DCSF 19 Online Feature Extraction and Event Generation for Computer-Animal Interaction

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This talk will present an architecture developed to investigate the interaction with and between animals. The architecture allows online processing of multimedia streams and the generation, storing and visualizing of events using feature extraction. It allows biologists to analyze the events by monitoring live or by replaying streams through a web interface Docker swarm is the central component of the architecture and serves as infrastructure for stream processing, event generation, event processing and visualization. The main entry point for users is a web interface that spins up one container per user and allows independent replay of streams. This talk will focus on the architecture and on technical details concerning its implementation as well as how docker is utilized to process, store and visualize events. Some time will be spent explaining details about custom made docker solutions.

Online feature extraction
and event generation for
computer-animal
interaction
Leon Bonde Larsen & Mathias Neerup
Advanced Computer Systems Group

Introduction

Introduction

ATS v2.0

Problem: Stateless HTTP protocol

State using cookies

State using cookies
Memory

Server process
Process

Server side playing
somePlayer

Server side playing > Multiple users

Server side playing > Multiple users > Containers

Streams

Streams > Producers

Streams > Consumers

Streams > Historian

Streams > Processors
Event Database

Streams > Implementation

ATS v1.0

ATS v1.0> Limitations
▪ Janus WebRTC
▪ One user at a time

ATS v2.0
▪ Docker swarm
▪ React
▪ Redux
▪ SocketIO
▪ Semantic UI

ATS v2.0 > Janus
Janus
- Janus
- Input: Stream

ATS v2.0 > Backend
Backend
- Player
- Avahi Info.

ATS v2.0 > Plugin
Plugin
- A react
component
- Inputs
- Outputs
- Image

ATS v2.0 > Services

ATS v2.0 > Services

ATS v2.0 > Services

ATS v2.0 > Services

Container (service) pooling
▪ Pooler
▪ Service
▪ Acquired
▪ Released
▪ Policies
▪ Memory

Container (service) pooling
▪ Label on service
▪ SessionID
▪ Release policy
▪ 24 hours
▪ Closing session

Multicast traﬃc
▪ Multicast streams
▪ Enable IPv6
▪ Connect to bridge
https://github.com/mcproxy/mcproxy

Multicast traﬃc
▪ Multicast streams
▪ Avahi
▪ Avahi-daemon
▪ reflector
https://github.com/mcproxy/mcproxy

CD/CI
▪ Gitlab docker registry

CD/CI - imageUpdater
▪ Gitlab docker registry

CD/CI - deploy strategy
▪ Gitlab docker registry

Software stack

Hardware

Docker swarm is suitable in biological research
Summary

Demo

Questions ?
Leon Bonde Larsen Mads Tilgaard Jensen Mathias Neerup
Ph.d Student programmer Research Assistant
https://acsg.sdu.dk

Demo - Video

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Kevin Jones, NGNIX - NGINX is one of the most popular images on Docker Hub and has been at the forefront of the web since the early 2000's. In this talk we will discuss how and why NGINX's lightweight and powerful architecture makes it a very popular choice for securing containerized applications as a sidecar reverse proxy within containers. We will highlight important aspects of application security that NGINX can help with, such as TLS, HTTP, AuthN, AuthZ and traffic control.

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Kathleen Juell, Digital Ocean - Containers are an essential part of today's microservice ecosystem, as they allow developers and operators to maintain standards of reliability and reproducibility in fast-paced deployment scenarios. And while there are best practices that extend across stacks in containerized environments, there are also things that make each stack distinct, starting with the application image itself. This talk will dive into some of these particularities, both at the image and service level, while also covering general best practices for building and running Node applications with database backends using Docker and Compose.

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Jeff Hajewski, Salesforce - There is a wealth of information on building deep learning models with PyTorch or TensorFlow. Anyone interested in building a deep learning model is only a quick search away from a number of clear and well written tutorials that will take them from zero knowledge to having a working image classifier. But what happens when you need to deploy these models in a production setting? At Salesforce, we use TensorFlow models to help us provide customers with insights into their data, and we do this as close to real-time as possible. Designing these systems in a scalable manner requires overcoming a number of design challenges, but the core component is Docker. Docker enables us to design highly scalable systems by allowing us to focus on service interactions, rather than how our services will interact with the hardware. Docker is also at the core of our test infrastructure, allowing developers and data scientists to build and test the system in an end to end manner on their local machines. While some of this may sound complex, the core message is simplicity - Docker allows us to focus on the aspects of the system that matter, greatly simplifying our lives.

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Distributed Deep Learning with Docker at Salesforce

James Fuller, webcomposite s.r.o. - Curl is the venerable (yet very modern) 'swiss army knife' command line tool and library for transferring data with URLs. Recently we (the Curl team) decided to build a release for Docker Hub. This talk will outline our current development workflow with respect to the docker image and provide insights on what it takes to build a docker image for mass public consumption. We are also keen to learn from users and other developers how we might improve and enhance the official curl docker image.

