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所在平台: Udemy |
课程主页: https://www.udemy.com/course/docker-containers-data-science-reproducible-research/
课程评论：没有评论
课程名称：数据科学与可重复研究的Docker容器 课程概述：本课程旨在快速启动Docker容器在数据科学和可重复研究中的应用，通过多个实际示例的复现来实现。课程将指导学员在任何安装了Docker引擎的机器（Mac、Windows、Linux）上设置Docker环境，并逐步创建自定义和分布式开发环境（RStudio）在容器中。学员将不再需要手动更新开发环境！您可以像往常一样工作，在容器中添加或开发研究文档，进行测试并以镜像形式分发！结果将能够独立于R版本进行复现，也能在容器中运行R程序。我们将展示在不同机器（Mac、Windows、Linux）上测试容器的能力。 课程内容亮点： - 复现并在不同基础设施上共享工作 - 在几年后仍能重复相同的工作 - 在隔离环境中使用R-Studio - 个性化Docker工作的提示，包括使用自动构建 课程将涵盖的内容： - 准备计算机以使用Docker - 开发Docker镜像的工作流程 - 构建用于交互模式的R-Studio的Docker镜像 - 使用Docker运行R程序的镜像 - 使用Docker网络在容器之间进行通信 - 在Docker容器中构建ShinyServer - 开发Shiny应用作为R包并使用golem框架在Docker容器中部署的示例 未来可能会添加更多相关材料（例如，持续集成和部署、docker-compose）。 为什么选择本课程？ 本课程提供了功能的快速概述，并提供了宝贵的方法和模板供学员构建基础。课程旨在使学习旅程尽可能简单——只需观看这些视频并重用提供的代码即可！ 开始使用Docker容器和您的数据科学工具，通过本课程进行复现吧！
Get excited!This course is designed to jump-start using Docker Containers for Data Science and Reproducible Research by reproducing several practical examples. Course will help to setup Docker Environment on any machine equipped with Docker Engine (Mac, Windows, Linux). Course will proceed with all steps to create custom and distributed development environment [RStudio] in a container. Forget about manual update of your Development Environment! Work as usual, add or develop the research document into your Container, test it and distribute in an image! Result will be reproducible independently on the R version, perhaps after several years...Same about running R programs in the container. We will demonstrate this capability including testing the container on completely different machines (Mac, Windows, Linux)Summary of ideas we will cover in this course:Reproduce and share work on a different infrastructureBe able to repeat the work after several yearsUse R-Studio in an isolated environmentTips to personalize work with Docker including usage of Automated BuildsWhat is covered by this course?This course will provide several use cases on using Docker Containers for Data Science:Preparing your computer for using DockerWorking pipeline to develop docker imageBuilding Docker image to work with R-Studio in Interactive modeBuilding Docker images to run R programsUsing Docker network to communicate between containersBuilding ShinyServer in Docker containerWalk-though example of developing Shiny App as an R Package and deploying in Docker Container using golem frameworkMore relevant materials may be added to this course in the future (e.g. continous integration and deployment, docker-compose)Why to take this course and not other?Added value of this course is to provide a quick overview of functionality and to provide valuable methods and templates to build on. Focus of this course is to make a learning journey as easy as possible - simply watch these videos and reuse provided code!Just Start using Docker Containers with your Data Science tools by reproducing this course!
