Dev containers in Machine Learning | SoftwareMill
Briefly

Dev containers in Machine Learning | SoftwareMill
"Docker has become extremely popular nowadays. It is lightweight, portable, self-contained, and thus great for microservices architecture. Docker containers make it easier to build software and isolate the environment, and they can be run in many different environments with different operating systems and hardware platforms. We could discuss more how great containers are for deployment purposes, but let's take a step back and see how containers can be used for development purposes."
"VSCode extension Dev Containers from Microsoft come in really handy here. The Dev Containers extension lets you use a Docker container as a full-featured development environment. Among other benefits, it enables you to: Develop using a consistent, reproducible toolset on the same OS as your deployment environment. Easily switch between distinct development environments and update them safely without affecting your local system. Provide new team members or contributors with a simple, consistent development environment, making it easier for them to get started."
Docker is lightweight, portable, and self-contained, making it well suited for microservices architectures. Containers simplify building software, isolating environments, and running applications across different operating systems and hardware platforms. Running a container interactively with docker run -it <IMAGE> or attaching with docker exec -it [container-id] bash gives a shell, but terminal-only development lacks modern IDE features. The VSCode Dev Containers extension lets a Docker container act as a full-featured development environment. Dev Containers deliver consistent, reproducible toolchains, easy environment switching, safe updates, simplified onboarding, and isolated experimentation without affecting the local system.
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