Hands on docker
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inovex Meetup: Let´s talk about docker! Speaker: Johannes Scheuermann, inovex GmbH Karlsruhe, 18.12.2014 Mehr Meetups: http://www.meetup.com/inovex-karlsruhe http://www.meetup.com/inovex-cologne http://www.meetup.com/inovex-munich
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3. The Docker CLI can be used to run containers by pulling images from Docker Hub, a registry for Docker images, and using commands like docker run to launch containers from those images.
How to Dockerize Web Application using Docker Compose
Enhancing the application development process in all its phases—building, scaling, shipping, deploying
and running—plays a vital role in today’s competitive IT industry by shortening the time between writing
code and running it.
Docker for developers
This document provides an overview of Docker for developers. It discusses Docker's capabilities for solving portability issues, its advantages over traditional virtualization through operating system-level virtualization using containers that share the same kernel, and how it addresses challenges like slow development times and inefficient resource usage. It also covers Docker concepts like images, containers, registries, networking, security best practices using tools like Docker Bench Security, and cluster management using Docker Swarm.
Docker In Brief
This document provides information about Docker and how it compares to virtual machines. It defines key Docker concepts like containers, images, and layers. It explains that Docker allows applications to be packaged with all their dependencies and shipped as standardized units called containers that can run on any Linux server that has Docker installed. Containers are more lightweight than virtual machines and provide greater performance and portability. The document also provides examples of how to build Docker images using Dockerfiles and deploy containers.
Develop with docker 2014 aug
This document summarizes Docker concepts and provides steps for a local Docker development setup. It introduces Docker images, containers, and registries. It then outlines requirements for development and production configurations and provides examples of setting up a Node.js/Angular frontend and Django backend using Docker images. The document concludes with notes on continuous integration and architecture options.
Docker - Der Wal in der Kiste
This document provides an agenda and overview for the "Der Wal in der Kiste – Docker 101" presentation at the Admincamp 2017 conference from September 18-21, 2017 in Gelsenkirchen, Germany. The presentation will cover why and how to use Docker, including installing Docker on Linux, Windows, and Raspberry Pi systems, working with Docker images and containers, the Docker registry and hub, using Docker with Domino applications, and Kubernetes. The presenter Ulrich Krause is an experienced IBM Lotus Notes and Domino developer and administrator who created the open source Let's Encrypt for Domino project.
Introduction to Docker
This document discusses options for resource sharing such as dedicated servers, virtual machines, and containers. It focuses on Docker as a lightweight containerization platform. Key points include: Docker uses the host's kernel to run containers without their own operating systems, making them faster to start up than virtual machines; images contain the components of an application and its dependencies to build containers; the Docker CLI is used to pull public images, map resources, and build custom images using Dockerfiles.
Docker Azure Friday OSS March 2017 - Developing and deploying Java & Linux on...
This document provides an overview of developing and deploying Java applications on Azure using Docker. It discusses using Docker to build Java applications, running containers, and deploying stacks. It also covers Docker Enterprise Edition, including subscriptions, certifications, and security features. Finally, it demonstrates using Docker on Azure, such as with Azure Container Service, and shows examples of building, running, and deploying Java applications with Docker.
How to Dockerize your Sitecore module
This document provides an overview of how to convert an existing Sitecore module into a Docker image. It discusses what Docker is, how existing Sitecore modules are installed, and the steps to take a module and prepare the necessary assets and configuration to build it into a Docker image. This includes extracting content and database resources, using tools like Sitecore Courier to generate a DACPAC, adding a Dockerfile and Docker Compose configuration, and building and publishing the final image.
What's New in Docker - February 2017
This document provides an overview of Container as a Service (CaaS) with Docker. It discusses key concepts like Docker containers, images, and orchestration tools. It also covers DevOps practices like continuous delivery that are enabled by Docker. Specific topics covered include Docker networking, volumes, and orchestration with Docker Swarm and compose files. Examples are provided of building and deploying Java applications with Docker, including Spring Boot apps, Java EE apps, and using Docker for builds. Security features of Docker like content trust and scanning are summarized. The document concludes by discussing Docker use cases across different industries and how Docker enables critical transformations around cloud, DevOps, and application modernization.