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The First 10M Pulls: Building The Official Curl Image for Docker Hub

Fabian Stäber, Instana - In recent years, we saw a great paradigm shift in software engineering away from static monolithic applications towards dynamic distributed horizontally scalable architectures. Docker is one of the key technologies enabling this development. This shift poses a lot of new challenges for application monitoring, ranging from practical issues (need for automation) to technical challenges (Docker networking) to organizational topics (blurring line between software engineers and operations) to fundamental questions (define what is an application). In this talk we show how Docker changed the way we do monitoring, how modern application monitoring systems work, and what future developments we expect.

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Clemente Biondo, Engineering Ingegneria Informatica - When the COVID 19 pandemic started, Engineering Ingegneria Informatica Group (1.25 billion euros of revenues, 65 offices around the world, 12.000 employees) was forced to put their digital transformation to the test in order to maintain operational continuity. In this session, Clemente Biondo, the Tech Lead of the Information Systems Department, will share how his company is reacting to this unforeseeable scenario and how Docker-driven digital transformation had paved the path for work to continue remotely. Clemente will discuss learnings moving from colocated teams, manual approaches, email based-business processes, and a monolithic application to a mature DevOps culture characterized by a distributed autonomous workforce and a continuous deployment process that deploys backward-compatible Docker containerized microservices into hybrid multi cloud datacenters an average of twice a day with zero-downtime. He will detail how they use Docker to unify dev, test and production environments, and as an efficient and automated mechanism for deploying applications. Lastly, Clemente shares how, in our darkest hour, he and others are working to shine their brightest light.

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Chris Lauer, NOAA Space Weather Prediction Center - This is the story of how adopting a containerized workflow changed the way our small software team works at NOAA’s Space Weather Prediction Center. Our old architecture, a big ball of mud shared-database integration, just wasn’t cutting it - it was killing our agility. Over the past two years, our small team has adopted a microservice style architecture, using Docker with docker-compose and environment files as our deployment strategy for all new development. We’ve discovered the joys of using containers for identical dev, staging, and production environments. We work closely with scientists: much of the code we’re running has complicated and conflicting library dependencies. Docker captures these beautifully - we’ve even had some success teaching our scientists to use it! I’ll share what we’ve learned, some of the persistent challenges we face, and one place we really got it wrong. This talk builds off of a popular hallway track from DockerCon 2019.

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Brian Christner, 56k + Docker Captain - In this session, we will unlock the full potential of using Microsoft Visual Studio Code (VS Code) and Docker Desktop to turn you into a Docker Power User. When we expand and utilize the VS Code Docker plugin, we can take our projects and Docker skills to the next level. In addition to using VS Code, we streamline our Docker Desktop development workflow with less context switching and built-in shortcuts. You will learn how to bootstrap new projects, quickly write Dockerfiles utilizing templates, build, run, and interact with containers all from VS Code.

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Ashish Sharma, SS&C Eze - SS&C Eze provides various products in the stock market domain. We spent the last couple of years building Eclipse which is an investment suite born in cloud. The journey so far has been very interesting. The very first version of the product were a bunch of monolithic windows services and deployed using Octopus tool. We successfully managed to bring all the monolithic problem to the cloud and created a nightmare for ourselves. We then started applying microservices architecture principles and started breaking the monolithic into small services. Very soon we realized that we need a better packaging/deployment tool. Docker looked like a magical solution to our problem. Since its adoption, It has not only solved the deployment problem for us but has made a deep impact on different aspects of SDLC. It allowed us to use heterogeneous technology stacks, simplified development environment setup, simplified our testing strategy, improved our speed of delivery, and made our developers more productive. In this talk I would like to share our experience of using Docker and its positive impact on our SDLC.

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Ara Pulido, Datadog - Container technologies, although not new, have increased their popularity in the past few years, with container orchestrators allowing companies around the world to adopt these technologies to help them ship and scale microservices with precision and velocity. Kubernetes is currently the most popular container orchestration platform, and while many organizations are migrating their workloads to it, Kubernetes is still relatively immature. New corner cases, errors, and quirks are regularly discovered as users push the boundaries of size and scale. When Datadog adopted Kubernetes we discovered some of these boundaries the hard way, and we continuously challenge and modify our infrastructure decisions in order to fit our use case. Join me in this talk for our story on what we learned while we scaled our Kubernetes clusters, the contributions to Kubernetes we made along the way, and how you can apply those learnings when growing your Kubernetes clusters from a handful to hundreds or thousands of nodes.

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Andy Clemenko, StackRox - One underutilized, and amazing, thing about the docker image scheme is labels. Labels are a built in way to document all aspects about the image itself. Think about all the information that the tags inside your clothing carry. If you care to look you can find out everything about the garment. All that information can be very valuable. Now think about how we can leverage labels to carry similar information. We can even use the labels to contain Docker Compose or even Kubernetes Yaml. We can even include labels into the CI/CD process making things more secure and smoother. Come find out some fun techniques on how to leverage labels to do some fun and amazing things.

Labels, Labels, Labels

Labels, Labels, Labels

Labels, Labels, Labels

Patrick Deloulay, Micro Focus - Micro Focus started their digital transformation 3 years ago, moving the entire portfolio into hundreds of container images. Leveraging Docker Hub as our primary registry service, we will cover how we ended up building a simple but secure push/pull model to publish and deliver our premium assets to our customers and partners to both meet the high agility of our DevOps teams while greatly simplifying the deployment of our applications.

