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Eduardo Arango Ph.D

Hi all!

Thanks for your interest in my OpenSource efforts and adventures!

I am Eduardo Arango Gutierrez Ph.D (as my GitHub accounts reveals), born in Cali Colombia, now Living in Munich, Germany. Certified Environmental engineer on 2013, and Computer Science Ph.D 2022, I have been an active OpenSource contributor and maintainer since 2015, working to enable computational science for all of us. Since my first involvement in OpenSource I have always been a great believer that by bridging the gap between Cloud Computing and HPC we will open the door to more Scientist to run their mathematical simulations and data analysis in a better way than it is being done today.

Currently a full time maintainer for a couple project under kubernetes-sigs, looking to enable specialized hardware and workloads to kubernetes, so we all can make use of GPU's and fast networks with our beloved containers.



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Carlos Eduardo Arango Gutierrez's Projects

gpu-operator icon gpu-operator

NVIDIA GPU Operator creates/configures/manages GPUs atop Kubernetes

grive icon grive

A Container implementation for the Google Drive Client

grive2 icon grive2

Google Drive client with support for new Drive REST API and partial sync

hypershift icon hypershift

Hyperscale OpenShift - clusters with hosted control planes

imagewolf icon imagewolf

Fast Distribution of Docker Images on Clusters

isc-tutorial icon isc-tutorial

2021 IN PROGRESS -- Getting Started with Containers on HPC

jetski icon jetski

Deployment Automation for OpenShift on Baremetal in Red Hat's Shared Labs

k8s-dra-driver icon k8s-dra-driver

Dynamic Resource Allocation (DRA) for NVIDIA GPUs in Kubernetes

k8s-driver-manager icon k8s-driver-manager

The NVIDIA Driver Manager is a Kubernetes component which assist in seamless upgrades of NVIDIA Driver on each node of the cluster.

k8s.io icon k8s.io

Kubernetes files for various *.k8s.io sites

kcp icon kcp

kcp is a prototype of a Kubernetes API server that is not a Kubernetes cluster - a place to create, update, and maintain Kube-like APIs with controllers above or without clusters.

kops icon kops

Kubernetes Operations (kops) - Production Grade K8s Installation, Upgrades, and Management

kubeflux icon kubeflux

Repository for code and documentation for the KubeFlux project: a joint effort with IBM, Red Hat, and LLNL

kubernetes icon kubernetes

Production-Grade Container Scheduling and Management

kueue icon kueue

Kueue: Kubernetes-native Job Queueing

lascilab-webpage icon lascilab-webpage

PƔgina web del laboratorio de redes y sistemas distribuidos de la Universidad del Valle

libpod icon libpod

libpod is a library used to create container pods. Home of Podman.

libretto icon libretto

Libretto is a Golang library to create Virtual Machines (VMs) on any cloud and Virtual Machine hosting platforms such as AWS, Azure, OpenStack, vSphere, or VirtualBox.

metropolis icon metropolis

Metropolisā€“Hastings algorithm In statistics and in statistical physics, the Metropolisā€“Hastings algorithm is a Markov chain Monte Carlo (MCMC) method for obtaining a sequence of random samples from a probability distribution for which direct sampling is difficult. This sequence can be used to approximate the distribution (e.g., to generate a histogram), or to compute an integral (such as an expected value). Metropolisā€“Hastings and other MCMC algorithms are generally used for sampling from multi-dimensional distributions, especially when the number of dimensions is high. For single-dimensional distributions, other methods are usually available (e.g. adaptive rejection sampling) that can directly return independent samples from the distribution, and are free from the problem of autocorrelated samples that is inherent in MCMC methods.

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