Revolutionizing the cloud with container virtualization
This document discusses container virtualization and key related technologies. It begins with an overview of virtualization and the hypervisor model. It then covers Linux containers and the kernel features they use like namespaces, cgroups, AppArmor, and SELinux. Popular container tools like LXC, Docker, CoreOS, and Kubernetes are introduced. The document argues that containers make it possible to run multiple isolated environments on one host more efficiently than virtual machines, improving cloud deployment.
Dockerizing a Symfony2 application
Dockerizing Symfony2 application. Why Docker is so cool And what is Docker? And what are Containers? How they works? What are the ecosystem of Docker? And how to dockerize your web application (can be based on Symfony2 framework)?
Docker Container As A Service - Mix-IT 2016
Docker Container As A Service
X11 Linux apps on mac in a container.
In container Java development with STS or Eclipse in a container.
Docker UCP and swarm load balancing with Interlock.
Accelerate your development with Docker
This document provides an overview of Docker and containers. It begins with a brief introduction to 12 Factor Applications methodology and then defines what Docker is, explaining that containers utilize Linux namespaces and cgroups to isolate processes. It describes the Docker software and ecosystem, including images, registries, Docker CLI, Docker Compose, building images with Dockerfile, and orchestrating with tools like Kubernetes. It concludes with a live demo and links to additional resources.
Accelerate your software development with Docker
Docker is in all the news and this talk presents you the technology and shows you how to leverage it to build your applications according to the 12 factor application model.
Docker Deep Dive Understanding Docker Engine Docker for DevOps
Up and running with docker, How docker engine works, Why use Docker as a web developer, How Docker help us achieving consistent environment for developing web app, How Docker swarm help us a DevOps, What are namespace and control groups, How images are different from container, What is content addressable storage, what is Fat mainfest and image manifest, Dot cloud and Docker.
Dev opsec dockerimage_patch_n_lifecyclemanagement_
Lors de cette présentation, nous allons dans un premier temps rappeler la spécificité de docker par rapport à une VM (PID, cgroups, etc) parler du système de layer et de la différence entre images et instances puis nous présenterons succinctement kubernetes.
Ensuite, nous présenterons un processus « standard » de propagation d’une version CI/CD (développement, préproduction, production) à travers les tags docker.
Enfin, nous parlerons des différents composants constituant une application docker (base-image, tooling, librairie, code).
Une fois cette introduction réalisée, nous parlerons du cycle de vie d’une application à travers ses phases de développement, BAU pour mettre en avant que les failles de sécurité en période de développement sont rapidement corrigées par de nouvelles releases, mais pas nécessairement en BAU où les releases sont plus rares. Nous parlerons des diverses solutions (jfrog Xray, clair, …) pour le suivie des automatique des CVE et l’automatisation des mises à jour. Enfin, nous ferons un bref retour d’expérience pour parler des difficultés rencontrées et des propositions d’organisation mises en oeuvre.
Cette présentation bien qu’illustrée par des implémentations techniques est principalement organisationnelle.
Docker 101 Checonf 2016
This document discusses Docker and how it powers the Eclipse Che IDE platform. It provides an overview of Docker concepts like containers, images, and orchestration. It also demonstrates how to build a sample Spring Boot app as a Docker image and run it as a container. Finally, it outlines the agenda for the CheConf2016 conference, including sessions on deploying Che on OpenShift and building an IoT IDE with Che.
Dockerization of Azure Platform
Docker containers have been making inroads into Windows and Azure world. Docker has now replaced the traditional Azure IaaS & PaaS services, offering superior container versions which are more responsive, cost effective, and agile. In this session for Charlotte Azure User Group, we will take an in-depth look at the intersection of Docker and Azure, and how Docker is empowering next gen Azure services.