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Using Docker Hub at Scale to Support Micro Focus' Delivery and Deployment Model

Using Docker Hub at Scale to Support Micro Focus' Delivery and Deployment Model

Lukonde Mwila, Entelect As the cloud-native approach to development and deployment becomes more prevalent, it's an exciting time for software engineers to be equipped on how to dockerize multi-container applications and deploy them to the cloud. In this talk, Lukonde Mwila, Software Engineer at Entelect, will cover the following topics: - Docker Compose - Containerizing an Nginx Server - Containerizing an React App - Containerizing an Node.JS App - Containerizing anMongoDB App - Runing Multi-Container App Locally - Creating a CI/CD Pipeline - Adding a build stage to test containers and push images to Docker Hub - Deploying Multi-Container App to AWS Elastic Beanstalk Lukonde will start by giving an overview of how Docker Compose works and how it makes it very easy and straightforward to startup multiple Docker containers at the same time and automatically connect them together with some form of networking. After that, Lukonde will take a hands on approach to containerize an Nginx server, a React app, a NodeJS app and a MongoDB instance to demonstrate the power of Docker Compose. He'll demonstrate usage of two Docker files for an application, one production grade and the other for local development and running of tests. Lastly, he'll demonstrate creating a CI/CD pipeline in AWS to build and test our Docker images before pushing them to Docker Hub or AWS ECR, and finally deploying our multi-container application AWS Elastic Beanstalk.

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Elton Stoneman, Docker Captain + Container Consultant and Trainer How do you provide a SaaS offering when your product is a 10-year old Fortran app, currently built to run on Windows 10? With Docker and Kubernetes of course - and you can do it in a week (... to prototype level at least). In this session I'll walk through the processes and practicalities of taking an older Windows app, making it run in containers with Kubernetes, and then building a simple API wrapper to host the whole stack as a cloud-based SaaS product. There's a lot of technology here from a real world case study, and I'll focus on: - running Windows apps in Docker containers - building a .NET Core API which can run in Linux or Windows containers - running the stack in Kubernetes with Docker Desktop locally and AKS in the cloud - configuring AKS workloads in Azure to burst out to Azure Container Instances And there's a core theme to this session: Docker and Kubernetes are complex technologies, but they're the key to modern development. If you invest time learning them, they make projects like this simple, portable, fast and fun.

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Corporate and higher education. Two industries that, in the past, have had a clear divide with very little crossover. The difference in goals, learning styles and objectives paved the way for differing learning technologies platforms to evolve. Now, those stark lines are blurring as both sides are discovering they have content that’s relevant to the other. Join Tammy Rutherford as she walks through the pros and cons of corporate and higher ed collaborating. And the challenges of these different technology platforms working together for a brighter future.

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Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

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DCSF 19 Online Feature Extraction and Event Generation for Computer-Animal Interaction

1. Online feature extraction and event generation for computer-animal interaction Leon Bonde Larsen & Mathias Neerup Advanced Computer Systems Group

2. Introduction

3. Introduction

5. Problem: Stateless HTTP protocol

6. State using cookies

7. State using cookies Memory

8. Server process Process

9. Server side playing somePlayer

10. Server side playing > Multiple users

11. Server side playing > Multiple users > Containers

13. Streams > Producers

14. Streams > Consumers

15. Streams > Historian

16. Streams > Processors Event Database

17. Streams > Implementation

19. ATS v1.0> Limitations ▪ Janus WebRTC ▪ One user at a time

20. ATS v2.0 ▪ Docker swarm ▪ React ▪ Redux ▪ SocketIO ▪ Semantic UI

21. ATS v2.0 > Janus Janus - Janus - Input: Stream

22. ATS v2.0 > Backend Backend - Player - Avahi Info.

23. ATS v2.0 > Plugin Plugin - A react component - Inputs - Outputs - Image

24. ATS v2.0 > Services

25. ATS v2.0 > Services

26. ATS v2.0 > Services

27. ATS v2.0 > Services

28. Container (service) pooling ▪ Pooler ▪ Service ▪ Acquired ▪ Released ▪ Policies ▪ Memory

29. Container (service) pooling ▪ Label on service ▪ SessionID ▪ Release policy ▪ 24 hours ▪ Closing session

30. Multicast traﬃc ▪ Multicast streams ▪ Enable IPv6 ▪ Connect to bridge https://github.com/mcproxy/mcproxy

31. Multicast traﬃc ▪ Multicast streams ▪ Avahi ▪ Avahi-daemon ▪ reflector https://github.com/mcproxy/mcproxy

32. CD/CI ▪ Gitlab docker registry

33. CD/CI - imageUpdater ▪ Gitlab docker registry

34. CD/CI - deploy strategy ▪ Gitlab docker registry

35. Software stack

37. Docker swarm is suitable in biological research Summary

39. Questions ? Leon Bonde Larsen Mads Tilgaard Jensen Mathias Neerup Ph.d Student programmer Research Assistant https://acsg.sdu.dk

40. Demo - Video