linux" Brand

Samsung, Red Hat to Work on Linux Drivers for Future Tech

The metaverse is expected to uproot system design as we know it, and Samsung is one of many hardware vendors re-imagining data center infrastructure in preparation for a parallel 3D world. Samsung is working on new memory technologies that provide faster bandwidth inside hardware for data to travel between CPUs, storage and other computing resources. The company also announced it was partnering with Red Hat to ensure these technologies have Linux compatibility. Read more

today's howtos

  • How to install go1.19beta on Ubuntu 22.04 – NextGenTips

    In this tutorial, we are going to explore how to install go on Ubuntu 22.04 Golang is an open-source programming language that is easy to learn and use. It is built-in concurrency and has a robust standard library. It is reliable, builds fast, and efficient software that scales fast. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel-type systems enable flexible and modular program constructions. Go compiles quickly to machine code and has the convenience of garbage collection and the power of run-time reflection. In this guide, we are going to learn how to install golang 1.19beta on Ubuntu 22.04. Go 1.19beta1 is not yet released. There is so much work in progress with all the documentation.

  • molecule test: failed to connect to bus in systemd container - openQA bites

    Ansible Molecule is a project to help you test your ansible roles. I’m using molecule for automatically testing the ansible roles of geekoops.

  • How To Install MongoDB on AlmaLinux 9 - idroot

    In this tutorial, we will show you how to install MongoDB on AlmaLinux 9. For those of you who didn’t know, MongoDB is a high-performance, highly scalable document-oriented NoSQL database. Unlike in SQL databases where data is stored in rows and columns inside tables, in MongoDB, data is structured in JSON-like format inside records which are referred to as documents. The open-source attribute of MongoDB as a database software makes it an ideal candidate for almost any database-related project. This article assumes you have at least basic knowledge of Linux, know how to use the shell, and most importantly, you host your site on your own VPS. The installation is quite simple and assumes you are running in the root account, if not you may need to add ‘sudo‘ to the commands to get root privileges. I will show you the step-by-step installation of the MongoDB NoSQL database on AlmaLinux 9. You can follow the same instructions for CentOS and Rocky Linux.

  • An introduction (and how-to) to Plugin Loader for the Steam Deck. - Invidious
  • Self-host a Ghost Blog With Traefik

    Ghost is a very popular open-source content management system. Started as an alternative to WordPress and it went on to become an alternative to Substack by focusing on membership and newsletter. The creators of Ghost offer managed Pro hosting but it may not fit everyone's budget. Alternatively, you can self-host it on your own cloud servers. On Linux handbook, we already have a guide on deploying Ghost with Docker in a reverse proxy setup. Instead of Ngnix reverse proxy, you can also use another software called Traefik with Docker. It is a popular open-source cloud-native application proxy, API Gateway, Edge-router, and more. I use Traefik to secure my websites using an SSL certificate obtained from Let's Encrypt. Once deployed, Traefik can automatically manage your certificates and their renewals. In this tutorial, I'll share the necessary steps for deploying a Ghost blog with Docker and Traefik.