Here's the link to CAG meetup for the event - https://www.meetup.com/Charlotte-Microsoft-Azure/events/fpftgmyxjbjb/
Docker: A New Way to Turbocharging Your Apps Development
Docker is a platform for developing, shipping, and running applications. It provides containers that package applications and dependencies together allowing them to run seamlessly on any infrastructure. The document discusses Docker concepts like containers, images, and the Docker ecosystem. It also provides examples of using Docker with various applications and frameworks like PHP, Java, .NET, Nginx, and Apache. Managing Docker containers at scale can be done with tools like Kubernetes, Docker Datacenter, Rancher, and Prometheus for monitoring.
Similar to Hands on docker (20)
How to Dockerize Web Application using Docker Compose
How to Dockerize Web Application using Docker Compose
Docker Azure Friday OSS March 2017 - Developing and deploying Java & Linux on...
Docker Azure Friday OSS March 2017 - Developing and deploying Java & Linux on...
Revolutionizing the cloud with container virtualization
Revolutionizing the cloud with container virtualization
Docker Deep Dive Understanding Docker Engine Docker for DevOps
Docker Deep Dive Understanding Docker Engine Docker for DevOps
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Dev opsec dockerimage_patch_n_lifecyclemanagement_
Docker: A New Way to Turbocharging Your Apps Development
Docker: A New Way to Turbocharging Your Apps Development
More from inovex GmbH
lldb – Debugger auf Abwegen
lldb kann mehr als nur einfache Breakpoints oder po. In dem Vortrag zeigt Oliver Bayer, wie sich mit Hilfe von lldb Programmcode zur Ausführungszeit manipulieren lässt, ohne das hierfür der Sourcecode anzupassen ist. Sei es, damit Test- oder Debugcode nicht in die produktiv App gelangt, oder weil der Sourcecode für einen Teil der App nicht vorliegt.
Event: macoun, 04.10.2019
Speaker: Oliver Bayer, inovex
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Are you sure about that?! Uncertainty Quantification in AI
With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order to assess risks and failure.
In my talk, I will give an introduction to different kinds of uncertainty, i.e. epistemic and aleatoric. To have a baseline for comparison, the classical method of Gaussian Processes for regression problems is presented. I then elaborate on different DL methods for uncertainty quantification like Quantile Regression, Monte-Carlo Dropout, and Deep Ensembles. The talk is concluded with a comparison of these techniques to Gaussian Processes and the current state of the art.
Speaker: Dr. Florian Wilhelm, Simon Bachstein, inovex
Event: PyCon/PyData Berlin 2019
Datum: 10.10.2019
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Why natural language is next step in the AI evolution
In 2010 ImageNet finally ended the AI winter and gave machines the sense of sight. Within the following years dramatic improvements in tasks such as image classification and object detection lead to innovations like face ID and autonomous driving. Recently, similar developments happened in the field of natural language. Using Attention mechanism and transformers tasks such as question answering and text summarization reached new benchmarks.
This talk will not only explain those, but point out how Transfer Learning and open source models such as Google Bert will open the field to new innovations in AI.
Speaker: Nico Kreiling, inovex
Event: AIxIA, 01.10.2019
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WWDC 2019 Recap
Die Worldwide Developers Conference (WWDC) ist eine von Apple jährlich durchgeführte Konferenz für Software-Entwickler (MacOS, iOS und WatchOS). Um die WWDC 2019 nochmal Revue passieren zu lassen, wurde beim Mobile Development Karlsruhe Meetup zu einer offenen Diskussionsrunde eingeladen. Die Slides fassen die für inovexler Philipp interessantesten Neuigkeiten der WWDC2019 zusammen und dienten beim Meetup als Diskussionsgrundlage.
Event: 9. Mobile Development Meetup (WWDC Edition)
Speaker: Philipp Wallrich, inovex
Datum: 17.06.2019
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Network Policies
Trust is good, control is better – A short story about Network Policies.
Abstract:
Probably everybody who uses Kubernetes in a productive environment with multiple users possibly has looked at policies. Often the operators of the cluster(s) just trust the policies but in some cases it might be useful to control if the policies actually have taken action and often there are just to many Policies in the cluster setup to manually test them all (and obviously you don’t want to do this). Testing the effectiveness of the Network Policies can be done in different approaches. In this talk we will show you the benefits and drawbacks of different approaches and what solution we finally chose. Also we will show you some other tools and how they complement our solution. As a takeaway you will get an overview of different testing strategies for policies, as well as understanding challenges in testing policies in general and the Kubernetes ecosystem.
