SDN x Cloud Native Meetup #38
介紹 VSCode Remote Development 工具,示範如何透過 VSCode Development Container 來打造跨語言的容器式開發環境,包括 Java、Python、Node.js、Go 等程式語言都能夠使用此方式來開發系統,並且一個專案一個容器,不會污染本機環境,可以安心地執行程式開發工作。
SDN x Cloud Native Meetup #38
介紹 VSCode Remote Development 工具,示範如何透過 VSCode Development Container 來打造跨語言的容器式開發環境,包括 Java、Python、Node.js、Go 等程式語言都能夠使用此方式來開發系統,並且一個專案一個容器,不會污染本機環境,可以安心地執行程式開發工作。
This document summarizes a presentation on Docker given at Yishou University. It introduces Docker concepts like virtualization, the differences between containers and VMs, the Docker ecosystem and tools. It covers the Linux and Docker CLIs, using the Docker Engine, building minimal Docker images with Dockerfile, and using Docker and Qemu to emulate a Raspberry Pi Raspbian image. Upcoming topics for next week are also listed.
This document discusses Docker and containers. It provides a brief history of containers from chroot in Unix V7 in 1979 to Docker in 2013. It compares containers to virtual machines and describes the container principle of one container, one process. It also discusses Docker tools, the relationship between containers and DevOps practices like microservices and infrastructure as code, and Docker management for images and containers.
This document summarizes Philipz's presentation on Docker. It includes sections on Docker basics, fight clubs, the matrix, Docker vs virtual machines, Docker layers, continuous deployment workflows, use scenarios, demos, monolithic vs microservices architectures, and the evolution of Docker. The presentation covers concepts such as operating system-level virtualization, container engines, Docker commands, deployment tools, and example use cases for Docker containers.
This document discusses Docker and related technologies. It begins with introductions to Docker and concepts like Dockerfiles, images, and the Docker Hub. It then discusses tips for using Docker and demos linking containers and building images. Later it covers technologies that work with Docker like etcd for service discovery and fleet for cluster management. It emphasizes how Docker supports microservices and DevOps practices.
The document discusses deep learning and convolutional neural networks. It provides a brief history of convolutional networks, starting with early models from the 1960s and work by LeCun in the 1980s and 1990s applying convolutional networks to tasks like handwritten digit recognition. The document also discusses how convolutional networks learn hierarchical representations and have been applied to tasks like face detection, semantic segmentation, and scene parsing. It notes that while deep learning has been successful, it is still missing capabilities for reasoning, structured prediction, memory and truly unsupervised learning.
Fusion Tables allows users to visualize and analyze data through interactive maps and charts. It functions as a web-based data management tool that allows for collaboration. Some applications of Fusion Tables include displaying maps of tsunami damage in Japan, census data maps, and submarine cable maps. It works by storing data in Google's BigTable infrastructure and allowing users to perform analyses and create multi-view, multi-user visualizations through an API and web interface.
This document discusses using MQTT in Docker containers on a Raspberry Pi. It begins with an introduction to using Docker on Raspberry Pi for IoT prototyping. It then discusses using GPIO pins in Docker containers to control LED blinking. It provides an overview of Docker compared to virtual machines and describes Docker layers and common Docker commands. Finally, it demonstrates using MQTT in a Docker container for IoT communication and answers some common Docker questions.
This document discusses Docker and CoreOS running on Raspberry Pi devices. It begins by providing background on Docker and virtual machines. It then discusses using Docker with Raspberry Pis, noting that Docker runs well on ARM-based devices like Raspberry Pi. It also discusses benefits of using the lightweight OS CoreOS with Docker, though CoreOS currently does not support ARM architectures. The document then covers potential issues with base images and importance of validation and verification for Docker images. It concludes by mentioning a live demo of Docker on Raspberry Pi and potential extensions like private repositories, web UIs, and Docker clusters.
This document provides an agenda for a one-day Docker introduction workshop. It includes an introduction to Docker tools and concepts like containers vs VMs, the Docker ecosystem and tools, Linux and Docker command line usage, Docker Engine, Docker Hub, Docker images, networking and volumes. It also covers deploying Docker images to Azure PaaS, Docker Compose, building ARM images on x86 machines, and a TensorFlow demo. The workshop aims to provide attendees with foundational Docker knowledge and hands-on experience through examples and exercises.
This document provides an overview of Docker concepts and tools for beginners. It covers:
1. The differences between virtual machines and containers, and the container lifecycle.
2. Tools in the Docker ecosystem such as Docker Engine, Docker CLI, Docker Hub, Docker Compose, and networking/volume commands.
3. Examples of using Docker Engine, Docker Hub for images, networking, volumes and deploying images to Azure PaaS.
4. How to use Docker Compose to define and run multi-container applications.
This document introduces Apache Ignite, an open source in-memory computing platform that provides in-memory caching, data grid, and database capabilities. It is supported by GridGain Systems and graduated from Apache in 2015. Key features include ACID compliance, SQL support, and the ability to perform both online transaction processing and online analytical processing. The document discusses Apache Ignite architecture, partitioning vs replication, consistency models, and use cases for in-memory caching, data grids, and databases. It also compares Ignite to other in-memory solutions and outlines approaches for data persistence and high availability.
This document discusses the steps to building a cloud native practice. It begins with introducing the speaker and what cloud native means. The 12 steps then cover: 1) version control, 2) continuous integration pipelines, 3) stateless applications, 4) containerization, 5) common services, 6) Kubernetes, 7) observability, 8) monitoring, 9) domain-driven design, 10) microservices and serverless architectures, 11) cloud strategies, and 12) reconstructing architectures with a focus on responsibilities of architects and challenges of open source.
This document outlines a course on container technology for data science applications taught by Philipz. The course covers using containers with R, Python, Jupyter notebooks, TensorFlow with GPUs, Docker Compose, and more. It emphasizes how containers can integrate data, methods, and computing platforms to easily reproduce research. Example code is provided for using RStudio, Jupyter, and composing multiple services with Docker Compose. The instructor concludes by discussing best practices for using containers at different stages of work and how they can save more valuable time than money.
This document outlines a course on container technology and IoT endpoint applications taught by Philipz. The 7 part course covers topics like Docker on Raspberry Pi, building ARM images on x86 machines, MQTT and Docker prototypes, using Docker Compose for IoT MQTT projects, and sending MQTT data to Adafruit IO. It provides examples of building and running containerized IoT applications on Raspberry Pi devices.
1. The document summarizes the topics covered in an advanced Docker workshop, including Docker Machine, Docker Swarm, networking, services, GitLab integration, IoT applications, Moby/LinuxKit, and a call to action to learn more about Docker on their own.
2. Specific topics included how to create Docker Machines on Azure, build a Swarm cluster, configure networking and services, integrate with GitLab for continuous integration/delivery, develop IoT applications using Docker on Raspberry Pi, and introduce Moby and LinuxKit for building customized container-based operating systems.
3. The workshop concluded by emphasizing business models, microservices, infrastructure as code, container design, DevOps, and
This document summarizes the key topics covered in Day 2 of a Docker and container technology introduction and hands-on course, including:
1) An overview of Docker Hub and how it relates to GitHub for automatically building images
2) Basic Git commands
3) Configuring automatic builds on Docker Hub by linking a GitHub repository
4) Docker network and volume commands, and exercises using these commands
5) Using Docker Compose to run multiple connected containers defined in a compose file
6) A demonstration of running TensorFlow using Docker