Event: ContainerDays 2019
Datum: 26.06.2019
Speaker: Johannes M. Scheuermann, Maximilian Bischoff (beide inovex)
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Interpretable Machine Learning
Interpretierbarkeit von ML-Modellen hat die Zielsetzung, die Ursachen einer Prognose offenzulegen und eine daraus abgeleitete Entscheidung für einen Menschen nachvollziehbar zu erklären. Durch die Nachvollziehbarkeit von Prognosen lässt sich beispielsweise sicherstellen, dass deren Herleitung konsistent zum Domänenwissen eines Experten ist. Auch ein unfairer Bias lässt sich durch die Erklärung aussagekräftiger Beispiele identifizieren.
Prognosemodelle lassen sich grob in intrinsisch interpretierbare Modelle und nicht-interpretierbare (auch Blackbox-) Modelle unterscheiden. Intrinsisch interpretierbare Modelle sind dafür bekannt, dass sie für einen Menschen leicht nachvollziehbar sind. Ein typisches Beispiel für ein solches Modell ist der Entscheidungsbaum, dessen regelbasierter Entscheidungsprozess intuitiv und leicht zugänglich ist. Im Gegensatz dazu gelten Neuronale Netze als Blackbox-Modelle, deren Prognosen durch die komplexe Netzstruktur schwer nachvollziehbar sind.
In diesem Talk erläuterte Marcel Spitzer das Konzept von Interpretierbarkeit im Kontext von Machine Learning und stellte gängige Verfahren zur Interpretation von Modellen vor. Besonderen Fokus legte er dabei auf modellunabhängige Verfahren, die sich auch auf prognosestarke Blackbox-Modelle anwenden lassen.
Event: M3 Minds Mastering Machines
Speaker: Marcel Spitzer
Blog-Artikel: https://www.inovex.de/blog/machine-learning-interpretability/
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Jenkins X – CI/CD in wolkigen Umgebungen
Das Ökosystem rund um Kubernetes wächst täglich. Insbesondere cloud-native Continuous-Deployment-Strategien stehen Hoch im Kurs und werden in diversen Open-Source-Projekten vorangetrieben. In einer Reihe von Evalutionen nimmt inovex diese Tools genauer unter die Lupe - den Anfang macht Jenkins X.
Jenkins X wurde im März 2018 veröffentlicht. Das Konzept hinter dem Tool ist primär, bestehende Teillösungen (Helm, Skaffold, Prow, Tekton) einzusetzen, um sie abstrahiert in ein Kommandozeilen-Interface zu packen. Der Vortrag beschreibt sowohl die klassische Architektur als auch den "Severless"-Ansatz. Des weiteren werden das Kommandozeilen-Tool "jx", der allgemeine Entwicklungs-Workflow sowie diverse Features vorgestellt.
Bei unseren Tests im Rahmen der Evaluation sind uns einige Stolpersteine aufgefallen. Es sind vor allem die vielen eingesetzten Dritt-Tools, die den Betrieb und den Upkeep eines mit Jenkins X erstellten Clusters verkomplizieren. Als Fazit stellen wir Jenkins X im Mai 2019 ein "befriedigend" aus und beobachten gespannt, wie sich das Tool in den kommmenden Monaten und Jahren weiterentwickeln wird.
Event: Talk4Nerds, 29.04.2019
Speaker: Simon Kienzler, Johannes M. Scheuermann (beide inovex)
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AI auf Edge-Geraeten
Neben dem großen Machine-Learning-Trend in der Cloud zeichnet sich zunehmend die Tendenz ab, bestimmte Aufgaben direkt auf Edge-Geräten auszuführen. Wir erkunden die Vorteile von Auswertungen direkt an der Quelle der Daten und die damit verbundenen Herausforderungen. Denn die Rechenleistung der Cloud steht uns hier leider nicht zur Verfügung.
Zur Lösung stehen uns verschiedene Hardwareoptionen wie CPUs, GPUs, FPGAs oder spezielle ASICs und Frameworks zur Verfügung, die wir am Beispiel von einem Convolutional Neural Network evaluieren. Dabei gibt es praktische Tipps und Erfahrungen aus realen Projekten sowie anschauliche Demos auf verschiedenen Hardwareplattformen.
Vorkenntnisse:
Vorkenntnisse über tiefe neuronale Netze sind von Vorteil.
Lernziele:
- Verständnis über die Vorteile von AI auf Edge-Geräten und den damit verbundenen Herausforderungen.
- Wissen über die verschiedenen Hard- und Softwarelösungen erlangen, um diese in eigenen Projekten einzusetzen.
Event: building IoT, 03.04.2019
Speaker: Dominik Helleberg, inovex
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Prometheus on Kubernetes
This document discusses Prometheus on Kubernetes. It provides an overview of Prometheus and its ecosystem, including how it is used for service discovery on Kubernetes, collecting and storing metrics, ensuring high availability and scalability through sharding, and defining and alerting on service level agreements. It also covers instrumentation using exporters, the Prometheus query language PromQL, and components of the Prometheus ecosystem like Grafana and Alertmanager.
Deep Learning for Recommender Systems
Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance.
Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019
Speaker: Marcel Kurovski, inovex GmbH
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Azure IoT Edge
In seinem Meetup Talk berichtete Maximilian von den aktuellen Problemen von Cloud Computing – insbesondere im Internet of Things – und wie diese durch Edge Computing mitigiert werden können. Er erklärte, wie eine generische Edge-Computing-Architektur aussehen kann und zeigte Anwendungsfälle, von denen manche auch schon in existierenden Produkten umgesetzt sind.
Im Anschluss stellte er Azure IoT Edge vor und erläuterte, wie es das bestehende IoT Framework von Microsoft erweitert sowie die Grundkonzepte, die IoT Edge bereitstellt. Auch die Probleme in dem noch jungen Produkt wurden angesprochen, aber auch die Vorteile und Features, die es liefert.
In der gemeinsamen Demo mit Eli haben dann beide Speaker die technischen Details von Azure IoT Edge gezeigt und demonstriert, beispielsweise wie Code automatisiert von einer CI/CD-Pipeline in Azure DevOps auf ein IoT-Gerät deployed werden kann.
Event: inovex Meetup, 12.03.& 19.03.2019
Speaker: Maximilian Bischoff, inovex
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Representation Learning von Zeitreihen
Es liegt in der Natur des Menschen das Unvorhersehbare vorherzusagen: Wetter, Aktienkurse, Krankheitsverläufe, die Reaktion eines Menschen. Neueste Deep Learning Ansätze sind in der Lage solche sequentielle Sachverhalte immer genauer zu prognostizieren, setzen aber auch immer größere Datenmengen und Rechenleistungen voraus, die sowohl in Forschung als auch in der Praxis häufig nicht vorliegen. Wie kann man gute Ergebnisse erreichen, wenn nur wenig Daten vorliegen?
Marisa Mohr stellte in ihrem Vortrag einen neuen und vielversprechenden informationstheoretischen Ansatz zum Feature Learning von sequentiellen Daten vor, der potenziell auch mit wenigen Daten auskommt. Dabei ging es speziell um ordinale Muster in Zeitreihen, wie sie beispielsweise als Veränderung von Emotionen im Gesprächsverlauf zu finden sind. Eine solche Entwicklung ist für Menschen in der Regel leicht zu erkennen. Chatbots hingegen können nicht intuitiv auf solche Emotionsverläufe reagieren, sondern müssen entsprechend programmiert werden.
Details:
Deep-Learning-Ansätze wie LSTMs, RNNs oder TCNs haben sich im Umgang mit sequentiellen Daten bewährt. Neuronale Netzwerke sind tief im technischen Sinn, weil sie mehrere (verborgene) Schichten besitzen, aber nicht weil sie ein tiefes Verständnis von Problemen entwickeln. In diesem Vortrag stellte Marisa einen symbolischen informationstheoretischen Ansatz des Representation Learnings von Zeitreihen vor und damit eine Möglichkeit, konzeptionelle Schichten zu konstruieren. Die Idee hinter der sogenannten Permutationsentropie besteht darin, anstelle der Werte einer Zeitreihe die Ordnungsrelation zwischen den Werten zu betrachten, und so auf das natürliche Auf und Ab des zugrundeliegenden dynamischen Systems zurückzugreifen.
Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019
Speakerin: Marisa Mohr (inovex)
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Talk to me – Chatbots und digitale Assistenten
Menschliche Kommunikation folgt zwar einer ganzen Reihe von Regeln, diese lassen sich aber schwer formalisieren. Nicht zuletzt deshalb, weil in unseren Interaktionen immer auch eine Fülle von Welt- und implizitem Kontextwissen eine Rolle spielt. Rein regelbasierte Chatbots sind daher nicht nur äußert komplex in der Programmierung, sondern stoßen in vielen Anwendungsbereichen schnell an ihre Grenzen.
In diesem Vortrag gab Anna Weißhaar einen Überblick über die aktuellen Lösungen und Herausforderungen im Bereich digitale Assistenten. Der Fokus lag dabei auf Ansätzen, die Chatbots „chatty“ machen, sie also möglichst adäquat auf im Voraus unbekannte Nutzereingaben reagieren zu lassen.
Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019
Speaker: Anna Weißhaar (inovex)
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Künstlich intelligent?
Nicht zuletzt durch die medienwirksame Erfolge des maschinellen Lernens durch DeepMind, OpenAI und Kollegen ist Künstliche Intelligenz im Moment wieder in aller Munde. Einerseits locken zahlreiche neue, vorher undenkbare Anwendungen wie die automatische Diagnose von Krankheiten, autonome Fahrzeuge und Drohnen, oder die automatische Übersetzung gesprochener Wörter. Andererseits warnen mahnenden Stimmen wird vor dem zunehmendem Einflussnahme der „Algorithmen“ auf fast alle Bereiche unseres Lebens sowie vor unerwünschten Folgen von sich verselbstständigenden Computern gewarnt. Einige träumen von – oder fürchten sich vor – der vermeintlich unausweichlichen Singularität, an der sich nichts weniger als das Schicksal der gesamten Menschheit entscheiden wird. Doch was verbirgt sich hinter dem Begriff Künstliche Intelligenz? Je nachdem, wen man fragt, erhält man unterschiedliche, bisweilen gegensätzliche Antworten. Dieser Vortrag stellt einige dieser Antworten vor und versucht sie (nicht nur) anhand von Beispielen aus Forschung und Anwendung einzuordnen.
Event: Business Analytics Day, 07.03.2019
Speaker: Dr. Matthias Richter, Dr. Stefan Igel (inovex)
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Dev + Ops = Go
In den letzten drei Jahren haben wir die Infrastruktur der Fernseh-Plattform waipu.tv gebaut. Dabei haben wir angefangen Tools für den Betrieb in Golang zu schreiben. Aus einigen der Tools wurden Core-Services, die auch die Last einer Fußball-WM-Übertragung locker wegstecken. Wir wollen euch zeigen, wie wir mit der selben Tool-Chain (Golang & Co) Betriebs-Probleme lösen und kritische Business-Applikationen entwickeln. Klassisch DevOps oder Golden Hammer?
Speaker: Christoph Petrausch, Igor Lankin (beide inovex)
Event: DevOpsConference, 04.12.2018
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Das Android Open Source Project
Das Android Open Source Project, kurz AOSP, ist das Betriebssystem, das auf den meisten heutigen und wahrscheinlich auch auf deinem Smartphone läuft. Es ist die Basis für das Android-App-Universum und wird von Millionen Nutzern und Entwicklern auf der Welt verwendet. Wegen der offenen Verfügbarkeit des Source Codes ist es auch die Basis für bekannte Custom ROMs wie LineageOS.
Der erste Teil des Talks gab eine Übersicht über die Architektur des Betriebssystems, das App-Ökosystem, den Hardware Abstraction Layer (HAL), die Sicherheitskonzepte und einige neue Betriebssystementwicklungen wie Project Treble in Android 8.0.
Der zweite Teil des Talks gab einen Einblick in den Quellcode und die Struktur des AOSP: Wie lädt man sich den Source Code herunter, wie baut man das AOSP für unterstützte Geräte und wie kann man die eigenen ROMs auf ein Smartphone flashen? Zum Spaß wurde auch noch in einige Implementierungsdetails von Android-App-API-Funktionen geblickt, die man als App Developer schon aufgerufen hat.
Speaker: Stefan Lengfeld, inovex
Event: inovex Meetup Köln, 23.10.2018
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Machine Learning Interpretability
This document discusses machine learning interpretability. It defines interpretation as giving explanations to humans for machine learning models and decisions. It notes that humans create, are affected by, and demand explanations for decision systems. The document outlines different techniques for model interpretability including intrinsically interpretable models, post-hoc interpretability techniques that provide explanations for black box models, and model-specific and model-agnostic techniques. It provides examples like partial dependence plots, individual conditional expectation, and local surrogate models. It recommends choosing techniques based on the recipient and purpose of explanations.
Performance evaluation of GANs in a semisupervised OCR use case
Online vehicle marketplaces are embracing artificial intelligence to ease the process of selling a vehicle on their platform. The tedious work of copying information from the vehicle registration document into some web form can be automated with the help of smart text-spotting systems, in which the seller takes a picture of the document, and the necessary information is extracted automatically.
Florian Wilhelm details the components of a text-spotting system, including the subtasks of object detection and optical character recognition (OCR). Florian elaborates on the challenges of OCR in documents with various distortions and artifacts, which rule out off-the-shelf products for this task. After offering an overview of semisupervised learning based on generative adversarial networks (GANs), Florian evaluates the performance gains of this method compared to supervised learning. More specifically, for a varying amount of labeled data, he compares the accuracy of a convolution neural network (CNN) to a GANthat uses additional unlabeled data during the training phase, showing that GANs significantly outperform classical CNNs in use cases with a lack of labeled data.
What you'll learn:
Understand how semisupervised learning with GANs works
Explore beneficial semisupervised methods based on GANs for use cases with a limited amount of labeled data
Gain insight into an interesting OCR use case of an online vehicle marketplace
Event: O'Reilly Artificial Intelligence Conference, London, 11.10.2018
Speaker: Dr. Florian Wilhelm
Mehr Tech-Vorträge: www.inovex.de/vortraege
Mehr Tech-Artikel: www.inovex.de/blog
People & Products – Lessons learned from the daily IT madness
IT im 21. Jahrhundert – What a time to be alive! Es gibt einen (unüberschaubaren) Zoo an Methoden und Produkten die uns so viel Freude an der Arbeit bereiten! Sie sind modern, weil sie neu sind. Sie fordern unser Können heraus, weil sie komplex sind. Sie lösen einige Probleme, die wir vorher nicht hatten. Jeder will sie verwenden, weil Google, Netflix & Co. sie propagieren und Hand auf’s Herz: Will nicht jeder gerne so arbeiten wie Google, Netflix & Co.? Aber macht das wirklich Sinn?
In diesem Vortrag blicken wir auf diverse Erkenntnisse aus dem Einsatz agiler Produktentwicklung, DevOps, Continuous Integration/Delivery, Infrastructure as Code, Immutable Infrastructure (bspw. Docker/Kubernetes), Application Logging und Service Monitoring.
Learning Goals:
- Wir müssen den Einsatz von Methoden und Tools an die Menschen ausrichten, die sie (weiter-)entwickeln und benutzen sollen.
- Manchmal lösen wir mit neuen Tools Probleme, die wir vorher nicht hatten.
- Die Suche nach einfachen Lösungen für komplexe Probleme ist essentiell, aber nicht immer einfach.
Event: Continuous Lifecycle, 15.11.2018
Speaker: Arnold Bechtoldt
Mehr Tech-Vorträge: www.inovex.de/vortraege
Mehr Tech-Artikel: www.inovex.de/blog
Infrastructure as (real) Code – Manage your K8s resources with Pulumi
Pulumi (pulumi.io) offers an open source platform to create/manage and deploy your infrastructure in realy programming languages like JavaScript/TypeScript, Go and Python. As Cloud platforms the major 3 cloud providers are supported and additionally you can also use Pulumi with OpenStack and Kubernetes to deploy your applications in the cloud.
In this talk we will take a look how Pulumi is different to traditional solutions like Terraform or the Cloud Provider specific solutions (e.g. CloudFormation). The main focus will be on deploying your services on top of Kubernetes. The talk will contain a little theory part about Pulumi, the rest of the talk is more focused on demos and practical parts. One focus of the talk is the difference of Pulumi to kubectl and helm (or to be precise how they complement each other.
As a takeaway of this talk you should understand the basics of Pulumi and know what are the differences to the traditional deployment tools.
Event: CNCF Meetup Hamburg & Stuttgart, 29.10.2018 & 07.11.2018
Speaker: Johannes M. Scheuermann, inovex
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
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Hands on docker
- 1. Hands On - Docker CoreOS - Docker - Kubernetes Deployment with Foreman Johannes Maximilian Scheuermann Karlsruhe, 18.12.2014
- 2. „Docker (container engine) logo“ von dotCloud, Inc. - http://www.docker.io/static/img/docker-top-logo.png. Lizenziert unter Apache License, Version 2.0 über Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Docker_ (container_engine)_logo.png#mediaviewer/File:Docker_(container_engine)_logo.png Agenda ‣ Architecture ‣ CoreOS ‣ Kubernetes ‣ Panamax ‣ Deployment
- 3. Orchestrator images: Docker Press kit, CoreOS Press kit and https://github. com/GoogleCloudPlatform/kubernetes/blob/master/logo.png Architecture Abstract View Container etcd ClusterWorker Cluster
- 4. Worker Cluster Architecture MiniRZ etcd Cluster e1 e2 e3 w2 w3 API Cluster a1 a2 a3 Overlay network role=etcd role=kube-api role=kube-worker Kubernetes- Client w1 Secured communication via token or TLS
- 6. image: https://coreos.com/assets/images/media/Host-Diagram.png CoreOS ● ChromeOS Fork ● Minimal Linux – uses ~40% less RAM ● Painless update – update OS as single unit ● Docker Container ● Clustered By Default ● Cluster Management with fleet ● Service Discovery with etcd ● Systemd as init-system Linux for Massive Server Deployments
- 7. Image: https://github.com/GoogleCloudPlatform/kubernetes/blob/master/logo.png Kubernetes ● Greek for pilot or helmsman of a ship ● Open Source cluster manager from Google ● Managing containerized applications across a cluster of nodes ● Kubernetes is lean, portable, extensible and self-healing ● Works with etcd ● Has Master and Minion components ● Easy Service deployments, updates and scalability ● Can run basically on every Linux platform Managing Container
- 10. images: http://panamax.io/ Panamax Docker Management for Humans ● Open Source Project to deploy complex containerized Applications ● Build on top of Kubernetes or fleet ● Web-GUI with drag and drop ● Separated in an UI and Remote-Agent
- 11. image: https://raw.githubusercontent.com/theforeman/foreman-graphics/master/logo/foreman_medium.png Foreman Provisioning Tool “Foreman is an open source project that gives system administrators the power to easily automate repetitive tasks, quickly deploy applications, and proactively manage servers, on-premises or in the cloud” ● Discover, provision and upgrade your entire bare-metal infrastructure ● Create and manage instances across private and public clouds ● Group your hosts and manage them in bulk, regardless of location ● Review historical changes for auditing or troubleshooting ● Extend as needed via a robust plugin architecture ● Automatically build images (on each platform) per system definition to optimize deployment
- 13. Image: https://github.com/docker/swarm/blob/master/logo.png Docker Tools ● Docker Machine ○ Create Docker hosts on local hypervisors and cloud providers ● Swarm ○ A Docker-native clustering system ● Compose: ○ Assemble Docker multi-container distributed apps
- 14. Kontakt Johannes Maximilian Scheuermann johannes.scheuermann@inovex.de inovex GmbH Office Karlsruhe Ludwig-Erhard-Allee 6 76131 Karlsruhe Thank you for listening!